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| BIOM(1) | biom-format | BIOM(1) |
NAME¶
biom - BIOM Documentation [image] The BIOM file format (canonically pronounced biome) is designed to be a general-use format for representing biological sample by observation contingency tables. BIOM is a recognized standard for the Earth Microbiome Project and is a Genomics Standards Consortium supported project. The BIOM format is designed for general use in broad areas of comparative -omics. For example, in marker-gene surveys, the primary use of this format is to represent OTU tables: the observations in this case are OTUs and the matrix contains counts corresponding to the number of times each OTU is observed in each sample. With respect to metagenome data, this format would be used to represent metagenome tables: the observations in this case might correspond to SEED subsystems, and the matrix would contain counts corresponding to the number of times each subsystem is observed in each metagenome. Similarly, with respect to genome data, this format may be used to represent a set of genomes: the observations in this case again might correspond to SEED subsystems, and the counts would correspond to the number of times each subsystem is observed in each genome. There are two components to the BIOM project: first is the definition of the BIOM format, and second is development of support objects in multiple programming languages to support the use of BIOM in diverse bioinformatics applications. The version of the BIOM file format is independent of the version of the biom-format software. There are official implementations of BIOM format support objects (APIs) in the Python and R programming languages. The rest of this site contains details about the BIOM file format (which is independent of the API) and the Python biom-format API. For more details about the R API, please see the CRAN biom package.- •
- QIIME
- •
- MG-RAST
- •
- PICRUSt
- •
- Mothur
- •
- phyloseq
- •
- MEGAN
- •
- VAMPS
- •
- metagenomeSeq
- •
- Phinch
If you are using BIOM in your project, and would like your project to be listed, please submit a pull request to the BIOM project. More information on submitting pull requests can be found here.
BIOM DOCUMENTATION¶
These pages provide format specifications and API information for the BIOM table objects.The biom file format¶
The BIOM project consists of two independent tools: the biom-format software package, which contains software tools for working with BIOM-formatted files and the tables they represent; and the BIOM file format. As of the 1.0.0 software version and the 1.0 file format version, the version of the software and the file format are independent of one another. Version specific documentation of the file formats can be found on the following pages.The biom file format: Version 1.0¶
The biom format is based on JSON to provide the overall structure for the format. JSON is a widely supported format with native parsers available within many programming languages. Required top-level fields:id : <string or null> a field that can be used to id a table (or null)
format : <string> The name and version of the current biom format
format_url : <url> A string with a static URL providing format details
type : <string> Table type (a controlled vocabulary)
Acceptable values:
"OTU table"
"Pathway table"
"Function table"
"Ortholog table"
"Gene table"
"Metabolite table"
"Taxon table"
generated_by : <string> Package and revision that built the table
date : <datetime> Date the table was built (ISO 8601 format)
rows : <list of objects> An ORDERED list of obj describing the rows
(explained in detail below)
columns : <list of objects> An ORDERED list of obj describing the columns
(explained in detail below)
matrix_type : <string> Type of matrix data representation (a controlled vocabulary)
Acceptable values:
"sparse" : only non-zero values are specified
"dense" : every element must be specified
matrix_element_type : Value type in matrix (a controlled vocabulary)
Acceptable values:
"int" : integer
"float" : floating point
"unicode" : unicode string
shape : <list of ints>, the number of rows and number of columns in data
data : <list of lists>, counts of observations by sample
if matrix_type is "sparse", [[row, column, value],
[row, column, value],
...]
if matrix_type is "dense", [[value, value, value, ...],
[value, value, value, ...],
...]
Optional top-level fields:
comment : <string> A free text field containing any information that you
feel is relevant (or just feel like sharing)
The rows value is an ORDERED list of objects where each object corresponds to a single row in the matrix. Each object can currently store arbitrary keys, although this might become restricted based on table type. Each object must provide, at the minimum:
id : <string> an arbitrary UNIQUE identifier metadata : <an object or null> A object containing key, value metadata pairs
The columns value is an ORDERED list of objects where each object corresponds to a single column in the matrix. Each object can currently store arbitrary keys, although this might become restricted based on table type. Each object must provide, at the minimum:
id : <string> an arbitrary UNIQUE identifier metadata : <an object or null> A object containing key, value metadata pairs
Example biom files¶
Below are examples of minimal and rich biom files in both sparse and dense formats. To decide which of these you should generate for new data types, see the section on sparse-or-dense.Minimal sparse OTU table¶
{
"id":null,
"format": "Biological Observation Matrix 0.9.1-dev",
"format_url": "http://biom-format.org/documentation/format_versions/biom-1.0.html",
"type": "OTU table",
"generated_by": "QIIME revision 1.4.0-dev",
"date": "2011-12-19T19:00:00",
"rows":[
{"id":"GG_OTU_1", "metadata":null},
{"id":"GG_OTU_2", "metadata":null},
{"id":"GG_OTU_3", "metadata":null},
{"id":"GG_OTU_4", "metadata":null},
{"id":"GG_OTU_5", "metadata":null}
],
"columns": [
{"id":"Sample1", "metadata":null},
{"id":"Sample2", "metadata":null},
{"id":"Sample3", "metadata":null},
{"id":"Sample4", "metadata":null},
{"id":"Sample5", "metadata":null},
{"id":"Sample6", "metadata":null}
],
"matrix_type": "sparse",
"matrix_element_type": "int",
"shape": [5, 6],
"data":[[0,2,1],
[1,0,5],
[1,1,1],
[1,3,2],
[1,4,3],
[1,5,1],
[2,2,1],
[2,3,4],
[2,4,2],
[3,0,2],
[3,1,1],
[3,2,1],
[3,5,1],
[4,1,1],
[4,2,1]
]
}
Minimal dense OTU table¶
{
"id":null,
"format": "Biological Observation Matrix 0.9.1-dev",
"format_url": "http://biom-format.org/documentation/format_versions/biom-1.0.html",
"type": "OTU table",
"generated_by": "QIIME revision 1.4.0-dev",
"date": "2011-12-19T19:00:00",
"rows":[
{"id":"GG_OTU_1", "metadata":null},
{"id":"GG_OTU_2", "metadata":null},
{"id":"GG_OTU_3", "metadata":null},
{"id":"GG_OTU_4", "metadata":null},
{"id":"GG_OTU_5", "metadata":null}
],
"columns": [
{"id":"Sample1", "metadata":null},
{"id":"Sample2", "metadata":null},
{"id":"Sample3", "metadata":null},
{"id":"Sample4", "metadata":null},
{"id":"Sample5", "metadata":null},
{"id":"Sample6", "metadata":null}
],
"matrix_type": "dense",
"matrix_element_type": "int",
"shape": [5,6],
"data": [[0,0,1,0,0,0],
[5,1,0,2,3,1],
[0,0,1,4,2,0],
[2,1,1,0,0,1],
[0,1,1,0,0,0]]
}
Rich sparse OTU table¶
{
"id":null,
"format": "Biological Observation Matrix 0.9.1-dev",
"format_url": "http://biom-format.org/documentation/format_versions/biom-1.0.html",
"type": "OTU table",
"generated_by": "QIIME revision 1.4.0-dev",
"date": "2011-12-19T19:00:00",
"rows":[
{"id":"GG_OTU_1", "metadata":{"taxonomy":["k__Bacteria", "p__Proteobacteria", "c__Gammaproteobacteria", "o__Enterobacteriales", "f__Enterobacteriaceae", "g__Escherichia", "s__"]}},
{"id":"GG_OTU_2", "metadata":{"taxonomy":["k__Bacteria", "p__Cyanobacteria", "c__Nostocophycideae", "o__Nostocales", "f__Nostocaceae", "g__Dolichospermum", "s__"]}},
{"id":"GG_OTU_3", "metadata":{"taxonomy":["k__Archaea", "p__Euryarchaeota", "c__Methanomicrobia", "o__Methanosarcinales", "f__Methanosarcinaceae", "g__Methanosarcina", "s__"]}},
{"id":"GG_OTU_4", "metadata":{"taxonomy":["k__Bacteria", "p__Firmicutes", "c__Clostridia", "o__Halanaerobiales", "f__Halanaerobiaceae", "g__Halanaerobium", "s__Halanaerobiumsaccharolyticum"]}},
{"id":"GG_OTU_5", "metadata":{"taxonomy":["k__Bacteria", "p__Proteobacteria", "c__Gammaproteobacteria", "o__Enterobacteriales", "f__Enterobacteriaceae", "g__Escherichia", "s__"]}}
],
"columns":[
{"id":"Sample1", "metadata":{
"BarcodeSequence":"CGCTTATCGAGA",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"gut",
"Description":"human gut"}},
{"id":"Sample2", "metadata":{
"BarcodeSequence":"CATACCAGTAGC",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"gut",
"Description":"human gut"}},
{"id":"Sample3", "metadata":{
"BarcodeSequence":"CTCTCTACCTGT",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"gut",
"Description":"human gut"}},
{"id":"Sample4", "metadata":{
"BarcodeSequence":"CTCTCGGCCTGT",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"skin",
"Description":"human skin"}},
{"id":"Sample5", "metadata":{
"BarcodeSequence":"CTCTCTACCAAT",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"skin",
"Description":"human skin"}},
{"id":"Sample6", "metadata":{
"BarcodeSequence":"CTAACTACCAAT",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"skin",
"Description":"human skin"}}
],
"matrix_type": "sparse",
"matrix_element_type": "int",
"shape": [5, 6],
"data":[[0,2,1],
[1,0,5],
[1,1,1],
[1,3,2],
[1,4,3],
[1,5,1],
[2,2,1],
[2,3,4],
[2,5,2],
[3,0,2],
[3,1,1],
[3,2,1],
[3,5,1],
[4,1,1],
[4,2,1]
]
}
Rich dense OTU table¶
{
"id":null,
"format": "Biological Observation Matrix 0.9.1-dev",
"format_url": "http://biom-format.org/documentation/format_versions/biom-1.0.html",
"type": "OTU table",
"generated_by": "QIIME revision 1.4.0-dev",
"date": "2011-12-19T19:00:00",
"rows":[
{"id":"GG_OTU_1", "metadata":{"taxonomy":["k__Bacteria", "p__Proteobacteria", "c__Gammaproteobacteria", "o__Enterobacteriales", "f__Enterobacteriaceae", "g__Escherichia", "s__"]}},
{"id":"GG_OTU_2", "metadata":{"taxonomy":["k__Bacteria", "p__Cyanobacteria", "c__Nostocophycideae", "o__Nostocales", "f__Nostocaceae", "g__Dolichospermum", "s__"]}},
{"id":"GG_OTU_3", "metadata":{"taxonomy":["k__Archaea", "p__Euryarchaeota", "c__Methanomicrobia", "o__Methanosarcinales", "f__Methanosarcinaceae", "g__Methanosarcina", "s__"]}},
{"id":"GG_OTU_4", "metadata":{"taxonomy":["k__Bacteria", "p__Firmicutes", "c__Clostridia", "o__Halanaerobiales", "f__Halanaerobiaceae", "g__Halanaerobium", "s__Halanaerobiumsaccharolyticum"]}},
{"id":"GG_OTU_5", "metadata":{"taxonomy":["k__Bacteria", "p__Proteobacteria", "c__Gammaproteobacteria", "o__Enterobacteriales", "f__Enterobacteriaceae", "g__Escherichia", "s__"]}}
],
"columns":[
{"id":"Sample1", "metadata":{
"BarcodeSequence":"CGCTTATCGAGA",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"gut",
"Description":"human gut"}},
{"id":"Sample2", "metadata":{
"BarcodeSequence":"CATACCAGTAGC",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"gut",
"Description":"human gut"}},
{"id":"Sample3", "metadata":{
"BarcodeSequence":"CTCTCTACCTGT",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"gut",
"Description":"human gut"}},
{"id":"Sample4", "metadata":{
"BarcodeSequence":"CTCTCGGCCTGT",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"skin",
"Description":"human skin"}},
{"id":"Sample5", "metadata":{
"BarcodeSequence":"CTCTCTACCAAT",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"skin",
"Description":"human skin"}},
{"id":"Sample6", "metadata":{
"BarcodeSequence":"CTAACTACCAAT",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"skin",
"Description":"human skin"}}
],
"matrix_type": "dense",
"matrix_element_type": "int",
"shape": [5,6],
"data": [[0,0,1,0,0,0],
[5,1,0,2,3,1],
[0,0,1,4,2,0],
[2,1,1,0,0,1],
[0,1,1,0,0,0]]
}
The biom file format: Version 2.0¶
The biom format is based on HDF5 to provide the overall structure for the format. HDF5 is a widely supported format with native parsers available within many programming languages. Required top-level attributes:id : <string or null> a field that can be used to id a table (or null)
format : <string> The name and version of the current biom format
format-url : <url> A string with a static URL providing format details
type : <string> Table type (a controlled vocabulary)
Acceptable values:
"OTU table"
"Pathway table"
"Function table"
"Ortholog table"
"Gene table"
"Metabolite table"
"Taxon table"
generated-by : <string> Package and revision that built the table
creation-date : <datetime> Date the table was built (ISO 8601 format)
nnz : <int> The number of non-zero elements in the table
shape : <list of ints>, the number of rows and number of columns in data
Required groups:
observation/ : The HDF5 group that contains observation specific information and an observation oriented view of the data observation/matrix : The HDF5 group that contains matrix data oriented for observation-wise operations (e.g., in compressed sparse row format) sample/ : The HDF5 group that contains sample specific information and a sample oriented data oriented view of the data sample/matrix : The HDF5 group that contains matrix data oriented for sample-wise operations (e.g., in compressed sparse column format)
Required datasets:
observation/ids : <string> or <variable length string> A (N,) dataset of the observation IDs, where N is the total number of IDs observation/matrix/data : <float64> A (nnz,) dataset containing the actual matrix data observation/matrix/indices : <int32> A (nnz,) dataset containing the column indices (e.g., maps into samples/ids) observation/matrix/indptr : <int32> A (M+1,) dataset containing the compressed row offsets sample/ids : <string> or <variable length string> A (M,) dataset of the sample IDs, where M is the total number of IDs sample/matrix/data : <float64> A (nnz,) dataset containing the actual matrix data sample/matrix/indices : <int32> A (nnz,) dataset containing the row indices (e.g., maps into observation/ids) sample/matrix/indptr : <int32> A (N+1,) dataset containing the compressed column offsets
Optional datasets:
observation/metadata : <variable length string or null> If specified, a (1,) dataset containing a JSON-string representation of the metadata sample/metadata : <variable length string or null> If specified, a (1,) dataset containing a JSON-string representation of the metadata
