Description

Perform logratio-based correlation analysis -> get proportionality & basis shrinkage partial correlation coefficients. One can also compute standard correlation coefficients, if required.

Input

name:type
description
pattern

meta:map

Groovy Map containing sample information. This can be used at the workflow level to pass optional parameters to the module. [id: ‘test’, …]

count:file

Count matrix, where rows = variables or genes, columns = samples or cells. This matrix should not contain zeros. Otherwise, they will be replaced by the minimun number. It is recommended to handle the zeros beforehand with the method of preference.

*.{csv,tsv}

Output

name:type
description
pattern

propr

meta:map

Groovy Map containing sample information. This can be used at the workflow level to pass optional parameters to the module. [id: ‘test’, …]

*.propr.rds:file

R propr object

*.propr.rds

matrix

meta:map

Groovy Map containing sample information. This can be used at the workflow level to pass optional parameters to the module. [id: ‘test’, …]

*.propr.tsv:file

Coefficient matrix

*.propr.tsv

fdr

meta:map

Groovy Map containing sample information. This can be used at the workflow level to pass optional parameters to the module. [id: ‘test’, …]

*.fdr.tsv:file

(optional) propr fdr table

*.fdr.tsv

adj

meta:map

Groovy Map containing sample information. This can be used at the workflow level to pass optional parameters to the module. [id: ‘test’, …]

*.adj.csv:file

(optional) propr adjacency table

*.adj.csv

warnings

*.warnings.log:file

Warnings

*.warnings.log

session_info

*.R_sessionInfo.log:file

dump of R SessionInfo

*.R_sessionInfo.log

versions

versions.yml:file

File containing software versions

versions.yml