Genes

Nessecary inputs

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. It has to be a comma-separated file with 6 columns, and a header row as shown in the examples below.

sample,fastq_1,fastq_2,condition,batch,cl
CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,control,1,0
CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz,control,2,0
CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz,control,3,0

Important

Sample names have to be in the first column or in a column called sampleID.

If you have column names other than condition and batch you need to declare the names in the params_genes.yml. See below (Preprocess,DEA,PEA)

sampleID,condition
CONTROL_REP1,ctrl
CONTROL_REP2,ctrl
TREATMENT_REP1,treat

C o l u m n	Description
s a m p l e	Custom sample name. This entry will be identical for multiple sequencing libraries/runs from the same sample. Spaces in sample names are automatically converted to underscores (`_`).
f a s t q _ 1	Full path to FastQ file for Illumina short reads 1. File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”.
f a s t q _ 2	Full path to FastQ file for Illumina short reads 2. File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”.
s t r a n d n e s s	Custom sample name. This entry will be identical for multiple sequencing libraries/runs from the same sample. Spaces in sample names are automatically converted to underscores (`_`).
c o n d i t i o n	Metadata describing your test condition (or treatment, or state etc)
b a t c h	Describes unwanted source of variation (e.g. technical replicates, different platfroms, different batches etc.).

Start from SRA

Alternatively, instead of providing with paths of fastq files you can provide a column with SRA run identifiers you wish to download from NCBI in the first column named sample and the second column has to be named sampleID and store the SRA codes:

sample,sampleID,condition,batch,cl
SRR2015757,SRX1022901,control,1,1
SRR2015760,SRX1022904,control,2,1
SRR2015761,SRX1022905,control,3,1

An example samplesheet has been provided with the pipeline.

Reference files

The user can choose to run the pipeline using fasta and gtf files supplied by aws:

params{
  igenomes_base              : 's3://ngi-igenomes/igenomes'
  igenomes_ignore            : false
}

Alternatively, they can provide the location of local refference files:

params{
  fasta_genes                   : '/home/bianca/gencode2/GRCh38.primary_assembly.genome.fa.gz'
  transcript_fasta_genes         : '/home/bianca/gencode2/gencode.v44.transcripts.fa.gz'
  gtf_genes                       : '/home/bianca/gencode2/gencode.v44.annotation.gtf.gz'

Running the pipeline

In order to run the genes part of the pipeline you have to modify one file, specifying which part of the analysis you want to run and specific parameters params_genes.yml:

params{
genome = 'GRCh38'  # Reference genome identifier from AWS, check /conf/igenomes.config
outdir  = 'full path of location you want to output'
salmonDiGenes = '/path/to/outdir/salmon/genes'
input_genes = 'full path of samplesheet with SRA code or location of fastq files'
}

In case you started from SRA codes you also need to declare it in the params_genes.yml

params{
  sra_genes = true
}

The general command to run the pipeline is:

nextflow run multiomicsintegrator -params-file multiomicsintegrator/params_genes.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

The pipeline initially downloads SRA codes and converts the runs into fastq files. Alternatively, you can provide local fastq files. It then performs quality control with FASTQC and then automatically detects and removes adapters with Trimgalore.

Each of the above steps can be skipped, for example if you don’t want to perform quality control, you can specify in the params_genes.yml file:

params{
  skip_qc_genes= true
}

It then employs salmon in order to obtain quantification files that are outputed in

/OUTDIR/salmon_genes/sampleID/quant.sf

directory.

If you have performed the alignment step outside you can organise your data in the aforementioned way and specify the directory that holds the quant.sf files in the params_genes.yml:

params{
  salmonDirGenes = '/path/to/directory_that_holds_quantification_files'
}

Note: All files need to be in the format:

salmonDirGenes:
- sampleID/
  - quant.sf

The sampleID is the same of the sampleID of the phenotype file (or the sample names of the samplesheet.csv)

If you want to skip the alignment step you need to specify the location of the count matrix and the respective phenotype (samplesheet_genes.csv) you have, in the params_genes.yml file:

params{
  count_matrix_genes = 'path where count matrix is located'
  input_genes = 'path where your phenotype file is located'
}

Preprocess

After the formation of the count matrix there is an optional module preprocess_matrix that performs preprocessing steps on the count matrix. Namely, the user can perform filtering, normalization and batch effect correction, depending on the state of their data.

Input_genes 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_genes.yml the column name of their conditions and that column name to be present in the samplesheet_genes.csv file:

params{
    mom_filt_method_genes           = "filterByExp"  # filterByExp or choose a cutoff value
    mom_norm_method_genes           = "quantile"     # calcNorm quantile
    mom_norm_condition_genes           = "condition"   # must be column in samples info
    mom_norm_treatment_genes           = "condition"   # must be column in samples info
    mom_batch_method_genes          = "com" # com for combat, sva,  comsva for combat & sva, svacom for sva and comba, none
    mom_batch_condition_genes       = "condition"    # which is the condition of interest, must be present in columns of sample info
    mom_batch_batch_genes           = "batch"
}

DEA

Next step is differential expression analysis. We provide three different algorithms for that, which we describe below.

Note

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

params{
  alg_genes     = 'edger' # Default
}

edger

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

DESeq2

Important note

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

params{
    batchdeseq2_genes               = false # perform batch effect correction
    deseqFormula_genes              = "~0 + condition"  # design matrix, values have to be column names in samlesInfo_genes.txt
    con1_genes                     = "mkc"   # control, has to be cell in samplesinfo
    con2_genes                     = "dmso"  # treatment, has to be cell in samplesinfo
    deseq2single_matrix             = true   # if the input is a single matrix or a directory of files
}

RankProduct rankprod

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

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

or if you started from SRA codes

sampleID,cl
SRX1022911,0
SRX1022917,0
SRX1022920,1

PEA

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
    genes_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
}

bash

params{
    // BIOTRANSLATOR
    pea_genes      = "biotranslator"
    biotrans_genes_organism          = "hsapiens"
    biotrans_genes_keytype          = "gene_symbol"
    biotrans_genes_ontology         = "GO" // MGIMP, Reactome
}