The metadata for each axis (observation and sample) are described with JSON. The required structure, if the metadata are specified, is a list of objects, where the list is in index order with respect to the axis (e.g, the object at element 0 corresponds to ID 0 for the given axis). Any metadata that corresponds to the ID, such as taxonomy, can be represented in the object. For instance, the following JSON string describes taxonomy for three IDs: Metadata description:
[
{"taxonomy": ["k__Bacteria", "p__Proteobacteria", "c__Gammaproteobacteria", "o__Enterobacteriales", "f__Enterobacteriaceae", "g__Escherichia", "s__"]}},
{"taxonomy": ["k__Bacteria", "p__Cyanobacteria", "c__Nostocophycideae", "o__Nostocales", "f__Nostocaceae", "g__Dolichospermum", "s__"]}},
{"taxonomy": ["k__Archaea", "p__Euryarchaeota", "c__Methanomicrobia", "o__Methanosarcinales", "f__Methanosarcinaceae", "g__Methanosarcina", "s__"]}}
]
Example biom files¶
Below are examples of minimal and rich biom files in both sparse and dense formats. To decide which of these you should generate for new data types, see the section on sparse-or-dense.BIOM 2.0 OTU table in the HDF5 data description langauge (DDL)¶
HDF5 "rich_sparse_otu_table_hdf5.biom" {
GROUP "/" {
ATTRIBUTE "creation-date" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "2014-05-13T14:50:32.052446"
}
}
ATTRIBUTE "format-url" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "http://biom-format.org"
}
}
ATTRIBUTE "format-version" {
DATATYPE H5T_STD_I64LE
DATASPACE SIMPLE { ( 2 ) / ( 2 ) }
DATA {
(0): 2, 0
}
}
ATTRIBUTE "generated-by" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "example"
}
}
ATTRIBUTE "id" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "No Table ID"
}
}
ATTRIBUTE "nnz" {
DATATYPE H5T_STD_I64LE
DATASPACE SCALAR
DATA {
(0): 15
}
}
ATTRIBUTE "shape" {
DATATYPE H5T_STD_I64LE
DATASPACE SIMPLE { ( 2 ) / ( 2 ) }
DATA {
(0): 5, 6
}
}
ATTRIBUTE "type" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "otu table"
}
}
GROUP "observation" {
DATASET "ids" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 5 ) / ( 5 ) }
DATA {
(0): "GG_OTU_1", "GG_OTU_2", "GG_OTU_3", "GG_OTU_4", "GG_OTU_5"
}
}
GROUP "matrix" {
DATASET "data" {
DATATYPE H5T_IEEE_F64LE
DATASPACE SIMPLE { ( 15 ) / ( 15 ) }
DATA {
(0): 1, 5, 1, 2, 3, 1, 1, 4, 2, 2, 1, 1, 1, 1, 1
}
}
DATASET "indices" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 15 ) / ( 15 ) }
DATA {
(0): 2, 0, 1, 3, 4, 5, 2, 3, 5, 0, 1, 2, 5, 1, 2
}
}
DATASET "indptr" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 6 ) / ( 6 ) }
DATA {
(0): 0, 1, 6, 9, 13, 15
}
}
}
DATASET "metadata" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 1 ) / ( 1 ) }
DATA {
(0): "[{"taxonomy": ["k__Bacteria", "p__Proteobacteria", "c__Gammaproteobacteria", "o__Enterobacteriales", "f__Enterobacteriaceae", "g__Escherichia", "s__"]}, {"taxonomy": ["k__Bacteria", "p__Cyanobacteria", "c__Nostocophycideae", "o__Nostocales", "f__Nostocaceae", "g__Dolichospermum", "s__"]}, {"taxonomy": ["k__Archaea", "p__Euryarchaeota", "c__Methanomicrobia", "o__Methanosarcinales", "f__Methanosarcinaceae", "g__Methanosarcina", "s__"]}, {"taxonomy": ["k__Bacteria", "p__Firmicutes", "c__Clostridia", "o__Halanaerobiales", "f__Halanaerobiaceae", "g__Halanaerobium", "s__Halanaerobiumsaccharolyticum"]}, {"taxonomy": ["k__Bacteria", "p__Proteobacteria", "c__Gammaproteobacteria", "o__Enterobacteriales", "f__Enterobacteriaceae", "g__Escherichia", "s__"]}]"
}
}
}
GROUP "sample" {
DATASET "ids" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 6 ) / ( 6 ) }
DATA {
(0): "Sample1", "Sample2", "Sample3", "Sample4", "Sample5",
(5): "Sample6"
}
}
GROUP "matrix" {
DATASET "data" {
DATATYPE H5T_IEEE_F64LE
DATASPACE SIMPLE { ( 15 ) / ( 15 ) }
DATA {
(0): 5, 2, 1, 1, 1, 1, 1, 1, 1, 2, 4, 3, 1, 2, 1
}
}
DATASET "indices" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 15 ) / ( 15 ) }
DATA {
(0): 1, 3, 1, 3, 4, 0, 2, 3, 4, 1, 2, 1, 1, 2, 3
}
}
DATASET "indptr" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 7 ) / ( 7 ) }
DATA {
(0): 0, 2, 5, 9, 11, 12, 15
}
}
}
DATASET "metadata" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 1 ) / ( 1 ) }
DATA {
(0): "[{"LinkerPrimerSequence": "CATGCTGCCTCCCGTAGGAGT", "BarcodeSequence": "CGCTTATCGAGA", "Description": "human gut", "BODY_SITE": "gut"}, {"LinkerPrimerSequence": "CATGCTGCCTCCCGTAGGAGT", "BarcodeSequence": "CATACCAGTAGC", "Description": "human gut", "BODY_SITE": "gut"}, {"LinkerPrimerSequence": "CATGCTGCCTCCCGTAGGAGT", "BarcodeSequence": "CTCTCTACCTGT", "Description": "human gut", "BODY_SITE": "gut"}, {"LinkerPrimerSequence": "CATGCTGCCTCCCGTAGGAGT", "BarcodeSequence": "CTCTCGGCCTGT", "Description": "human skin", "BODY_SITE": "skin"}, {"LinkerPrimerSequence": "CATGCTGCCTCCCGTAGGAGT", "BarcodeSequence": "CTCTCTACCAAT", "Description": "human skin", "BODY_SITE": "skin"}, {"LinkerPrimerSequence": "CATGCTGCCTCCCGTAGGAGT", "BarcodeSequence": "CTAACTACCAAT", "Description": "human skin", "BODY_SITE": "skin"}]"
}
}
}
}
}
The biom file format: Version 2.1¶
The biom format is based on HDF5 to provide the overall structure for the format. HDF5 is a widely supported format with native parsers available within many programming languages. Required top-level attributes:id : <string or null> a field that can be used to id a table (or null)
type : <string> Table type (a controlled vocabulary)
Acceptable values:
"OTU table"
"Pathway table"
"Function table"
"Ortholog table"
"Gene table"
"Metabolite table"
"Taxon table"
format-url : <url> A string with a static URL providing format details
format-version : <tuple> The version of the current biom format, major and minor
generated-by : <string> Package and revision that built the table
creation-date : <datetime> Date the table was built (ISO 8601 format)
shape : <list of ints>, the number of rows and number of columns in data
nnz : <int> The number of non-zero elements in the table
Required groups:
observation/ : The HDF5 group that contains observation specific information and an observation oriented view of the data observation/matrix : The HDF5 group that contains matrix data oriented for observation-wise operations (e.g., in compressed sparse row format) observation/metadata : The HDF5 group that contains observation specific metadata information observation/group-metadata : The HDF5 group that contains observation specific group metadata information (e.g., phylogenetic tree) sample/ : The HDF5 group that contains sample specific information and a sample oriented data oriented view of the data sample/matrix : The HDF5 group that contains matrix data oriented for sample-wise operations (e.g., in compressed sparse column format) sample/metadata : The HDF5 group that contains sample specific metadata information sample/group-metadata : The HDF5 group that contains sample specific group metadata information (e.g., relationships between samples)
Required datasets:
observation/ids : <string> or <variable length string> A (N,) dataset of the observation IDs, where N is the total number of IDs observation/matrix/data : <float64> A (nnz,) dataset containing the actual matrix data observation/matrix/indices : <int32> A (nnz,) dataset containing the column indices (e.g., maps into samples/ids) observation/matrix/indptr : <int32> A (M+1,) dataset containing the compressed row offsets sample/ids : <string> or <variable length string> A (M,) dataset of the sample IDs, where M is the total number of IDs sample/matrix/data : <float64> A (nnz,) dataset containing the actual matrix data sample/matrix/indices : <int32> A (nnz,) dataset containing the row indices (e.g., maps into observation/ids) sample/matrix/indptr : <int32> A (N+1,) dataset containing the compressed column offsets
Under the observation/metadata and sample/metadata groups, the user can specify an arbitrary number of datasets that represents a metadata category for that axis. The expected structure for each of these metadata datasets is a list of atomic type objects (int, float, str, ...) where the index order of the list corresponds to the index order of the relevant axis IDs. Special complex metadata fields have been defined, and they are stored in a specific way. Currently, the available special metadata fields are:
observation/metadata/taxonomy : <string> or <variable length string> A (N, ?) dataset containing the taxonomy names assigned to the observation observation/metadata/KEGG_Pathways : <string> or <variable length string> A (N, ?) dataset containing the KEGG Pathways assigned to the observation observation/metadata/collapsed_ids : <string> or <variable length string> A (N, ?) dataset containing the observation ids of the original table that have been collapsed in the given observation sample/metadata/collapsed_ids : <string> or <variable length string> A (M, ?) dataset containing the sample ids of the original table that have been collapsed in the given sample
Under the observation/group-metadata and sample/group-metadata groups, the user can specify an arbitrary number of datasets that represents a relationship between the ids for that axis. The expected structure for each of these group metadata datasets is a single string or variable length string. Each of these datasets should have defined an attribute called data_type, which specifies how the string should be interpreted. One example of such group metadata dataset is observation/group-metadata/phylogeny, with the attribute observation/group-metadata/phylogeny.attrs['data_type'] = "newick", which stores a single string with the newick format of the phylogenetic tree for the observations.
Example biom files¶
Below is an examples of a rich biom file. To decide which of these you should generate for new data types, see the section on sparse-or-dense.BIOM 2.1 OTU table in the HDF5 data description langauge (DDL)¶
HDF5 "examples/rich_sparse_otu_table_hdf5.biom" {
GROUP "/" {
ATTRIBUTE "creation-date" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "2014-07-29T16:16:36.617320"
}
}
ATTRIBUTE "format-url" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "http://biom-format.org"
}
}
ATTRIBUTE "format-version" {
DATATYPE H5T_STD_I64LE
DATASPACE SIMPLE { ( 2 ) / ( 2 ) }
DATA {
(0): 2, 1
}
}
ATTRIBUTE "generated-by" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "example"
}
}
ATTRIBUTE "id" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "No Table ID"
}
}
ATTRIBUTE "nnz" {
DATATYPE H5T_STD_I64LE
DATASPACE SCALAR
DATA {
(0): 15
}
}
ATTRIBUTE "shape" {
DATATYPE H5T_STD_I64LE
DATASPACE SIMPLE { ( 2 ) / ( 2 ) }
DATA {
(0): 5, 6
}
}
ATTRIBUTE "type" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "otu table"
}
}
GROUP "observation" {
GROUP "group-metadata" {
}
DATASET "ids" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 5 ) / ( 5 ) }
DATA {
(0): "GG_OTU_1", "GG_OTU_2", "GG_OTU_3", "GG_OTU_4", "GG_OTU_5"
}
}
GROUP "matrix" {
DATASET "data" {
DATATYPE H5T_IEEE_F64LE
DATASPACE SIMPLE { ( 15 ) / ( 15 ) }
DATA {
(0): 1, 5, 1, 2, 3, 1, 1, 4, 2, 2, 1, 1, 1, 1, 1
}
}
DATASET "indices" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 15 ) / ( 15 ) }
DATA {
(0): 2, 0, 1, 3, 4, 5, 2, 3, 5, 0, 1, 2, 5, 1, 2
}
}
DATASET "indptr" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 6 ) / ( 6 ) }
DATA {
(0): 0, 1, 6, 9, 13, 15
}
}
}
GROUP "metadata" {
DATASET "taxonomy" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 5, 7 ) / ( 5, 7 ) }
DATA {
(0,0): "k__Bacteria", "p__Proteobacteria",
(0,2): "c__Gammaproteobacteria", "o__Enterobacteriales",
(0,4): "f__Enterobacteriaceae", "g__Escherichia", "s__",
(1,0): "k__Bacteria", "p__Cyanobacteria", "c__Nostocophycideae",
(1,3): "o__Nostocales", "f__Nostocaceae", "g__Dolichospermum",
(1,6): "s__",
(2,0): "k__Archaea", "p__Euryarchaeota", "c__Methanomicrobia",
(2,3): "o__Methanosarcinales", "f__Methanosarcinaceae",
(2,5): "g__Methanosarcina", "s__",
(3,0): "k__Bacteria", "p__Firmicutes", "c__Clostridia",
(3,3): "o__Halanaerobiales", "f__Halanaerobiaceae",
(3,5): "g__Halanaerobium", "s__Halanaerobiumsaccharolyticum",
(4,0): "k__Bacteria", "p__Proteobacteria",
(4,2): "c__Gammaproteobacteria", "o__Enterobacteriales",
(4,4): "f__Enterobacteriaceae", "g__Escherichia", "s__"
}
}
}
}
GROUP "sample" {
GROUP "group-metadata" {
}
DATASET "ids" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 6 ) / ( 6 ) }
DATA {
(0): "Sample1", "Sample2", "Sample3", "Sample4", "Sample5",
(5): "Sample6"
}
}
GROUP "matrix" {
DATASET "data" {
DATATYPE H5T_IEEE_F64LE
DATASPACE SIMPLE { ( 15 ) / ( 15 ) }
DATA {
(0): 5, 2, 1, 1, 1, 1, 1, 1, 1, 2, 4, 3, 1, 2, 1
}
}
DATASET "indices" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 15 ) / ( 15 ) }
DATA {
(0): 1, 3, 1, 3, 4, 0, 2, 3, 4, 1, 2, 1, 1, 2, 3
}
}
DATASET "indptr" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 7 ) / ( 7 ) }
DATA {
(0): 0, 2, 5, 9, 11, 12, 15
}
}
}
GROUP "metadata" {
DATASET "BODY_SITE" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_UTF8;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 6 ) / ( 6 ) }
DATA {
(0): "gut", "gut", "gut", "skin", "skin", "skin"
}
}
DATASET "BarcodeSequence" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_UTF8;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 6 ) / ( 6 ) }
DATA {
(0): "CGCTTATCGAGA", "CATACCAGTAGC", "CTCTCTACCTGT",
(3): "CTCTCGGCCTGT", "CTCTCTACCAAT", "CTAACTACCAAT"
}
}
DATASET "Description" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_UTF8;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 6 ) / ( 6 ) }
DATA {
(0): "human gut", "human gut", "human gut", "human skin",
(4): "human skin", "human skin"
}
}
DATASET "LinkerPrimerSequence" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_UTF8;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 6 ) / ( 6 ) }
DATA {
(0): "CATGCTGCCTCCCGTAGGAGT", "CATGCTGCCTCCCGTAGGAGT",
(2): "CATGCTGCCTCCCGTAGGAGT", "CATGCTGCCTCCCGTAGGAGT",
(4): "CATGCTGCCTCCCGTAGGAGT", "CATGCTGCCTCCCGTAGGAGT"
}
}
}
}
}
}
Release versions contain three integers in the following format: major-version.minor-version.micro-version. When -dev is appended to the end of a version string that indicates a development (or between-release version). For example, 1.0.0-dev would refer to the development version following the 1.0.0 release.
