Proteins

Necessary inputs

The pipeline has as input the count matrix with the abundance of proteins and a phenotype file describing the metadata of each sample. You have to modify one file, specifying which part of the analysis you want to run and specific parameters params_proteins.yml:

params{
  outdir  '[full path of location you want to output]'
  count_matrix_proteins  '[full path of location your proteins count matrix]'
  input_proteins   '[full path of location your proteins phenotype file]'
}

Change the location of the files appropriately

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_proteins.yml. See below (preprocess_matrix,dea,pea)

Running the pipeline

The general command to run the pipeline is:

nextflow run multiomicsintegrator -params-file multiomicsintegrator/params_proteins.yml -profile docker

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

Preprocess

Initially, there is an optional module preprocess_matrix with various preprocessing steps. Namely, the user can perform filtering, normalization and batch effect correction, depending on the state of their data.

Input_proteins should have a column named “condition” describing the states of the experiment (ctr vs treat) and one called “batch” describing batches of the experiment (if there is no batch then the replicate column is the batch). If the user wants other names they have to specify in the params_proteins.yml the column name of their conditions and that column name to be present in the input_proteins.csv file:

params{

    mom_norm_condition_proteins           = "condition"   # must be column in samples info
    mom_norm_treatment_proteins           = "condition"   # must be column in samples info
    mom_batch_condition_proteins       = "condition"    # which is the condition of interest, must be present in columns of sample info
    mom_batch_batch_proteins           = "batch"
}

Once the count matrix is ready, we can move on to differential expression analysis. We provide three different algorithms for that:

Note

You need to specify which algorithm you are going to use in params_proteins.yml

params{
  alg_proteins     = 'edger' # Default
}

DEA

edger

params{
    dgergroupingfactor_proteins        =  "condition" # column name where your treatments are located
    edgerformulamodelmatrix_proteins   =  "~0 + condition" # design matrix, values have to be column names in the samplesheet_proteins.csv
    edgercontrasts_proteins            = "TNBC-non_TNBC"  # contrasts of interest. Values have to be present in the samplesheet_proteins.csv
}

DESeq2

Important note

For DESeq2 to run, the column of the treatments in the samplesheet_proteins.csv has to be named condition and the batches batch

params{
    batchdeseq2_proteins               = false # perform batch effect correction
    deseqFormula_proteins              = "~0 + condition"  # design matrix, values have to be column names in the samplesheet_proteins.csv
    con1_proteins                     = "mkc"   # control, has to be cell in samplesinfo
    con2_proteins                     = "dmso"  # treatment, has to be cell in samplesinfo
    deseq2single_matrix             = true   # if the input is a single matrix or a directory of files
}

RankProduct

Inputs for running RankProduct are the same, with a single difference: The condition column has to be named cl and the user has to assign 0 to controls and 1 to treatments

sampleID,cl
CONTROL_REP1,1
CONTROL_REP2,1
TREATMENT_REP1,0

Pathway Enrichment Analysis (PEA)

The last step of the analysis is to perform pathway enrichment analysis with clusterprofiler or biotranslator:

params{
    features                         = null # if you want to perform clusterprofiler as a standalone tool, specify directory of features here
    alg                        = "edger" # algoritmh you used to perform differential expression analysis or mcia
    proteins_genespval                  = 1 # pval cutoff for genes
    mirna_genespval                  = 1 # pval cutoff for miRNA
    proteins_genespval               = 0.5 # pval cutoff for proteins
    lipids_genespval                 = 0.5 # pval cutoff for lipids
}

BIOTRANSLATOR

params{

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

}