FROGSSTAT DESeq2 Preprocess
Context
Are there ASV with differential abundance between 2 conditions ? And which are they ?
To answer these questions, we perform a differential abundance analysis using DESeq2 on the phyloseq object.
The package DESeq2 provides methods to test for differential expression by use of negative binomial generalized linear models
- 1st step: FROGSSTAT DESeq2 Preprocess
- 2nd step : FROGSSTAT DESeq2 Visualisation
Available to analyse ASV or Function (from FROGSFUNC tools)
Be aware to use data without normalisation
DESeq has is own normalisation method suited to this kind of data. It uses the postcount function optimised for metagenomic count table
1st step: create dds object with FROGSSTAT DESeq2 Preprocess
FROGSSTAT DESeq2 Preprocess launches Rscript to compute differential abundance analysis using DESeq2 on a Phyloseq object.
use cases:
- for ASVs
- FROGSSTAT DESeq2 Preprocess needs the phyloseq object (.Rdata) created by FROGSSTAT Phyloseq Import Data
- and
- creates Deseq2 object (dds)
- for functions
- FROGSSTAT DESeq2 Preprocess creates the phyloseq object (.Rdata)
- and
- creates Deseq2 object (dds)
Command line
v4.1.0
usage: deseq2_preprocess.py [-h] [--debug] [--version] -v VAR -a
{ASV,FUNCTION} [-d DATA] [-f INPUT_FUNCTIONS]
[-s SAMPLEFILE] [--out-Phyloseq OUT_PHYLOSEQ]
[-o OUT_RDATA] [-l LOG_FILE]
Launch Rscript to generate dataframe of DESEq2 from a phyloseq object in RData
file
optional arguments:
-h, --help show this help message and exit
--debug Keep temporary files to debug program.
--version show programs version number and exit
-v VAR, --var VAR Experimental variable suspected to have an impact on
abundances. You may precise complexe string such as
variables with confounding effect (ex:
Treatment+Gender or Treatmet*Gender)
Inputs:
-a {ASV,FUNCTION}, --analysis {ASV,FUNCTION}
Type of data to perform the differential analysis.
ASV: DESeq2 is run on the ASVs abundances table.
FUNCTION: DESeq2 is run on FROGSFUNC function
abundances table (frogsfunc_functions_unstrat.tsv from
FROGSFUNC function step).
ASV :
-d DATA, --data DATA The path of RData file containing a phyloseq object,
result of FROGS Phyloseq Import Data. Required.
FUNCTION :
-f INPUT_FUNCTIONS, --input-functions INPUT_FUNCTIONS
Input file of metagenome function prediction
abundances (frogsfunc_functions_unstrat.tsv from
FROGSFUNC function step). Required. (default: None).
-s SAMPLEFILE, --samplefile SAMPLEFILE
path to sample file (format: TSV). Required.
--out-Phyloseq OUT_PHYLOSEQ
path to store phyloseq-class object in Rdata file.
[Default: function_data.Rdata]
Outputs:
-o OUT_RDATA, --out-Rdata OUT_RDATA
The path to store resulting dataframe of DESeq2.
[Default: None]
-l LOG_FILE, --log-file LOG_FILE
This output file will contain several information on
executed commands.
Example of command line:
./deseq2_preprocess.py \
--data data.Rdata \
--analysis ASV \
--log-file deseq2_preprocess_ASV.log \
--out-Rdata deseq2_preprocess_ASV.Rdata \
--var EnvType
./deseq2_preprocess.py \
--samplefile chaillou.sample \
--input-functions frogsfunc_functions_unstrat_EC.tsv \
--analysis FUNCTION \
--log-file deseq2_preprocess_func.log \
--out-Rdata deseq2_preprocess_func.Rdata \
--out-Phyloseq phyloseq_functions.Rdata \
--var EnvType
Galaxy for ASV analyses
FROGSSTAT DESeq2 Preprocess needs the phyloseq object (.Rdata) created by FROGSSTAT Phyloseq Import Data tool.