Tips and FAQs regarding the BIOM file format¶
Motivation for the BIOM format¶
The BIOM format was motivated by several goals. First, to facilitate efficient handling and storage of large, sparse biological contingency tables; second, to support encapsulation of core study data (contingency table data and sample/observation metadata) in a single file; and third, to facilitate the use of these tables between tools that support this format (e.g., passing of data between QIIME, MG-RAST, and VAMPS.).Efficient handling and storage of very large tables¶
In QIIME, we began hitting limitations with OTU table objects when working with thousands of samples and hundreds of thousands of OTUs. In the near future we expect that we'll be dealing with hundreds of thousands of samples in single analyses. The OTU table format up to QIIME 1.4.0 involved a dense matrix: if an OTU was not observed in a given sample, that would be indicated with a zero. We now primarily represent OTU tables in a sparse format: if an OTU is not observed in a sample, there is no count for that OTU. The two ways of representing this data are exemplified here. A dense representation of an OTU table:OTU ID PC.354 PC.355 PC.356 OTU0 0 0 4 OTU1 6 0 0 OTU2 1 0 7 OTU3 0 0 3
A sparse representation of an OTU table:
PC.354 OTU1 6 PC.354 OTU2 1 PC.356 OTU0 4 PC.356 OTU2 7 PC.356 OTU3 3
OTU table data tends to be sparse (e.g., greater than 90% of counts are zero, and frequently as many as 99% of counts are zero) in which case the latter format is more convenient to work with as it has a smaller memory footprint. Both of these representations are supported in the biom-format project via dense and sparse Table types. Generally if less than 85% of your counts are zero, a dense representation will be more efficient.
Encapsulation of core study data (OTU table data and sample/OTU metadata) in a single file¶
Formats, such as JSON and HDF5, made more efficient storage of highly sparse data and allowed for storage of arbitrary amounts of sample and OTU metadata in a single file. Sample metadata corresponds to what is generally found in QIIME mapping files. At this stage inclusion of this information in the OTU table file is optional, but it may be useful for sharing these files with other QIIME users and for publishing or archiving results of analyses. OTU metadata (generally a taxonomic assignment for an OTU) is also optional. In contrast to the previous OTU table format, you can now store more than one OTU metadata value in this field, so for example you can score taxonomic assignments based on two different taxonomic assignment approaches.Facilitating the use of tables between tools that support this format¶
Different tools, such as QIIME, MG-RAST, and VAMPS work with similar data structures that represent different types of data. An example of this is a metagenome table that could be generated by MG-RAST (where for example, columns are metagenomes and rows are functional categories). Exporting this data from MG-RAST in a suitable format will allow for the application of many of the QIIME tools to this data (such as generation of alpha rarefaction plots or beta diversity ordination plots). This new format is far more general than previous formats, so will support adoption by groups working with different data types and is already being integrated to support transfer of data between QIIME, MG-RAST, and VAMPS.File extension¶
We recommend that BIOM files use the .biom extension.Quick start¶
BIOM has an example table and two methods for reading in Table objects that are immediately available at the package level.Functions¶
| load_table(f) | Load a Table from a path |
biom.load_table¶
- biom.load_table(f)
- Load a Table from a path
- Parameters
- f : str
- Returns
- Table
- Raises
- IOError
If the path does not exist
TypeError
If the data in the path does not appear to be a BIOM
table
Examples Parse a table from a path. BIOM will attempt to determine if the fhe file is either in TSV, HDF5, JSON, gzip'd JSON or gzip'd TSV and parse accordingly:
>>> from biom import load_table
>>> table = load_table('path/to/table.biom') # doctest: +SKIP
Examples¶
Load an example table:>>> from biom import example_table >>> print example_table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S1 S2 S3 O1 0.0 1.0 2.0 O2 3.0 4.0 5.0Parse a table from an open file object:
>>> from biom import parse_table
>>> with open('path/to/table.biom') as f: # doctest: +SKIP
... table = parse_table(f)
Parse a table from a path. BIOM will attempt to determine if the file is either
in TSV, HDF5, JSON, gzip'd JSON or gzip'd TSV and parse accordingly:
>>> from biom import load_table
>>> table = load_table('path/to/table.biom') # doctest: +SKIP
BIOM Table ( biom.table)¶
The biom-format project provides rich Table objects to support use of the BIOM file format. The objects encapsulate matrix data (such as OTU counts) and abstract the interaction away from the programmer.Classes¶
| Table(data, observation_ids, sample_ids[, ...]) | The (canonically pronounced 'teh') Table. |
biom.table.Table¶
- class biom.table.Table(data, observation_ids, sample_ids, observation_metadata=None, sample_metadata=None, table_id=None, type=None, create_date=None, generated_by=None, observation_group_metadata=None, sample_group_metadata=None, **kwargs)
- The (canonically pronounced 'teh') Table.
Give in to the power of the Table! Attributes
dtype The type of the objects in the underlying contingency matrix matrix_data The sparse matrix object nnz Number of non-zero elements of the underlying contingency matrix shape The shape of the underlying contingency matrix
biom.table.Table.dtype¶
- Table.dtype
- The type of the objects in the underlying contingency matrix
biom.table.Table.matrix_data¶
- Table.matrix_data
- The sparse matrix object
biom.table.Table.nnz¶
- Table.nnz
- Number of non-zero elements of the underlying contingency matrix
biom.table.Table.shape¶
- Table.shape
- The shape of the underlying contingency matrix
Methods
| __getitem__(args) | Handles row or column slices |
| _extract_data_from_tsv(lines[, delim, ...]) | Parse a classic table into (sample_ids, obs_ids, data, metadata, |
| add_group_metadata(group_md[, axis]) | Take a dict of group metadata and add it to an axis |
| add_metadata(md[, axis]) | Take a dict of metadata and add it to an axis. |
| collapse(f[, reduce_f, norm, ...]) | Collapse partitions in a table by metadata or by IDs |
| copy() | Returns a copy of the table |
| data(id[, axis, dense]) | Returns data associated with an id |
| delimited_self([delim, header_key, ...]) | Return self as a string in a delimited form |
| descriptive_equality(other) | For use in testing, describe how the tables are not equal |
| exists(id[, axis]) | Returns whether id exists in axis |
| filter(ids_to_keep[, axis, invert, inplace]) | Filter a table based on a function or iterable. |
| from_hdf5(h5grp[, ids, axis]) | Parse an HDF5 formatted BIOM table |
| from_json(json_table[, data_pump, ...]) | Parse a biom otu table type |
| from_tsv(lines, obs_mapping, sample_mapping, ...) | Parse a tab separated (observation x sample) formatted BIOM table |
| get_table_density() | Returns the fraction of nonzero elements in the table. |
| get_value_by_ids(obs_id, samp_id) | Return value in the matrix corresponding to (obs_id, samp_id) |
| group_metadata([axis]) | Return the group metadata of the given axis |
| ids([axis]) | Return the ids along the given axis |
| index(id, axis) | Return the index of the identified sample/observation. |
| is_empty() | Check whether the table is empty |
| iter([dense, axis]) | Yields (value, id, metadata) |
| iter_data([dense, axis]) | Yields axis values |
| iter_pairwise([dense, axis, tri, diag]) | Pairwise iteration over self |
| max([axis]) | Get the maximum nonzero value over an axis |
| merge(other[, sample, observation, ...]) | Merge two tables together |
| metadata([id, axis]) | Return the metadata of the identified sample/observation. |
| min([axis]) | Get the minimum nonzero value over an axis |
| nonzero() | Yields locations of nonzero elements within the data matrix |
| nonzero_counts(axis[, binary]) | Get nonzero summaries about an axis |
| norm([axis, inplace]) | Normalize in place sample values by an observation, or vice versa. |
| pa([inplace]) | Convert the table to presence/absence data |
| partition(f[, axis]) | Yields partitions |
| reduce(f, axis) | Reduce over axis using function f |
| sort([sort_f, axis]) | Return a table sorted along axis |
| sort_order(order[, axis]) | Return a new table with axis in order |
| subsample(n[, axis, by_id]) | Randomly subsample without replacement. |
| sum([axis]) | Returns the sum by axis |
| to_hdf5(h5grp, generated_by[, compress]) | Store CSC and CSR in place |
| to_json(generated_by[, direct_io]) | Returns a JSON string representing the table in BIOM format. |
| to_tsv([header_key, header_value, ...]) | Return self as a string in tab delimited form |
| transform(f[, axis, inplace]) | Iterate over axis, applying a function f to each vector. |
| transpose() | Transpose the contingency table |
biom.table.Table.__getitem__¶
- Table.__getitem__(args)
- Handles row or column slices Slicing over an individual axis is supported, but slicing over both axes at the same time is not supported. Partial slices, such as foo[0, 5:10] are not supported, however full slices are supported, such as foo[0, :].
- Parameters
- args : tuple or slice
The specific element (by index position) to return or an
entire row or column of the data.
- Returns
- float or spmatrix
A float is return if a specific element is specified,
otherwise a spmatrix object representing a vector of sparse data is
returned.
- Raises
- IndexError
- •
- If the matrix is empty
- •
- If the arguments do not appear to be a tuple
- •
- If a slice on row and column is specified
- •
- If a partial slice is specified
Notes Switching between slicing rows and columns is inefficient. Slicing of rows requires a CSR representation, while slicing of columns requires a CSC representation, and transforms are performed on the data if the data are not in the required representation. These transforms can be expensive if done frequently.
biom.table.Table._extract_data_from_tsv¶
- static Table._extract_data_from_tsv(lines, delim='t', dtype=<type 'float'>, header_mark=None, md_parse=None)
- Parse a classic table into (sample_ids, obs_ids, data, metadata, name)
- Parameters
- lines: list or file-like object
delimted data to parse
delim: string
delimeter in file lines
dtype: type header_mark: string or None
string that indicates start of header line
md_parse: function or None
funtion used to parse metdata
- Returns
- list
sample_ids
list
observation_ids
array
data
list
metadata
string
column name if last column is non-numeric
Notes This is intended to be close to how QIIME classic OTU tables are parsed with the exception of the additional md_name field This function is ported from QIIME ( http://www.qiime.org), previously named parse_classic_otu_table. QIIME is a GPL project, but we obtained permission from the authors of this function to port it to the BIOM Format project (and keep it under BIOM's BSD license).
biom.table.Table.add_group_metadata¶
- Table.add_group_metadata(group_md, axis='sample')
- Take a dict of group metadata and add it to an axis
- Parameters
- group_md : dict of tuples
group_md should be of the form {category: (data
type, value)
axis : {'sample', 'observation'}, optional
The axis to operate on
- Raises
- UnknownAxisError
If provided an unrecognized axis.
biom.table.Table.add_metadata¶
- Table.add_metadata(md, axis='sample')
- Take a dict of metadata and add it to an axis.
- Parameters
- md : dict of dict
md should be of the form {id:
{dict_of_metadata}}
axis : {'sample', 'observation'}, optional
The axis to operate on
biom.table.Table.collapse¶
- Table.collapse(f, reduce_f=<built-in function add>, norm=True, min_group_size=1, include_collapsed_metadata=True, one_to_many=False, one_to_many_mode='add', one_to_many_md_key='Path', strict=False, axis='sample')
- Collapse partitions in a table by metadata or by IDs Partition data by metadata or IDs and then collapse each partition into a single vector. If include_collapsed_metadata is True, the metadata for the collapsed partition will be a category named 'collapsed_ids', in which a list of the original ids that made up the partition is retained The remainder is only relevant to setting one_to_many to True. If one_to_many is True, allow vectors to collapse into multiple bins if the metadata describe a one-many relationship. Supplied functions must allow for iteration support over the metadata key and must return a tuple of (path, bin) as to describe both the path in the hierarchy represented and the specific bin being collapsed into. The uniqueness of the bin is _not_ based on the path but by the name of the bin. The metadata value for the corresponding collapsed column may include more (or less) information about the collapsed data. For example, if collapsing "FOO", and there are vectors that span three associations A, B, and C, such that vector 1 spans A and B, vector 2 spans B and C and vector 3 spans A and C, the resulting table will contain three collapsed vectors:
- •
- A, containing original vectors 1 and 3
- •
- B, containing original vectors 1 and 2
- •
- C, containing original vectors 2 and 3
If a vector maps to the same partition multiple times, it will be counted multiple times. There are two supported modes for handling one-to-many relationships via one_to_many_mode: add and divide. add will add the vector counts to each partition that the vector maps to, which may increase the total number of counts in the output table. divide will divide a vectors's counts by the number of metadata that the vector has before adding the counts to each partition. This will not increase the total number of counts in the output table. If one_to_many_md_key is specified, that becomes the metadata key that describes the collapsed path. If a value is not specified, then it defaults to 'Path'. If strict is specified, then all metadata pathways operated on must be indexable by metadata_f. one_to_many and norm are not supported together. one_to_many and reduce_f are not supported together. one_to_many and min_group_size are not supported together. A final note on space consumption. At present, the one_to_many functionality requires a temporary dense matrix representation.
