Lipids

Necessary inputs

The pipeline has as input the count matrix with the abundance of lipids and a phenotype file describing the metadata of each sample.

params{
  count_matrix_lipids = ' path where count matrix is located'
  input_lipids = 'path where your phenotype file is located'
}

Important

Sample names have to be in the first column or in a column called sampleID and need to match the column names of your count matrix.

If you have column names other than condition and batch you need to declare the names in the params_lipids.yml.

Running the pipeline

In order to run the lipids part of the pipeline you have to modify one file, specifying which part of the analysis you want to run and respective parameters params_lipids.yml:

params{
  outdir  '[full path of location you want to output]'
  count_matrix_lipids  '[full path of location your lipids count matrix]'
  input_lipids   '[full path of location your lipids phenotype file]'
}

The general command to run the pipeline is:

nextflow run multiomicsintegrator -params-file multiomicsintegrator/params_lipids.yaml -profile docker

Change the location of the files appropriately

This will launch the pipeline with the docker configuration profile. See below for more information about profiles.

Note that the pipeline will create the following files in your working directory:

work                'Directory containing the nextflow working files'
<OUTDIR>            ' Location of where you want your results (defined by outdir)'
.nextflow_log       # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

Functionality

All in one analysis with LipidR

We provide the possibility to perform the proprocessing steps, as well as the the differential expression analysis using the R Bioconductor package lipidR. LipidR provides additional exploratory plots regarding the different classes of lipids as well as any enrichment of these classes between conditions. Moreover, it provides with information abou the saturation level of the carbon chains of the different classes of lipids between conditions.

You need to have either the first or one column named sampleID and the column that stores the different settings of your experiment has be named condition in your samplesheet_lipids.csv file.

LipiDB

LipidR will produce differentially expressed features for each category of lipids. Subsequently, LipiDB, using KREGGREST and a local database will find genes associated to these differentially expressed lipids, for each category. Input is the result of lipidR or in other words a txt file that has deregulated lipids along with their logFC and pval. The outputs are in as form of a text file and a heatmap.

PEA

Last step of the analysis is to perform pathway enrichment analysis with metabAnalystR or with biotranslator

BIOTRANSLATOR

params{

    pea_proteins      = "biotranslator"
    biotrans_pro_organism          = "hsapiens"
    biotrans_pro_keytype          = "gene_symbol"
    biotrans_pro_ontology         = "GO" // MGIMP, Reactome

}