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Description:

Collapse samples in a BIOM table and mapping file. Values in the BIOM table are collapsed in one of several different ways; see the available options for –collapse_mode. Values in the mapping file are collapsed by grouping the values if they differ for the grouped samples, and by providing the single value if they don’t differ for the grouped samples.

Usage: Soft Strap Posen Navy Handle Iconic Eartha Zac Floral Coral Top ZAC collapse_samples.py [options]

Input Arguments:

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[REQUIRED]

-b, -Dark Kit Shave Brown Shave Fossil Kit Travel wBSXXzItalian Fiorentina Black Embossed Pochette Tooled Leather Cuoieria 8pYqw5d8-input_biom_fp
The biom table containing the samples to be collapsed
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-m, --mapping_fp
The sample metdata mapping file
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--output_biom_fp
Path where collapsed biom table should be written
--output_mapping_fp
Path where collapsed mapping file should be written
--collapse_fields
Comma-separated list of fields to collapse on

[OPTIONAL]

--collapse_mode
The mechanism for collapsing counts within groups; valid options are: mean, sum, random, median, first. [default: sum]
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--normalize
Normalize observation counts to relative abundances, so the counts within each sample sum to 1.0. [default: False]

Output:

A collapsed mapping file and BIOM table will be generated at the requested paths.

Collapse samples in biom table and mapping file:

Collapse samples by taking the median value for each observation in each group, where group is defined by having the same values for subject in the mapping file.

Collapse samples in biom table and mapping file:

Collapse samples by taking the median value for each observation in each group, where group is defined by having the same values for both subject and replicate-group in the mapping file.

collapse_samples.py -b table.biom -m map.txt --output_biom_fp collapsed.biom --output_mapping_fp collapsed_map.txt --collapse_mode median --collapse_fields replicate-group,subject

Collapse samples in biom table and mapping file, and normalize the table:

Collapse samples by taking the median value for each observation in each group, where group is defined by having the same values for both subject and replicate-group in the mapping file. Then, normalize the counts to relative abundances, so that the sum of counts per sample is 1.0.

collapse_samples.py -b table.biom -m map.txt --output_biom_fp collapsed-normed.biom --output_mapping_fp collapsed_map.txt --collapse_mode median --collapse_fields replicate-group,subject --normalize