- Parameters
- f : function
Function that is used to determine what partition a
vector belongs to
reduce_f : function, optional
Defaults to operator.add. Function that reduces
two vectors in a one-to-one collapse
norm : bool, optional
Defaults to True. If True, normalize the
resulting table
min_group_size : int, optional
Defaults to 1. The minimum size of a partition
when performing a one-to-one collapse
include_collapsed_metadata : bool, optional
Defaults to True. If True, retain the
collapsed metadata keyed by the original IDs of the associated vectors
one_to_many : bool, optional
Defaults to False. Perform a one-to-many
collapse
one_to_many_mode : {'add', 'divide'}, optional
The way to reduce two vectors in a one-to-many
collapse
one_to_many_md_key : str, optional
Defaults to "Path". If
include_collapsed_metadata is True, store the original vector
metadata under this key
strict : bool, optional
Defaults to False. Requires full pathway data
within a one-to-many structure
axis : {'sample', 'observation'}, optional
The axis to collapse
- Returns
- Table
The collapsed table
Examples.sp
>>> import numpy as np >>> from biom.table import TableCreate a Table
>>> dt_rich = Table(
... np.array([[5, 6, 7], [8, 9, 10], [11, 12, 13]]),
... ['1', '2', '3'], ['a', 'b', 'c'],
... [{'taxonomy': ['k__a', 'p__b']},
... {'taxonomy': ['k__a', 'p__c']},
... {'taxonomy': ['k__a', 'p__c']}],
... [{'barcode': 'aatt'},
... {'barcode': 'ttgg'},
... {'barcode': 'aatt'}])
>>> print dt_rich # doctest: +NORMALIZE_WHITESPACE
# Constructed from biom file
#OTU ID a b c
1 5.0 6.0 7.0
2 8.0 9.0 10.0
3 11.0 12.0 13.0
Create Function to determine what partition a vector belongs to
>>> bin_f = lambda id_, x: x['taxonomy'][1] >>> obs_phy = dt_rich.collapse( ... bin_f, norm=False, min_group_size=1, ... axis='observation').sort(axis='observation') >>> print obs_phy # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID a b c p__b 5.0 6.0 7.0 p__c 19.0 21.0 23.0
biom.table.Table.copy¶
- Table.copy()
- Returns a copy of the table
biom.table.Table.data¶
- Table.data(id, axis='sample', dense=True)
- Returns data associated with an id
- Parameters
- id : str
ID of the samples or observations whose data will be
returned.
axis : {'sample', 'observation'}
Axis to search for id.
dense : bool, optional
If True, return data as dense
- Returns
- np.ndarray or scipy.sparse.spmatrix
np.ndarray if dense, otherwise
scipy.sparse.spmatrix
- Raises
- UnknownAxisError
If provided an unrecognized axis.
Examples.sp
>>> from biom import example_table
>>> example_table.data('S1', axis='sample')
array([ 0., 3.])
biom.table.Table.delimited_self¶
- Table.delimited_self(delim='t', header_key=None, header_value=None, metadata_formatter=<type 'str'>, observation_column_name='#OTU ID')
- Return self as a string in a delimited form Default str output for the Table is just row/col ids and table data without any metadata Including observation metadata in output: If header_key is not None, the observation metadata with that name will be included in the delimited output. If header_value is also not None, the observation metadata will use the provided header_value as the observation metadata name (i.e., the column header) in the delimited output. metadata_formatter: a function which takes a metadata entry and returns a formatted version that should be written to file observation_column_name: the name of the first column in the output table, corresponding to the observation IDs. For example, the default will look something like:
#OTU ID Sample1 Sample2 OTU1 10 2 OTU2 4 8
biom.table.Table.descriptive_equality¶
- Table.descriptive_equality(other)
- For use in testing, describe how the tables are not equal
biom.table.Table.exists¶
- Table.exists(id, axis='sample')
- Returns whether id exists in axis
- Parameters
- id: str
id to check if exists
axis : {'sample', 'observation'}, optional
The axis to check
- Returns
- bool
True if id exists, False
otherwise
Examples.sp
>>> import numpy as np >>> from biom.table import TableCreate a 2x3 BIOM table:
>>> data = np.asarray([[0, 0, 1], [1, 3, 42]]) >>> table = Table(data, ['O1', 'O2'], ['S1', 'S2', 'S3'])Check whether sample ID is in the table:
>>> table.exists('S1')
True
>>> table.exists('S4')
False
Check whether an observation ID is in the table:
>>> table.exists('O1', 'observation')
True
>>> table.exists('O3', 'observation')
False
biom.table.Table.filter¶
- Table.filter(ids_to_keep, axis='sample', invert=False, inplace=True)
- Filter a table based on a function or iterable.
- Parameters
- ids_to_keep : iterable, or function(values, id, metadata) -> bool
If a function, it will be called with the values of the
sample/observation, its id (a string) and the dictionary of metadata of each
sample/observation, and must return a boolean. If it's an iterable, it must be
a list of ids to keep.
axis : {'sample', 'observation'}, optional
It controls whether to filter samples or observations and
defaults to "sample".
invert : bool, optional
Defaults to False. If set to True, discard
samples or observations where ids_to_keep returns True
inplace : bool, optional
Defaults to True. Whether to return a new table or
modify itself.
- Returns
- biom.Table
Returns itself if inplace, else returns a new
filtered table.
- Raises
- UnknownAxisError
If provided an unrecognized axis.
Examples.sp
>>> import numpy as np >>> from biom.table import TableCreate a 2x3 BIOM table, with observation metadata and sample metadata:
>>> data = np.asarray([[0, 0, 1], [1, 3, 42]])
>>> table = Table(data, ['O1', 'O2'], ['S1', 'S2', 'S3'],
... [{'full_genome_available': True},
... {'full_genome_available': False}],
... [{'sample_type': 'a'}, {'sample_type': 'a'},
... {'sample_type': 'b'}])
Define a function to keep only samples with sample_type == 'a'. This will drop
sample S3, which has sample_type 'b':
>>> filter_fn = lambda val, id_, md: md['sample_type'] == 'a'Get a filtered version of the table, leaving the original table untouched:
>>> new_table = table.filter(filter_fn, inplace=False) >>> print table.ids() ['S1' 'S2' 'S3'] >>> print new_table.ids() ['S1' 'S2']Using the same filtering function, discard all samples with sample_type 'a'. This will keep only sample S3, which has sample_type 'b':
>>> new_table = table.filter(filter_fn, inplace=False, invert=True) >>> print table.ids() ['S1' 'S2' 'S3'] >>> print new_table.ids() ['S3']Filter the table in-place using the same function (drop all samples where sample_type is not 'a'):
>>> table.filter(filter_fn) 2 x 2 <class 'biom.table.Table'> with 2 nonzero entries (50% dense) >>> print table.ids() ['S1' 'S2']Filter out all observations in the table that do not have full_genome_available == True. This will filter out observation O2:
>>> filter_fn = lambda val, id_, md: md['full_genome_available'] >>> table.filter(filter_fn, axis='observation') 1 x 2 <class 'biom.table.Table'> with 0 nonzero entries (0% dense) >>> print table.ids(axis='observation') ['O1']
biom.table.Table.from_hdf5¶
- classmethod Table.from_hdf5(h5grp, ids=None, axis='sample')
- Parse an HDF5 formatted BIOM table If ids is provided, only the samples/observations listed in ids (depending on the value of axis) will be loaded The expected structure of this group is below. A few basic definitions, N is the number of observations and M is the number of samples. Data are stored in both compressed sparse row (for observation oriented operations) and compressed sparse column (for sample oriented operations).
- Parameters
- h5grp : a h5py Group or an open h5py File ids : iterable
The sample/observation ids of the samples/observations
that we need to retrieve from the hdf5 biom table
axis : {'sample', 'observation'}, optional
The axis to subset on
- Returns
- biom.Table
A BIOM Table object
- Raises
- ValueError
If ids are not a subset of the samples or
observations ids present in the hdf5 biom table
SEE ALSO:
Table.to_hdf5
Notes The expected HDF5 group structure is below. An example of an HDF5 file in DDL can be found here [R9].
- •
- ./id : str, an arbitrary ID
- •
- ./type : str, the table type (e.g, OTU table)
- •
- ./format-url : str, a URL that describes the format
- •
- ./format-version : two element tuple of int32, major and minor
- •
- ./generated-by : str, what generated this file
- •
- ./creation-date : str, ISO format
- •
- ./shape : two element tuple of int32, N by M
- •
- ./nnz : int32 or int64, number of non zero elems
- •
- ./observation : Group
- •
- ./observation/ids : (N,) dataset of str or vlen str
- •
- ./observation/matrix : Group
- •
- ./observation/matrix/data : (nnz,) dataset of float64
- •
- ./observation/matrix/indices : (nnz,) dataset of int32
- •
- ./observation/matrix/indptr : (M+1,) dataset of int32
- •
- ./observation/metadata : Group
- •
- [./observation/metadata/foo] : Optional, (N,) dataset of any valid HDF5 type in index order with IDs.
- •
- ./observation/group-metadata : Group
- •
- [./observation/group-metadata/foo] : Optional, (?,) dataset of group metadata that relates IDs
- •
- [./observation/group-metadata/foo.attrs['data_type']] : attribute of the foo dataset that describes contained type (e.g., newick)
- •
- ./sample : Group
- •
- ./sample/ids : (M,) dataset of str or vlen str
- •
- ./sample/matrix : Group
- •
- ./sample/matrix/data : (nnz,) dataset of float64
- •
- ./sample/matrix/indices : (nnz,) dataset of int32
- •
- ./sample/matrix/indptr : (N+1,) dataset of int32
- •
- ./sample/metadata : Group
- •
- [./sample/metadata/foo] : Optional, (M,) dataset of any valid HDF5 type in index order with IDs.
- •
- ./sample/group-metadata : Group
- •
- [./sample/group-metadata/foo] : Optional, (?,) dataset of group metadata that relates IDs
- •
- [./sample/group-metadata/foo.attrs['data_type']] : attribute of the foo dataset that describes contained type (e.g., newick)
The '?' character on the dataset size means that it can be of arbitrary length. The expected structure for each of the metadata datasets is a list of atomic type objects (int, float, str, ...), where the index order of the list corresponds to the index order of the relevant axis IDs. Special metadata fields have been defined, and they are stored in a specific way. Currently, the available special metadata fields are:
- •
- taxonomy: (N, ?) dataset of str or vlen str
- •
- KEGG_Pathways: (N, ?) dataset of str or vlen str
- •
- collapsed_ids: (N, ?) dataset of str or vlen str
References.IP [R7] 5 http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.sparse.csr_matrix.html
- [R9]
- http://biom-format.org/documentation/format_versions/biom-2.0.html
Examples.sp
>>> from biom.table import Table >>> from biom.util import biom_open >>> with biom_open('rich_sparse_otu_table_hdf5.biom') as f # doctest: +SKIP >>> t = Table.from_hdf5(f) # doctest: +SKIPParse a hdf5 biom table subsetting observations >>> from biom.util import biom_open # doctest: +SKIP >>> from biom.parse import parse_biom_table >>> with biom_open('rich_sparse_otu_table_hdf5.biom') as f # doctest: +SKIP >>> t = Table.from_hdf5(f, ids=["GG_OTU_1"],
biom.table.Table.from_json¶
- classmethod Table.from_json(json_table, data_pump=None, input_is_dense=False)
- Parse a biom otu table type
- Parameters
- json_table : dict
A JSON object or dict that represents the BIOM
table
data_pump : tuple or None
A secondary source of data
input_is_dense : bool
If True, the data contained will be interpretted
as dense
- Returns
- Table
Examples.sp
>>> from biom import Table
>>> json_obj = {"id": "None",
... "format": "Biological Observation Matrix 1.0.0",
... "format_url": "http://biom-format.org",
... "generated_by": "foo",
... "type": "OTU table",
... "date": "2014-06-03T14:24:40.884420",
... "matrix_element_type": "float",
... "shape": [5, 6],
... "data": [[0,2,1.0],
... [1,0,5.0],
... [1,1,1.0],
... [1,3,2.0],
... [1,4,3.0],
... [1,5,1.0],
... [2,2,1.0],
... [2,3,4.0],
... [2,5,2.0],
... [3,0,2.0],
... [3,1,1.0],
... [3,2,1.0],
... [3,5,1.0],
... [4,1,1.0],
... [4,2,1.0]],
... "rows": [{"id": "GG_OTU_1", "metadata": None},
... {"id": "GG_OTU_2", "metadata": None},
... {"id": "GG_OTU_3", "metadata": None},
... {"id": "GG_OTU_4", "metadata": None},
... {"id": "GG_OTU_5", "metadata": None}],
... "columns": [{"id": "Sample1", "metadata": None},
... {"id": "Sample2", "metadata": None},
... {"id": "Sample3", "metadata": None},
... {"id": "Sample4", "metadata": None},
... {"id": "Sample5", "metadata": None},
... {"id": "Sample6", "metadata": None}]
... }
>>> t = Table.from_json(json_obj)
biom.table.Table.from_tsv¶
- static Table.from_tsv(lines, obs_mapping, sample_mapping, process_func, **kwargs)
- Parse a tab separated (observation x sample) formatted BIOM table
- Parameters
- lines : list, or file-like object
The tab delimited data to parse
obs_mapping : dict or None
The corresponding observation metadata
sample_mapping : dict or None
The corresponding sample metadata
process_func : function
A function to transform the observation metadata
- Returns
- biom.Table
A BIOM Table object
Examples Parse tab separated data into a table:
>>> from biom.table import Table >>> from StringIO import StringIO >>> tsv = 'a\tb\tc\n1\t2\t3\n4\t5\t6' >>> tsv_fh = StringIO(tsv) >>> func = lambda x : x >>> test_table = Table.from_tsv(tsv_fh, None, None, func)
biom.table.Table.get_table_density¶
- Table.get_table_density()
- Returns the fraction of nonzero elements in the table.