Outputs for ASV analyses
a asv_dds.Rdata file that is a DESeq2 dataset stored in Rdata file.
Galaxy for function analyses
For FUNCTION: two files are requiered
- the files that contains predicted function abundances, it is named frogsfunc_functions_unstrat.tsv and produced by FROGSFUNC_2_function tool.
- the file that must contain the metadata that characterise each sample i.e. the conditions of experiment with sample ID in the first column as:
FROGSSTAT DESeq2 Preprocess creates the phyloseq object (.Rdata) and the Deseq2 object (dds)
Outputs for function analyses
- a function_data.Rdata that contains information of data in one phyloseq object.
- a function_dds.Rdata that is a DESeq2 dataset stored in Rdata file. This result will be one of the input of the FROGSSTAT DESeq Visualisation tool.
FROGSSTAT DESeq2 Preprocess
FROGSSTAT DESeq2 Preprocess
Context
Are there ASV with differential abundance between 2 conditions ? And which are they ?
To answer these questions, we perform a differential abundance analysis using DESeq2 on the phyloseq object.
The package DESeq2 provides methods to test for differential expression by use of negative binomial generalized linear models
Available to analyse ASV or Function (from FROGSFUNC tools)
Be aware to use data without normalisation
DESeq has is own normalisation method suited to this kind of data. It uses the postcount function optimised for metagenomic count table
1st step: create dds object with FROGSSTAT DESeq2 Preprocess
FROGSSTAT DESeq2 Preprocess launches Rscript to compute differential abundance analysis using DESeq2 on a Phyloseq object.
use cases:
Command line
v4.1.0
usage: deseq2_preprocess.py [-h] [--debug] [--version] -v VAR -a {ASV,FUNCTION} [-d DATA] [-f INPUT_FUNCTIONS] [-s SAMPLEFILE] [--out-Phyloseq OUT_PHYLOSEQ] [-o OUT_RDATA] [-l LOG_FILE] Launch Rscript to generate dataframe of DESEq2 from a phyloseq object in RData file optional arguments: -h, --help show this help message and exit --debug Keep temporary files to debug program. --version show programs version number and exit -v VAR, --var VAR Experimental variable suspected to have an impact on abundances. You may precise complexe string such as variables with confounding effect (ex: Treatment+Gender or Treatmet*Gender) Inputs: -a {ASV,FUNCTION}, --analysis {ASV,FUNCTION} Type of data to perform the differential analysis. ASV: DESeq2 is run on the ASVs abundances table. FUNCTION: DESeq2 is run on FROGSFUNC function abundances table (frogsfunc_functions_unstrat.tsv from FROGSFUNC function step). ASV : -d DATA, --data DATA The path of RData file containing a phyloseq object, result of FROGS Phyloseq Import Data. Required. FUNCTION : -f INPUT_FUNCTIONS, --input-functions INPUT_FUNCTIONS Input file of metagenome function prediction abundances (frogsfunc_functions_unstrat.tsv from FROGSFUNC function step). Required. (default: None). -s SAMPLEFILE, --samplefile SAMPLEFILE path to sample file (format: TSV). Required. --out-Phyloseq OUT_PHYLOSEQ path to store phyloseq-class object in Rdata file. [Default: function_data.Rdata] Outputs: -o OUT_RDATA, --out-Rdata OUT_RDATA The path to store resulting dataframe of DESeq2. [Default: None] -l LOG_FILE, --log-file LOG_FILE This output file will contain several information on executed commands.
Example of command line:
Galaxy for ASV analyses
FROGSSTAT DESeq2 Preprocess needs the phyloseq object (.Rdata) created by FROGSSTAT Phyloseq Import Data tool.
Outputs for ASV analyses
a asv_dds.Rdata file that is a DESeq2 dataset stored in Rdata file.
Galaxy for function analyses
For FUNCTION: two files are requiered
FROGSSTAT DESeq2 Preprocess creates the phyloseq object (.Rdata) and the Deseq2 object (dds)
Outputs for function analyses
A work by FROGS team