- Returns
- float
The fraction of nonzero elements in the table
biom.table.Table.get_value_by_ids¶
- Table.get_value_by_ids(obs_id, samp_id)
- Return value in the matrix corresponding to (obs_id, samp_id)
- Parameters
- obs_id : str
The ID of the observation
samp_id : str
The ID of the sample
- Returns
- float
The data value corresponding to the specified matrix
position
biom.table.Table.group_metadata¶
- Table.group_metadata(axis='sample')
- Return the group metadata of the given axis
- Parameters
- axis : {'sample', 'observation'}, optional
Axis to search for the group metadata. Defaults to
'sample'
- Returns
- dict
The corresponding group metadata for the given axis
- Raises
- UnknownAxisError
If provided an unrecognized axis.
Examples.sp
>>> import numpy as np >>> from biom.table import TableCreate a 2x3 BIOM table, with group observation metadata and no group sample metadata:
>>> data = np.asarray([[0, 0, 1], [1, 3, 42]])
>>> group_observation_md = {'tree': ('newick', '(O1:0.3,O2:0.4);')}
>>> table = Table(data, ['O1', 'O2'], ['S1', 'S2', 'S3'],
... observation_group_metadata=group_observation_md)
Get the observation group metadata:
>>> table.group_metadata(axis='observation')
{'tree': ('newick', '(O1:0.3,O2:0.4);')}
Get the sample group metadata:
>> table.group_metadata() Nonebiom.table.Table.ids¶
- Table.ids(axis='sample')
- Return the ids along the given axis
- Parameters
- axis : {'sample', 'observation'}, optional
Axis to search for id. Defaults to 'sample'
- Returns
- 1-D numpy array
The ids along the given axis
- Raises
- UnknownAxisError
If provided an unrecognized axis.
Examples.sp
>>> import numpy as np >>> from biom.table import TableCreate a 2x3 BIOM table:
>>> data = np.asarray([[0, 0, 1], [1, 3, 42]]) >>> table = Table(data, ['O1', 'O2'], ['S1', 'S2', 'S3'])Get the ids along the observation axis:
>>> print table.ids(axis='observation') ['O1' 'O2']Get the ids along the sample axis:
>>> print table.ids() ['S1' 'S2' 'S3']
biom.table.Table.index¶
- Table.index(id, axis)
- Return the index of the identified sample/observation.
- Parameters
- id : str
ID of the sample or observation whose index will be
returned.
axis : {'sample', 'observation'}
Axis to search for id.
- Returns
- int
Index of the sample/observation identified by
id.
- Raises
- UnknownAxisError
If provided an unrecognized axis.
UnknownIDError
If provided an unrecognized sample/observation ID.
Examples.sp
>>> import numpy as np >>> from biom.table import TableCreate a 2x3 BIOM table:
>>> data = np.asarray([[0, 0, 1], [1, 3, 42]]) >>> table = Table(data, ['O1', 'O2'], ['S1', 'S2', 'S3'])Get the index of the observation with ID "O2":
>>> table.index('O2', 'observation')
1
Get the index of the sample with ID "S1":
>>> table.index('S1', 'sample')
0
biom.table.Table.is_empty¶
- Table.is_empty()
- Check whether the table is empty
- Returns
- bool
True if the table is empty, False
otherwise
biom.table.Table.iter¶
- Table.iter(dense=True, axis='sample')
- Yields (value, id, metadata)
- Parameters
- dense : bool, optional
Defaults to True. If False, yield
compressed sparse row or compressed sparse columns if axis is
'observation' or 'sample', respectively.
axis : {'sample', 'observation'}, optional
The axis to iterate over.
- Returns
- GeneratorType
A generator that yields (values, id, metadata)
Examples.sp
>>> import numpy as np >>> from biom.table import TableCreate a 2x3 BIOM table:
>>> data = np.asarray([[0, 0, 1], [1, 3, 42]]) >>> table = Table(data, ['O1', 'O2'], ['S1', 'S2', 'Z3'])Iter over samples and keep those that start with an Z:
>>> [(values, id, metadata) ... for values, id, metadata in table.iter() if id[0]=='Z'] [(array([ 1., 42.]), 'Z3', None)]Iter over observations and add the 2nd column of the values
>>> col = [values[1] for values, id, metadata in table.iter()] >>> sum(col) 46.0
biom.table.Table.iter_data¶
- Table.iter_data(dense=True, axis='sample')
- Yields axis values
- Parameters
- dense : bool, optional
Defaults to True. If False, yield
compressed sparse row or compressed sparse columns if axis is
'observation' or 'sample', respectively.
axis : {'sample', 'observation'}, optional
Axis to iterate over.
- Returns
- generator
Yields list of values for each value in axis
- Raises
- UnknownAxisError
If axis other than 'sample' or 'observation' passed
Examples.sp
>>> import numpy as np >>> from biom.table import Table >>> data = np.arange(30).reshape(3,10) # 3 X 10 OTU X Sample table >>> obs_ids = ['o1', 'o2', 'o3'] >>> sam_ids = ['s%i' %i for i in range(1,11)] >>> bt = Table(data, observation_ids=obs_ids, sample_ids=sam_ids)Lets find the sample with the largest sum
>>> sample_gen = bt.iter_data(axis='sample') >>> max_sample_count = max([sample.sum() for sample in sample_gen]) >>> print max_sample_count 57.0
biom.table.Table.iter_pairwise¶
- Table.iter_pairwise(dense=True, axis='sample', tri=True, diag=False)
- Pairwise iteration over self
- Parameters
- dense : bool, optional
Defaults to True. If False, yield
compressed sparse row or compressed sparse columns if axis is
'observation' or 'sample', respectively.
axis : {'sample', 'observation'}, optional
The axis to iterate over.
tri : bool, optional
If True, just yield [i, j] and not [j, i]
diag : bool, optional
If True, yield [i, i]
- Returns
- GeneratorType
Yields [(val_i, id_i, metadata_i), (val_j, id_j,
metadata_j)]
- Raises
- UnknownAxisError
Examples.sp
>>> from biom import example_tableBy default, only the upper triangle without the diagonal of the resulting pairwise combinations is yielded.
>>> iter_ = example_table.iter_pairwise() >>> for (val_i, id_i, md_i), (val_j, id_j, md_j) in iter_: ... print id_i, id_j S1 S2 S1 S3 S2 S3The full pairwise combinations can also be yielded though.
>>> iter_ = example_table.iter_pairwise(tri=False, diag=True) >>> for (val_i, id_i, md_i), (val_j, id_j, md_j) in iter_: ... print id_i, id_j S1 S1 S1 S2 S1 S3 S2 S1 S2 S2 S2 S3 S3 S1 S3 S2 S3 S3
biom.table.Table.max¶
- Table.max(axis='sample')
- Get the maximum nonzero value over an axis
- Parameters
- axis : {'sample', 'observation', 'whole'}, optional
Defaults to "sample". The axis over which to
calculate maxima.
- Returns
- scalar of self.dtype or np.array of self.dtype
- Raises
- UnknownAxisError
If provided an unrecognized axis.
Examples.sp
>>> from biom import example_table >>> print example_table.max(axis='observation') [ 2. 5.]
biom.table.Table.merge¶
- Table.merge(other, sample='union', observation='union', sample_metadata_f=<function prefer_self at 0x7fe2a9d56b18>, observation_metadata_f=<function prefer_self at 0x7fe2a9d56b18>)
- Merge two tables together The axes, samples and observations, can be controlled independently. Both can work on either "union" or "intersection". sample_metadata_f and observation_metadata_f define how to merge metadata between tables. The default is to just keep the metadata associated to self if self has metadata otherwise take metadata from other. These functions are given both metadata dicts and must return a single metadata dict
- Parameters
- other : biom.Table
The other table to merge with this one
sample : {'union', 'intersection'}, optional observation : {'union', 'intersection'}, optional sample_metadata_f : function, optional
Defaults to biom.util.prefer_self. Defines how to
handle sample metadata during merge.
obesrvation_metadata_f : function, optional
Defaults to biom.util.prefer_self. Defines how to
handle observation metdata during merge.
- Returns
- biom.Table
The merged table
Notes.INDENT 7.0
- •
- There is an implicit type conversion to float.
- •
- The return type is always that of self
Examples.sp
>>> import numpy as np >>> from biom.table import TableCreate a 2x2 table and a 3x2 table:
>>> d_a = np.asarray([[2, 0], [6, 1]]) >>> t_a = Table(d_a, ['O1', 'O2'], ['S1', 'S2']) >>> d_b = np.asarray([[4, 5], [0, 3], [10, 10]]) >>> t_b = Table(d_b, ['O1', 'O2', 'O3'], ['S1', 'S2'])Merging the table results in the overlapping samples/observations (see O1 and S2) to be summed and the non-overlapping ones to be added to the resulting table (see S3).
>>> merged_table = t_a.merge(t_b) >>> print merged_table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S1 S2 O1 6.0 5.0 O2 6.0 4.0 O3 10.0 10.0
biom.table.Table.metadata¶
- Table.metadata(id=None, axis='sample')
- Return the metadata of the identified sample/observation.
- Parameters
- id : str
ID of the sample or observation whose index will be
returned.
axis : {'sample', 'observation'}
Axis to search for id.
- Returns
- defaultdict or None
The corresponding metadata defaultdict or
None of that axis does not have metadata.
- Raises
- UnknownAxisError
If provided an unrecognized axis.
UnknownIDError
If provided an unrecognized sample/observation ID.
Examples.sp
>>> import numpy as np >>> from biom.table import TableCreate a 2x3 BIOM table, with observation metadata and no sample metadata:
>>> data = np.asarray([[0, 0, 1], [1, 3, 42]])
>>> table = Table(data, ['O1', 'O2'], ['S1', 'S2', 'S3'],
... [{'foo': 'bar'}, {'x': 'y'}], None)
Get the metadata of the observation with ID "O2":
>>> # casting to `dict` as the return is `defaultdict`
>>> dict(table.metadata('O2', 'observation'))
{'x': 'y'}
Get the metadata of the sample with ID "S1":
>>> table.metadata('S1', 'sample') is None
True
biom.table.Table.min¶
- Table.min(axis='sample')
- Get the minimum nonzero value over an axis
- Parameters
- axis : {'sample', 'observation', 'whole'}, optional
Defaults to "sample". The axis over which to
calculate minima.
- Returns
- scalar of self.dtype or np.array of self.dtype
- Raises
- UnknownAxisError
If provided an unrecognized axis.
Examples.sp
>>> from biom import example_table >>> print example_table.min(axis='sample') [ 3. 1. 2.]
biom.table.Table.nonzero¶
- Table.nonzero()
- Yields locations of nonzero elements within the data matrix
- Returns
- generator
Yields (observation_id, sample_id) for each
nonzero element
biom.table.Table.nonzero_counts¶
- Table.nonzero_counts(axis, binary=False)
- Get nonzero summaries about an axis
- Parameters
- axis : {'sample', 'observation', 'whole'}
The axis on which to count nonzero entries
binary : bool, optional
Defaults to False. If False, return number
of nonzero entries. If True, sum the values of the entries.
- Returns
- numpy.array
Counts in index order to the axis
biom.table.Table.norm¶
- Table.norm(axis='sample', inplace=True)
- Normalize in place sample values by an observation, or vice versa.
- Parameters
- axis : {'sample', 'observation'}, optional
The axis to use for normalization
inplace : bool, optional
Defaults to True. If True, performs the
normalization in place. Otherwise, returns a new table with the noramlization
applied.
- Returns
- biom.Table
The normalized table
Examples.sp
>>> import numpy as np >>> from biom.table import TableCreate a 2x2 table:
>>> data = np.asarray([[2, 0], [6, 1]]) >>> table = Table(data, ['O1', 'O2'], ['S1', 'S2'])Get a version of the table normalized on the 'sample' axis, leaving the original table untouched:
>>> new_table = table.norm(inplace=False) >>> print table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S1 S2 O1 2.0 0.0 O2 6.0 1.0 >>> print new_table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S1 S2 O1 0.25 0.0 O2 0.75 1.0Get a version of the table normalized on the 'observation' axis, again leaving the original table untouched:
>>> new_table = table.norm(axis='observation', inplace=False) >>> print table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S1 S2 O1 2.0 0.0 O2 6.0 1.0 >>> print new_table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S1 S2 O1 1.0 0.0 O2 0.857142857143 0.142857142857Do the same normalization on 'observation', this time in-place:
>>> table.norm(axis='observation') 2 x 2 <class 'biom.table.Table'> with 3 nonzero entries (75% dense) >>> print table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S1 S2 O1 1.0 0.0 O2 0.857142857143 0.142857142857
biom.table.Table.pa¶
- Table.pa(inplace=True)
- Convert the table to presence/absence data
- Parameters
- inplace : bool, optional
Defaults to False
- Returns
- Table
Returns itself if inplace, else returns a new
presence/absence table.
Examples.sp
>>> from biom.table import Table >>> import numpy as npCreate a 2x3 BIOM table
>>> data = np.asarray([[0, 0, 1], [1, 3, 42]]) >>> table = table = Table(data, ['O1', 'O2'], ['S1', 'S2', 'S3'])Convert to presence/absence data
>>> _ = table.pa()
>>> print table.data('O1', 'observation')
[ 0. 0. 1.]
>>> print table.data('O2', 'observation')
[ 1. 1. 1.]
biom.table.Table.partition¶
- Table.partition(f, axis='sample')
- Yields partitions
- Parameters
- f : function
f is given the ID and metadata of the vector and
must return what partition the vector is part of.
axis : {'sample', 'observation'}, optional
The axis to iterate over
- Returns
- GeneratorType
A generator that yields (partition, Table)
Examples.sp
>>> import numpy as np >>> from biom.table import Table >>> from biom.util import unzipCreate a 2x3 BIOM table, with observation metadata and sample metadata:
>>> data = np.asarray([[0, 0, 1], [1, 3, 42]])
>>> table = Table(data, ['O1', 'O2'], ['S1', 'S2', 'S3'],
... [{'full_genome_available': True},
... {'full_genome_available': False}],
... [{'sample_type': 'a'}, {'sample_type': 'a'},
... {'sample_type': 'b'}])
Define a function to bin by sample_type
>>> f = lambda id_, md: md['sample_type']Partition the table and view results
>>> bins, tables = table.partition(f) >>> print bins[1] # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S1 S2 O1 0.0 0.0 O2 1.0 3.0 >>> print tables[1] # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S3 O1 1.0 O2 42.0
biom.table.Table.reduce¶
- Table.reduce(f, axis)
- Reduce over axis using function f
- Parameters
- f : function
The function to use for the reduce operation
axis : {'sample', 'observation'}
The axis on which to operate
- Returns
- numpy.array
A one-dimensional array representing the reduced rows
(observations) or columns (samples) of the data matrix
- Raises
- UnknownAxisError
If axis is neither "sample" nor
"observation"
TableException
If the table's data matrix is empty
Examples.sp
>>> import numpy as np >>> from biom.table import TableCreate a 2x3 table
>>> data = np.asarray([[0, 0, 1], [1, 3, 42]])
>>> table = Table(data, ['O1', 'O2'], ['S1', 'S2', 'S3'],
... [{'foo': 'bar'}, {'x': 'y'}], None)
Create a reduce function
>>> func = lambda x, y: x + yReduce table on samples
>>> table.reduce(func, 'sample') # doctest: +NORMALIZE_WHITESPACE array([ 1., 3., 43.])Reduce table on observations
>>> table.reduce(func, 'observation') # doctest: +NORMALIZE_WHITESPACE array([ 1., 46.])
biom.table.Table.sort¶
- Table.sort(sort_f=<function natsort at 0x7fe2a9d56aa0>, axis='sample')
- Return a table sorted along axis
- Parameters
- sort_f : function, optional
Defaults to biom.util.natsort. A function that
takes a list of values and sorts it
axis : {'sample', 'observation'}, optional
The axis to operate on
- Returns
- biom.Table
A table whose samples or observations are sorted
according to the sort_f function
Examples.sp
>>> import numpy as np >>> from biom.table import TableCreate a 2x3 BIOM table:
>>> data = np.asarray([[1, 0, 4], [1, 3, 0]]) >>> table = Table(data, ['O2', 'O1'], ['S2', 'S1', 'S3']) >>> print table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S2 S1 S3 O2 1.0 0.0 4.0 O1 1.0 3.0 0.0Sort the order of samples in the table using the default natural sorting:
>>> new_table = table.sort() >>> print new_table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S1 S2 S3 O2 0.0 1.0 4.0 O1 3.0 1.0 0.0Sort the order of observations in the table using the default natural sorting:
>>> new_table = table.sort(axis='observation') >>> print new_table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S2 S1 S3 O1 1.0 3.0 0.0 O2 1.0 0.0 4.0Sort the samples in reverse order using a custom sort function:
>>> sort_f = lambda x: list(sorted(x, reverse=True)) >>> new_table = table.sort(sort_f=sort_f) >>> print new_table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S3 S2 S1 O2 4.0 1.0 0.0 O1 0.0 1.0 3.0
biom.table.Table.sort_order¶
- Table.sort_order(order, axis='sample')
- Return a new table with axis in order
- Parameters
- order : iterable
The desired order for axis
axis : {'sample', 'observation'}, optional
The axis to operate on
- Returns
- Table
A table where the observations or samples are sorted
according to order
Examples.sp
>>> import numpy as np >>> from biom.table import TableCreate a 2x3 BIOM table:
>>> data = np.asarray([[1, 0, 4], [1, 3, 0]]) >>> table = Table(data, ['O2', 'O1'], ['S2', 'S1', 'S3']) >>> print table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S2 S1 S3 O2 1.0 0.0 4.0 O1 1.0 3.0 0.0Sort the table using a list of samples:
>>> sorted_table = table.sort_order(['S2', 'S3', 'S1']) >>> print sorted_table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S2 S3 S1 O2 1.0 4.0 0.0 O1 1.0 0.0 3.0Additionally you could sort the table's observations:
>>> sorted_table = table.sort_order(['O1', 'O2'], axis="observation") >>> print sorted_table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S2 S1 S3 O1 1.0 3.0 0.0 O2 1.0 0.0 4.0
biom.table.Table.subsample¶
- Table.subsample(n, axis='sample', by_id=False)
- Randomly subsample without replacement.
- Parameters
- n : int
Number of items to subsample from counts.
axis : {'sample', 'observation'}, optional
The axis to sample over
by_id : boolean, optional
If False, the subsampling is based on the counts
contained in the matrix (e.g., rarefaction). If True, the subsampling
is based on the IDs (e.g., fetch a random subset of samples). Default is
False.
- Returns
- biom.Table
A subsampled version of self
- Raises
- ValueError
If n is less than zero.
Notes Subsampling is performed without replacement. If n is greater than the sum of a given vector, that vector is omitted from the result. Adapted from skbio.math.subsample, see biom-format/licenses for more information about scikit-bio. This code assumes absolute abundance if by_id is False. Examples.sp
>>> import numpy as np >>> from biom.table import Table >>> table = Table(np.array([[0, 2, 3], [1, 0, 2]]), ['O1', 'O2'], ... ['S1', 'S2', 'S3'])Subsample 1 item over the sample axis by value (e.g., rarefaction):
>>> print table.subsample(1).sum(axis='sample') [ 1. 1. 1.]Subsample 2 items over the sample axis, note that 'S1' is filtered out:
>>> ss = table.subsample(2) >>> print ss.sum(axis='sample') [ 2. 2.] >>> print ss.ids() ['S2' 'S3']Subsample by IDs over the sample axis. For this example, we're going to randomly select 2 samples and do this 100 times, and then print out the set of IDs observed.
>>> ids = set([tuple(table.subsample(2, by_id=True).ids())
... for i in range(100)])
>>> print sorted(ids)
[('S1', 'S2'), ('S1', 'S3'), ('S2', 'S3')]
biom.table.Table.sum¶
- Table.sum(axis='whole')
- Returns the sum by axis
- Parameters
- axis : {'whole', 'sample', 'observation'}, optional
The axis on which to operate.
- Returns
- numpy.array or float
If axis is "whole", returns an float
representing the whole table sum. If axis is either "sample"
or "observation", returns a numpy.array that holds a sum for each
sample or observation, respectively.
Examples.sp
>>> import numpy as np >>> from biom.table import TableCreate a 2x3 BIOM table:
>>> data = np.asarray([[0, 0, 1], [1, 3, 42]]) >>> table = Table(data, ['O1', 'O2'], ['S1', 'S2', 'S3'])Add all values in the table:
>>> table.sum() array(47.0)Add all values per sample:
>>> table.sum(axis='sample') # doctest: +NORMALIZE_WHITESPACE array([ 1., 3., 43.])Add all values per observation:
>>> table.sum(axis='observation') # doctest: +NORMALIZE_WHITESPACE array([ 1., 46.])
biom.table.Table.to_hdf5¶
- Table.to_hdf5(h5grp, generated_by, compress=True)
- Store CSC and CSR in place The resulting structure of this group is below. A few basic definitions, N is the number of observations and M is the number of samples. Data are stored in both compressed sparse row [R10] (CSR, for observation oriented operations) and compressed sparse column [R11] (CSC, for sample oriented operations).
- Parameters
- h5grp : h5py.Group or h5py.File
The HDF5 entity in which to write the BIOM formatted
data.
generated_by : str
A description of what generated the table
compress : bool, optional
Defaults to True means fields will be compressed
with gzip, False means no compression
SEE ALSO:
Table.from_hdf5
Notes The expected HDF5 group structure is below. An example of an HDF5 file in DDL can be found here [R12].
- •
- ./id : str, an arbitrary ID
- •
- ./type : str, the table type (e.g, OTU table)
- •
- ./format-url : str, a URL that describes the format
- •
- ./format-version : two element tuple of int32, major and minor
- •
- ./generated-by : str, what generated this file
- •
- ./creation-date : str, ISO format
- •
- ./shape : two element tuple of int32, N by M
- •
- ./nnz : int32 or int64, number of non zero elems
- •
- ./observation : Group
- •
- ./observation/ids : (N,) dataset of str or vlen str
- •
- ./observation/matrix : Group
- •
- ./observation/matrix/data : (nnz,) dataset of float64
- •
- ./observation/matrix/indices : (nnz,) dataset of int32
- •
- ./observation/matrix/indptr : (M+1,) dataset of int32
- •
- ./observation/metadata : Group
- •
- [./observation/metadata/foo] : Optional, (N,) dataset of any valid HDF5 type in index order with IDs.
- •
- ./observation/group-metadata : Group
- •
- [./observation/group-metadata/foo] : Optional, (?,) dataset of group metadata that relates IDs
- •
- [./observation/group-metadata/foo.attrs['data_type']] : attribute of the foo dataset that describes contained type (e.g., newick)
- •
- ./sample : Group
- •
- ./sample/ids : (M,) dataset of str or vlen str
- •
- ./sample/matrix : Group
- •
- ./sample/matrix/data : (nnz,) dataset of float64
- •
- ./sample/matrix/indices : (nnz,) dataset of int32
- •
- ./sample/matrix/indptr : (N+1,) dataset of int32
- •
- ./sample/metadata : Group
- •
- [./sample/metadata/foo] : Optional, (M,) dataset of any valid HDF5 type in index order with IDs.
- •
- ./sample/group-metadata : Group
- •
- [./sample/group-metadata/foo] : Optional, (?,) dataset of group metadata that relates IDs
- •
- [./sample/group-metadata/foo.attrs['data_type']] : attribute of the foo dataset that describes contained type (e.g., newick)
The '?' character on the dataset size means that it can be of arbitrary length. The expected structure for each of the metadata datasets is a list of atomic type objects (int, float, str, ...), where the index order of the list corresponds to the index order of the relevant axis IDs. Special metadata fields have been defined, and they are stored in a specific way. Currently, the available special metadata fields are:
- •
- taxonomy: (N, ?) dataset of str or vlen str
- •
- KEGG_Pathways: (N, ?) dataset of str or vlen str
- •
- collapsed_ids: (N, ?) dataset of str or vlen str
References.IP [R10] 5 http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.sparse.csr_matrix.html
- [R12]
- http://biom-format.org/documentation/format_versions/biom-2.0.html
Examples.sp
>>> from biom.util import biom_open # doctest: +SKIP >>> from biom.table import Table >>> from numpy import array >>> t = Table(array([[1, 2], [3, 4]]), ['a', 'b'], ['x', 'y']) >>> with biom_open('foo.biom', 'w') as f: # doctest: +SKIP ... t.to_hdf5(f, "example")
biom.table.Table.to_json¶
- Table.to_json(generated_by, direct_io=None)
- Returns a JSON string representing the table in BIOM format.
- Parameters
- generated_by : str
a string describing the software used to build the
table
direct_io : file or file-like object, optional
Defaults to None. Must implementing a write
function. If direct_io is not None, the final output is written
directly to direct_io during processing.
- Returns
- str
A JSON-formatted string representing the biom table
biom.table.Table.to_tsv¶
- Table.to_tsv(header_key=None, header_value=None, metadata_formatter=<type 'str'>, observation_column_name='#OTU ID')
- Return self as a string in tab delimited form Default str output for the Table is just row/col ids and table data without any metadata
- Parameters
- header_key : str or None, optional
Defaults to None
header_value : str or None, optional
Defaults to None
metadata_formatter : function, optional
Defaults to str. a function which takes a metadata
entry and returns a formatted version that should be written to file
observation_column_name : str, optional
Defaults to "#OTU ID". The name of the first
column in the output table, corresponding to the observation IDs.
- Returns
- str
tab delimited representation of the Table
Examples.sp
>>> import numpy as np >>> from biom.table import TableCreate a 2x3 BIOM table, with observation metadata and no sample metadata:
>>> data = np.asarray([[0, 0, 1], [1, 3, 42]])
>>> table = Table(data, ['O1', 'O2'], ['S1', 'S2', 'S3'],
... [{'foo': 'bar'}, {'x': 'y'}], None)
>>> print table.to_tsv() # doctest: +NORMALIZE_WHITESPACE
# Constructed from biom file
#OTU ID S1 S2 S3
O1 0.0 0.0 1.0
O2 1.0 3.0 42.0
biom.table.Table.transform¶
- Table.transform(f, axis='sample', inplace=True)
- Iterate over axis, applying a function f to each vector. Only non null values can be modified and the density of the table can't increase. However, zeroing values is fine.
- Parameters
- f : function(data, id, metadata) -> new data
A function that takes three values: an array of nonzero
values corresponding to each observation or sample, an observation or sample
id, and an observation or sample metadata entry. It must return an array of
transformed values that replace the original values.
axis : {'sample', 'observation'}, optional
The axis to operate on. Can be "sample" or
"observation".
inplace : bool, optional
Defaults to True. Whether to return a new table or
modify itself.
- Returns
- biom.Table
Returns itself if inplace, else returns a new
transformed table.
- Raises
- UnknownAxisError
If provided an unrecognized axis.
Examples.sp
>>> import numpy as np >>> from biom.table import TableCreate a 2x3 table
>>> data = np.asarray([[0, 0, 1], [1, 3, 42]])
>>> table = Table(data, ['O1', 'O2'], ['S1', 'S2', 'S3'],
... [{'foo': 'bar'}, {'x': 'y'}], None)
>>> print table # doctest: +NORMALIZE_WHITESPACE
# Constructed from biom file
#OTU ID S1 S2 S3
O1 0.0 0.0 1.0
O2 1.0 3.0 42.0
Create a transform function
>>> f = lambda data, id_, md: data / 2Transform to a new table on samples
>>> table2 = table.transform(f, 'sample', False) >>> print table2 # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S1 S2 S3 O1 0.0 0.0 0.5 O2 0.5 1.5 21.0table hasn't changed
>>> print table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S1 S2 S3 O1 0.0 0.0 1.0 O2 1.0 3.0 42.0Tranform in place on observations
>>> table3 = table.transform(f, 'observation', True)table is different now
>>> print table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S1 S2 S3 O1 0.0 0.0 0.5 O2 0.5 1.5 21.0but the table returned ( table3) is the same as table
>>> print table3 # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S1 S2 S3 O1 0.0 0.0 0.5 O2 0.5 1.5 21.0
biom.table.Table.transpose¶
- Table.transpose()
- Transpose the contingency table The returned table will be an entirely new table, including copies of the (transposed) data, sample/observation IDs and metadata.
- Returns
- Table
Return a new table that is the transpose of caller
table.
Examples¶
First, lets create a toy table to play around with. For this example, we're going to construct a 10x4 Table, or one that has 10 observations and 4 samples. Each observation and sample will be given an arbitrary but unique name. We'll also add on some metadata.>>> import numpy as np
>>> from biom.table import Table
>>> data = np.arange(40).reshape(10, 4)
>>> sample_ids = ['S%d' % i for i in range(4)]
>>> observ_ids = ['O%d' % i for i in range(10)]
>>> sample_metadata = [{'environment': 'A'}, {'environment': 'B'},
... {'environment': 'A'}, {'environment': 'B'}]
>>> observ_metadata = [{'taxonomy': ['Bacteria', 'Firmicutes']},
... {'taxonomy': ['Bacteria', 'Firmicutes']},
... {'taxonomy': ['Bacteria', 'Proteobacteria']},
... {'taxonomy': ['Bacteria', 'Proteobacteria']},
... {'taxonomy': ['Bacteria', 'Proteobacteria']},
... {'taxonomy': ['Bacteria', 'Bacteroidetes']},
... {'taxonomy': ['Bacteria', 'Bacteroidetes']},
... {'taxonomy': ['Bacteria', 'Firmicutes']},
... {'taxonomy': ['Bacteria', 'Firmicutes']},
... {'taxonomy': ['Bacteria', 'Firmicutes']}]
>>> table = Table(data, observ_ids, sample_ids, observ_metadata,
... sample_metadata, table_id='Example Table')
Now that we have a table, let's explore it at a high level first.
>>> table 10 x 4 <class 'biom.table.Table'> with 39 nonzero entries (97% dense) >>> print table # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S0 S1 S2 S3 O0 0.0 1.0 2.0 3.0 O1 4.0 5.0 6.0 7.0 O2 8.0 9.0 10.0 11.0 O3 12.0 13.0 14.0 15.0 O4 16.0 17.0 18.0 19.0 O5 20.0 21.0 22.0 23.0 O6 24.0 25.0 26.0 27.0 O7 28.0 29.0 30.0 31.0 O8 32.0 33.0 34.0 35.0 O9 36.0 37.0 38.0 39.0 >>> print table.ids() # doctest: +NORMALIZE_WHITESPACE ['S0' 'S1' 'S2' 'S3'] >>> print table.ids(axis='observation') # doctest: +NORMALIZE_WHITESPACE ['O0' 'O1' 'O2' 'O3' 'O4' 'O5' 'O6' 'O7' 'O8' 'O9'] >>> print table.nnz # number of nonzero entries 39While it's fun to just poke at the table, let's dig deeper. First, we're going to convert table into relative abundances (within each sample), and then filter table to just the samples associated with environment 'A'. The filtering gets fancy: we can pass in an arbitrary function to determine what samples we want to keep. This function must accept a sparse vector of values, the corresponding ID and the corresponding metadata, and should return True or False, where True indicates that the vector should be retained.
>>> normed = table.norm(axis='sample', inplace=False) >>> filter_f = lambda values, id_, md: md['environment'] == 'A' >>> env_a = normed.filter(filter_f, axis='sample', inplace=False) >>> print env_a # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S0 S2 O0 0.0 0.01 O1 0.0222222222222 0.03 O2 0.0444444444444 0.05 O3 0.0666666666667 0.07 O4 0.0888888888889 0.09 O5 0.111111111111 0.11 O6 0.133333333333 0.13 O7 0.155555555556 0.15 O8 0.177777777778 0.17 O9 0.2 0.19But, what if we wanted individual tables per environment? While we could just perform some fancy iteration, we can instead just rely on Table.partition for these operations. partition, like filter, accepts a function. However, the partition method only passes the corresponding ID and metadata to the function. The function should return what partition the data are a part of. Within this example, we're also going to sum up our tables over the partitioned samples. Please note that we're using the original table (ie, not normalized) here.
>>> part_f = lambda id_, md: md['environment']
>>> env_tables = table.partition(part_f, axis='sample')
>>> for partition, env_table in env_tables:
... print partition, env_table.sum('sample')
A [ 180. 200.]
B [ 190. 210.]
For this last example, and to highlight a bit more functionality, we're going to
first transform the table such that all multiples of three will be retained,
while all non-multiples of three will get set to zero. Following this, we'll
then collpase the table by taxonomy, and then convert the table into
presence/absence data.
First, let's setup the transform. We're going to define a function that takes
the modulus of every value in the vector, and see if it is equal to zero. If
it is equal to zero, we'll keep the value, otherwise we'll set the value to
zero.
>>> transform_f = lambda v,i,m: np.where(v % 3 == 0, v, 0) >>> mult_of_three = tform = table.transform(transform_f, inplace=False) >>> print mult_of_three # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S0 S1 S2 S3 O0 0.0 0.0 0.0 3.0 O1 0.0 0.0 6.0 0.0 O2 0.0 9.0 0.0 0.0 O3 12.0 0.0 0.0 15.0 O4 0.0 0.0 18.0 0.0 O5 0.0 21.0 0.0 0.0 O6 24.0 0.0 0.0 27.0 O7 0.0 0.0 30.0 0.0 O8 0.0 33.0 0.0 0.0 O9 36.0 0.0 0.0 39.0Next, we're going to collapse the table over the phylum level taxon. To do this, we're going to define a helper variable for the index position of the phylum (see the construction of the table above). Next, we're going to pass this to Table.collapse, and since we want to collapse over the observations, we'll need to specify 'observation' as the axis.
>>> phylum_idx = 1 >>> collapse_f = lambda id_, md: '; '.join(md['taxonomy'][:phylum_idx + 1]) >>> collapsed = mult_of_three.collapse(collapse_f, axis='observation') >>> print collapsed # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S0 S1 S2 S3 Bacteria; Firmicutes 7.2 6.6 7.2 8.4 Bacteria; Bacteroidetes 12.0 10.5 0.0 13.5 Bacteria; Proteobacteria 4.0 3.0 6.0 5.0Finally, let's convert the table to presence/absence data.
>>> pa = collapsed.pa() >>> print pa # doctest: +NORMALIZE_WHITESPACE # Constructed from biom file #OTU ID S0 S1 S2 S3 Bacteria; Firmicutes 1.0 1.0 1.0 1.0 Bacteria; Bacteroidetes 1.0 1.0 0.0 1.0 Bacteria; Proteobacteria 1.0 1.0 1.0 1.0
Converting between file formats¶
- The convert command in the biom-format project can be used to convert between biom and tab-delimited table formats. This is useful for several reasons:
- •
- converting biom format tables to tab-delimited tables for easy viewing in programs such as Excel
- •
- converting between sparse and dense biom formats
NOTE:
The tab-delimited tables are commonly referred to as the
classic format tables, while BIOM formatted tables are referred to as
biom tables.
General usage examples¶
Convert a tab-delimited table to a HDF5 or JSON biom format. Note that you must specify the type of table here:biom convert -i table.txt -o table.from_txt_json.biom --table-type="OTU table" --to-json biom convert -i table.txt -o table.from_txt_hdf5.biom --table-type="OTU table" --to-hdf5
Convert biom format to tab-delimited table format:
biom convert -i table.biom -o table.from_biom.txt --to-tsv
Convert biom format to classic format, including the taxonomy observation metadata as the last column of the classic format table. Because the BIOM format can support an arbitrary number of observation (or sample) metadata entries, and the classic format can support only a single observation metadata entry, you must specify which of the observation metadata entries you want to include in the output table:
biom convert -i table.biom -o table.from_biom_w_taxonomy.txt --to-tsv --header-key taxonomy
Convert biom format to classic format, including the taxonomy observation metadata as the last column of the classic format table, but renaming that column as ConsensusLineage. This is useful when using legacy tools that require a specific name for the observation metadata column.:
biom convert -i table.biom -o table.from_biom_w_consensuslineage.txt --to-tsv --header-key taxonomy --output-metadata-id "ConsensusLineage"
Special case usage examples¶
Round-tripping between biom and tsv¶
In specific cases, see this comment, it is still useful to convert our biom table to tsv so we can open in Excel, make some changes to the file and then convert back to biom. For this cases you should follow this steps:- •
- Convert from biom to txt:
biom convert -i otu_table.biom -o otu_table.txt --to-tsv --header-key taxonomy
- •
- Make your changes in Excel.
- •
- Convert back to biom:
biom convert -i otu_table.txt -o new_otu_table.biom --to-hdf5 --table-type="OTU table" --process-obs-metadata taxonomy
Converting QIIME 1.4.0 and earlier OTU tables to BIOM format¶
If you are converting a QIIME 1.4.0 or earlier OTU table to BIOM format, there are a few steps to go through. First, for convenience, you might want to rename the ConsensusLineage column taxonomy. You can do this with the following command:sed 's/Consensus Lineage/ConsensusLineage/' < otu_table.txt | sed 's/ConsensusLineage/taxonomy/' > otu_table.taxonomy.txt
Then, you'll want to perform the conversion including a step to convert the taxonomy string from the classic OTU table to a taxonomy list, as it's represented in QIIME 1.4.0-dev and later:
biom convert -i otu_table.taxonomy.txt -o otu_table.from_txt.biom --table-type="OTU table" --process-obs-metadata taxonomy
Adding sample and observation metadata to biom files¶
Frequently you'll have an existing BIOM file and want to add sample and/or observation metadata to it. For samples, metadata is frequently environmental or technical details about your samples: the subject that a sample was collected from, the pH of the sample, the PCR primers used to amplify DNA from the samples, etc. For observations, metadata is frequently a categorization of the observation: the taxonomy of an OTU, or the EC hierarchy of a gene. You can use the biom add-metadata command to add this information to an existing BIOM file. To get help with add-metadata you can call:biom add-metadata -h
This command takes a BIOM file, and corresponding sample and/or observation mapping files. The following examples are used in the commands below. You can find these files in the biom-format/examples directory. Your BIOM file might look like the following:
{
"id":null,
"format": "1.0.0",
"format_url": "http://biom-format.org",
"type": "OTU table",
"generated_by": "some software package",
"date": "2011-12-19T19:00:00",
"rows":[
{"id":"GG_OTU_1", "metadata":null},
{"id":"GG_OTU_2", "metadata":null},
{"id":"GG_OTU_3", "metadata":null},
{"id":"GG_OTU_4", "metadata":null},
{"id":"GG_OTU_5", "metadata":null}
],
"columns": [
{"id":"Sample1", "metadata":null},
{"id":"Sample2", "metadata":null},
{"id":"Sample3", "metadata":null},
{"id":"Sample4", "metadata":null},
{"id":"Sample5", "metadata":null},
{"id":"Sample6", "metadata":null}
],
"matrix_type": "sparse",
"matrix_element_type": "int",
"shape": [5, 6],
"data":[[0,2,1],
[1,0,5],
[1,1,1],
[1,3,2],
[1,4,3],
[1,5,1],
[2,2,1],
[2,3,4],
[2,5,2],
[3,0,2],
[3,1,1],
[3,2,1],
[3,5,1],
[4,1,1],
[4,2,1]
]
}
A sample metadata mapping file could then look like the following. Notice that there is an extra sample in here with respect to the above BIOM table. Any samples in the mapping file that are not in the BIOM file are ignored.
#SampleID BarcodeSequence DOB # Some optional # comment lines... Sample1 AGCACGAGCCTA 20060805 Sample2 AACTCGTCGATG 20060216 Sample3 ACAGACCACTCA 20060109 Sample4 ACCAGCGACTAG 20070530 Sample5 AGCAGCACTTGT 20070101 Sample6 AGCAGCACAACT 20070716
An observation metadata mapping file might look like the following. Notice that there is an extra observation in here with respect to the above BIOM table. Any observations in the mapping file that are not in the BIOM file are ignored.
#OTUID taxonomy confidence # Some optional # comment lines GG_OTU_0 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__ 0.980 GG_OTU_1 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae 0.665 GG_OTU_2 Root;k__Bacteria 0.980 GG_OTU_3 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae 1.000 GG_OTU_4 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae 0.842 GG_OTU_5 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae 1.000
Adding metadata¶
To add sample metadata to a BIOM file, you can run the following:biom add-metadata -i min_sparse_otu_table.biom -o table.w_smd.biom --sample-metadata-fp sam_md.txt
To add observation metadata to a BIOM file, you can run the following:
biom add-metadata -i min_sparse_otu_table.biom -o table.w_omd.biom --observation-metadata-fp obs_md.txt
You can also combine these in a single command to add both observation and sample metadata:
biom add-metadata -i min_sparse_otu_table.biom -o table.w_md.biom --observation-metadata-fp obs_md.txt --sample-metadata-fp sam_md.txt
In the last case, the resulting BIOM file will look like the following:
{
"columns": [
{
"id": "Sample1",
"metadata": {
"BarcodeSequence": "AGCACGAGCCTA",
"DOB": "20060805"
}
},
{
"id": "Sample2",
"metadata": {
"BarcodeSequence": "AACTCGTCGATG",
"DOB": "20060216"
}
},
{
"id": "Sample3",
"metadata": {
"BarcodeSequence": "ACAGACCACTCA",
"DOB": "20060109"
}
},
{
"id": "Sample4",
"metadata": {
"BarcodeSequence": "ACCAGCGACTAG",
"DOB": "20070530"
}
},
{
"id": "Sample5",
"metadata": {
"BarcodeSequence": "AGCAGCACTTGT",
"DOB": "20070101"
}
},
{
"id": "Sample6",
"metadata": {
"BarcodeSequence": "AGCAGCACAACT",
"DOB": "20070716"
}
}
],
"data": [
[0, 2, 1.0],
[1, 0, 5.0],
[1, 1, 1.0],
[1, 3, 2.0],
[1, 4, 3.0],
[1, 5, 1.0],
[2, 2, 1.0],
[2, 3, 4.0],
[2, 5, 2.0],
[3, 0, 2.0],
[3, 1, 1.0],
[3, 2, 1.0],
[3, 5, 1.0],
[4, 1, 1.0],
[4, 2, 1.0]
],
"date": "2012-12-11T07:36:15.467843",
"format": "Biological Observation Matrix 1.0.0",
"format_url": "http://biom-format.org",
"generated_by": "some software package",
"id": null,
"matrix_element_type": "float",
"matrix_type": "sparse",
"rows": [
{
"id": "GG_OTU_1",
"metadata": {
"confidence": "0.665",
"taxonomy": "Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae"
}
},
{
"id": "GG_OTU_2",
"metadata": {
"confidence": "0.980",
"taxonomy": "Root;k__Bacteria"
}
},
{
"id": "GG_OTU_3",
"metadata": {
"confidence": "1.000",
"taxonomy": "Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae"
}
},
{
"id": "GG_OTU_4",
"metadata": {
"confidence": "0.842",
"taxonomy": "Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae"
}
},
{
"id": "GG_OTU_5",
"metadata": {
"confidence": "1.000",
"taxonomy": "Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae"
}
}
],
"shape": [5, 6],
"type": "OTU table"
}
Processing metadata while adding¶
There are some additional parameters you can pass to this command for more complex processing. You can tell the command to process certain metadata column values as integers ( --int-fields), floating point (i.e., decimal or real) numbers (--float-fields), or as hierarchical semicolon-delimited data ( --sc-separated).biom add-metadata -i min_sparse_otu_table.biom -o table.w_md.biom --observation-metadata-fp obs_md.txt --sample-metadata-fp sam_md.txt --int-fields DOB --sc-separated taxonomy --float-fields confidence
Here your resulting BIOM file will look like the following, where DOB values are now integers (compare to the above: they're not quoted now), confidence values are now floating point numbers (again, not quoted now), and taxonomy values are now lists where each entry is a taxonomy level, opposed to above where they appear as a single semi-colon-separated string.
{
"columns": [
{
"id": "Sample1",
"metadata": {
"BarcodeSequence": "AGCACGAGCCTA",
"DOB": 20060805
}
},
{
"id": "Sample2",
"metadata": {
"BarcodeSequence": "AACTCGTCGATG",
"DOB": 20060216
}
},
{
"id": "Sample3",
"metadata": {
"BarcodeSequence": "ACAGACCACTCA",
"DOB": 20060109
}
},
{
"id": "Sample4",
"metadata": {
"BarcodeSequence": "ACCAGCGACTAG",
"DOB": 20070530
}
},
{
"id": "Sample5",
"metadata": {
"BarcodeSequence": "AGCAGCACTTGT",
"DOB": 20070101
}
},
{
"id": "Sample6",
"metadata": {
"BarcodeSequence": "AGCAGCACAACT",
"DOB": 20070716
}
}
],
"data": [
[0, 2, 1.0],
[1, 0, 5.0],
[1, 1, 1.0],
[1, 3, 2.0],
[1, 4, 3.0],
[1, 5, 1.0],
[2, 2, 1.0],
[2, 3, 4.0],
[2, 5, 2.0],
[3, 0, 2.0],
[3, 1, 1.0],
[3, 2, 1.0],
[3, 5, 1.0],
[4, 1, 1.0],
[4, 2, 1.0]
],
"date": "2012-12-11T07:30:29.870689",
"format": "Biological Observation Matrix 1.0.0",
"format_url": "http://biom-format.org",
"generated_by": "some software package",
"id": null,
"matrix_element_type": "float",
"matrix_type": "sparse",
"rows": [
{
"id": "GG_OTU_1",
"metadata": {
"confidence": 0.665,
"taxonomy": ["Root", "k__Bacteria", "p__Firmicutes", "c__Clostridia", "o__Clostridiales", "f__Lachnospiraceae"]
}
},
{
"id": "GG_OTU_2",
"metadata": {
"confidence": 0.98,
"taxonomy": ["Root", "k__Bacteria"]
}
},
{
"id": "GG_OTU_3",
"metadata": {
"confidence": 1.0,
"taxonomy": ["Root", "k__Bacteria", "p__Firmicutes", "c__Clostridia", "o__Clostridiales", "f__Lachnospiraceae"]
}
},
{
"id": "GG_OTU_4",
"metadata": {
"confidence": 0.842,
"taxonomy": ["Root", "k__Bacteria", "p__Firmicutes", "c__Clostridia", "o__Clostridiales", "f__Lachnospiraceae"]
}
},
{
"id": "GG_OTU_5",
"metadata": {
"confidence": 1.0,
"taxonomy": ["Root", "k__Bacteria", "p__Firmicutes", "c__Clostridia", "o__Clostridiales", "f__Lachnospiraceae"]
}
}
],
"shape": [5, 6],
"type": "OTU table"
}
If you have multiple fields that you'd like processed in one of these ways, you can pass a comma-separated list of field names (e.g., --float-fields confidence,pH).
Renaming (or naming) metadata columns while adding¶
You can also override the names of the metadata fields provided in the mapping files with the --observation-header and --sample-header parameters. This is useful if you want to rename metadata columns, or if metadata column headers aren't present in your metadata mapping file. If you pass either of these parameters, you must name all columns in order. If there are more columns in the metadata mapping file then there are headers, extra columns will be ignored (so this is also a useful way to select only the first n columns from your mapping file). For example, if you want to rename the DOB column in the sample metadata mapping you could do the following:biom add-metadata -i min_sparse_otu_table.biom -o table.w_smd.biom --sample-metadata-fp sam_md.txt --sample-header SampleID,BarcodeSequence,DateOfBirth
If you have a mapping file without headers such as the following:
GG_OTU_0 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__ 0.980 GG_OTU_1 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae 0.665 GG_OTU_2 Root;k__Bacteria 0.980 GG_OTU_3 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae 1.000 GG_OTU_4 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae 0.842 GG_OTU_5 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae 1.000
you could name these while adding them as follows:
biom add-metadata -i min_sparse_otu_table.biom -o table.w_omd.biom --observation-metadata-fp obs_md.txt --observation-header OTUID,taxonomy,confidence
As a variation on the last command, if you only want to include the taxonomy column and exclude the confidence column, you could run:
biom add-metadata -i min_sparse_otu_table.biom -o table.w_omd.biom --observation-metadata-fp obs_md.txt --observation-header OTUID,taxonomy
Summarizing BIOM tables¶
If you have an existing BIOM file and want to compile a summary of the information in that table, you can use the biom summarize-table command. To get help with biom summarize-table you can call:biom summarize-table -h
This command takes a BIOM file or gzipped BIOM file as input, and will print a summary of the count information on a per-sample basis to the new file specified by the -o parameter. The example file used in the commands below can be found in the biom-format/examples directory.
Summarizing sample data¶
To summarize the per-sample data in a BIOM file, you can run:biom summarize-table -i rich_sparse_otu_table.biom -o rich_sparse_otu_table_summary.txt
The following information will be written to rich_sparse_otu_table_summary.txt:
Num samples: 6 Num observations: 5 Total count: 27 Table density (fraction of non-zero values): 0.500 Counts/sample summary: Min: 3.0 Max: 7.0 Median: 4.000 Mean: 4.500 Std. dev.: 1.500 Sample Metadata Categories: LinkerPrimerSequence; BarcodeSequence; Description; BODY_SITE Observation Metadata Categories: taxonomy Counts/sample detail: Sample5: 3.0 Sample2: 3.0 Sample6: 4.0 Sample3: 4.0 Sample4: 6.0 Sample1: 7.0
As you can see, general summary information about the table is provided, including the number of samples, the number of observations, the total count (i.e., the sum of all values in the table), and so on, followed by the per-sample counts.
Summarizing sample data qualitatively¶
To summarize the per-sample data in a BIOM file qualitatively, where the number of unique observations per sample (rather than the total count of observations per sample) are provided, you can run:biom summarize-table -i rich_sparse_otu_table.biom --qualitative -o rich_sparse_otu_table_qual_summary.txt
The following information will be written to rich_sparse_otu_table_qual_summary.txt:
Num samples: 6 Num observations: 5 Observations/sample summary: Min: 1 Max: 4 Median: 2.500 Mean: 2.500 Std. dev.: 0.957 Sample Metadata Categories: LinkerPrimerSequence; BarcodeSequence; Description; BODY_SITE Observation Metadata Categories: taxonomy Observations/sample detail: Sample5: 1 Sample4: 2 Sample1: 2 Sample6: 3 Sample2: 3
THE BIOM FORMAT LICENSE¶
The BIOM Format project is licensed under the terms of the Modified BSD
License (also known as New or Revised BSD), as follows:
Copyright (c) 2011-2014, The BIOM Format Development Team <gregcaporaso@gmail.com>
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of the BIOM Format Development Team nor the names of its
contributors may be used to endorse or promote products derived from this
software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE BIOM FORMAT DEVELOPMENT TEAM BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The following banner should be used in any source code file to indicate the
copyright and license terms:
#-----------------------------------------------------------------------------
# Copyright (c) 2011-2014, The BIOM Format Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
The latest official version of the biom-format project is 2.1 and of the BIOM file format is 2.0. Details on the file format can be found here. To install the biom-format project, you can download the latest version here, or work with the development version. Generally we recommend working with the release version as it will be more stable, but if you want access to the latest features (and can tolerate some instability) you should work with the development version. The biom-format project has the following dependencies:
- •
- Python >= 2.7 and < 3.0
- •
- numpy >= 1.7.0
- •
- pyqi 0.3.2
- •
- scipy >= 0.13.0
- •
- h5py >= 2.20.0 (optional; must be installed if creating or reading HDF5 formatted files)
The easiest way to install the latest version of the biom-format project and its required dependencies is via pip:
pip install numpy pip install biom-format
That's it! If you decided not to install biom-format using pip, it is also possible to manually install the latest release. We'll illustrate the install process in the $HOME/code directory. You can either work in this directory on your system (creating it, if necessary, by running mkdir $HOME/code) or replace all occurrences of $HOME/code in the following instructions with your working directory. Please note that numpy must be in your installed prior to installing biom-format. Change to this directory to start the install process:
cd $HOME/code
Download the latest release, which can be found here. After downloading, unpack and install (note: x.y.z refers to the downloaded version):
tar xzf biom-format-x.y.z.tar.gz cd $HOME/code/biom-format-x.y.z
Alternatively, to install the development version, pull it from GitHub, and change to the resulting directory:
git clone git://github.com/biocore/biom-format.git cd $HOME/code/biom-format
To install (either the development or release version), follow these steps:
sudo python setup.py install
If you do not have sudo access on your system (or don't want to install the biom-format project in the default location) you'll need to install the library code and scripts in specified directories, and then tell your system where to look for those files. You can do this as follows:
echo "export PATH=$HOME/bin/:$PATH" >> $HOME/.bashrc echo "export PYTHONPATH=$HOME/lib/:$PYTHONPATH" >> $HOME/.bashrc mkdir -p $HOME/bin $HOME/lib/ source $HOME/.bashrc python setup.py install --install-scripts=$HOME/bin/ --install-purelib=$HOME/lib/ --install-lib=$HOME/lib/
You should then have access to the biom-format project. You can test this by running the following command:
python -c "from biom import __version__; print __version__"
You should see the current version of the biom-format project. Next you can run:
which biom
You should get a file path ending with biom printed to your screen if it is installed correctly. Finally, to see a list of all biom commands, run:
biom
ENABLING TAB COMPLETION OF BIOM COMMANDS¶
The biom command referenced in the previous section is a driver for commands in biom-format, powered by the pyqi project. You can enable tab completion of biom command names and command options (meaning that when you begin typing the name of a command or option you can auto-complete it by hitting the tab key) by following a few simple steps from the pyqi documentation. While this step is optional, tab completion is very convenient so it's worth enabling. To enable tab completion, follow the steps outlined under Configuring bash completion in the pyqi install documentation, substituting biom for my-project and my_project in all commands. After completing those steps and closing and re-opening your terminal, auto-completion should be enabled. There is also a BIOM format package for R, called biom. This package includes basic tools for reading biom-format files, accessing and subsetting data tables from a biom object, as well as limited support for writing a biom-object back to a biom-format file. The design of this API is intended to match the python API and other tools included with the biom-format project, but with a decidedly "R flavor" that should be familiar to R users. This includes S4 classes and methods, as well as extensions of common core functions/methods. To install the latest stable release of the biom package enter the following command from within an R session:install.packages("biom")
To install the latest development version of the biom package, enter the following lines in an R session:
install.packages("devtools") # if not already installed
library("devtools")
install_github("biom", "joey711")
Please post any support or feature requests and bugs to the biom issue tracker. See the biom project on GitHub for further details, or if you would like to contribute. Note that the licenses between the biom R package (GPL-2) and the other biom-format software (Modified BSD) are different. You can cite the BIOM format as follows ( link):
The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome. Daniel McDonald, Jose C. Clemente, Justin Kuczynski, Jai Ram Rideout, Jesse Stombaugh, Doug Wendel, Andreas Wilke, Susan Huse, John Hufnagle, Folker Meyer, Rob Knight, and J. Gregory Caporaso. GigaScience 2012, 1:7. doi:10.1186/2047-217X-1-7The biom-format project was conceived of and developed by the QIIME, MG-RAST, and VAMPS development groups to support interoperability of our software packages. If you have questions about the biom-format project you can contact gregcaporaso@gmail.com.
AUTHOR¶
The BIOM ProjectCOPYRIGHT¶
2011-2013, The BIOM Format Development Team| August 12, 2014 | 2.1 |