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Prepare LimmaVoom Specifications

Process Details

  • Name: Prepare_LimmaVoom
  • Process UUID: AlWXX7KZpfxYvKK6SmCOf7uclZg8Dl
  • Process Group: Misc.

Overview

This process sets up the necessary inputs and options for running Limma Voom analysis, including quality control, filtering, PCA plots, batch correction, significance thresholds, and gene labeling. It provides a comprehensive framework for differential expression analysis with extensive customization options for data preprocessing, visualization, and statistical analysis parameters.

This process is implemented in Bash, which invokes a Python script (prepare_limmaVoom.py) for the main analysis workflow.

Key Functionality

  • Data Preprocessing: Filters genes based on count thresholds and sample requirements, with options for local or global filtering strategies
  • Quality Control: Generates count distribution plots, reproducibility plots, and PCA visualizations with customizable aesthetics
  • Batch Correction: Performs optional batch effect correction using various normalization algorithms prior to differential expression analysis
  • Limma Voom Analysis: Configures and executes Limma Voom differential expression analysis with customizable normalization methods and statistical parameters
  • Visualization: Creates volcano plots, MA plots, and heatmaps with options for gene highlighting and labeling
  • Gene Annotation: Converts between different gene identifier types (ENSEMBL, ENTREZID, SYMBOL) using organism-specific databases

Input/Output Specification

Inputs

Required Inputs

  • Count File

    • Description: Gene or transcript count matrix with samples as columns and features as rows
    • Format: TSV

  • Run LimmaVoom Flag

    • Description: Control parameter to determine whether the process should execute
    • Format: Value

Optional Inputs

  • Group File

    • Description: Sample metadata file containing experimental groups and batch information
    • Format: TSV

  • Compare File

    • Description: Comparison matrix defining which groups to compare in differential expression analysis
    • Format: TSV

Outputs

  • Output File

    • Description: Processed analysis results and configuration files
    • Format: TSV

  • Postfix

    • Description: String identifier for output file naming
    • Format: Value

Parameters & Settings

These parameters can be adjusted in the Foundry UI when running this process.

  • Feature Type

    • Description: Type of features to analyze
    • Available options: gene (default), transcript

  • Create count distribution plot

    • Description: Generate quality control plots showing count distributions
    • Default value: true

  • Create reproducibility plots

    • Description: Generate all-vs-all sample correlation plots
    • Default value: true

  • Create PCA plots

    • Description: Generate principal component analysis plots
    • Default value: true

  • Filter Strategy

    • Description: Local filter considers only samples in the comparison. Global filter considers all samples within counts file
    • Available options: local (default), global

  • Minimal Counts

    • Description: Filter genes with less than this count
    • Default value: 10

  • Minimal Samples

    • Description: Filter genes which do not have at least min_count reads in this many samples
    • Default value: 1

  • Minimal Total Counts per Sample

    • Description: Removes samples with less than this many total counts prior to DE
    • Default value: 1000

  • Excluded Events

    • Description: Space, comma, or newline separated list of genes to remove prior to any processing
    • Default value: (empty)

  • Run batch correction

    • Description: Perform batch correction prior to differential expression analysis
    • Default value: false

  • Correction column

    • Description: Column used for batch correction
    • Default value: batch

  • Biological variable column

    • Description: Column containing biological variable grouping
    • Default value: group

  • Normalization Algorithm

    • Description: Normalization algorithm to use prior to batch correction
    • Available options: MRN, TMM (default), RLE, upperquartile

  • Data Transformation

    • Description: Transformation to apply prior to PCA (does not apply to data sent into differential expression)
    • Available options: Default (default), rlog, vst, None

  • Color

    • Description: Column from metadata file by which to determine point color
    • Default value: group

  • Shape

    • Description: Column from metadata file by which to determine the point shape
    • Default value: (empty)

  • Fill

    • Description: Column from metadata file by which to determine point fill
    • Default value: (empty)

  • Transparency

    • Description: Column from metadata file by which to determine point transparency
    • Default value: (empty)

  • Label

    • Description: Column from metadata file by which to determine point label
    • Default value: (empty)

  • Run Limma Voom

    • Description: Execute Limma Voom differential expression analysis
    • Default value: true

  • Use batch corrected counts

    • Description: Use batch corrected counts for differential expression analysis
    • Default value: true

  • Normalization Method

    • Description: Normalization algorithm to use during differential expression calculation
    • Available options: TMM (default), TMMwsp, RLE, upperquartile, none

  • LogRatio Trim

    • Description: The fraction of observations to be trimmed from each tail of the distribution of log-ratios before computing the mean
    • Default value: 0.3

  • sumTrim

    • Description: The fraction of observations to be trimmed from each tail of the distribution of A-values before computing the mean
    • Default value: 0.05

  • A cutoff

    • Description: Minimum cutoff applied to A-values for each pair of samples
    • Default value: -1e10

  • Use Weights

    • Description: Use asymptotic binomial precision weights when computing the mean M-values
    • Default value: true

  • p

    • Description: Numeric value between 0 and 1 specifying which quantile of the counts should be used
    • Default value: 0.75

  • Create volcano plots

    • Description: Generate volcano plots for differential expression results
    • Default value: true

  • Create MA plots

    • Description: Generate MA plots for differential expression results
    • Default value: true

  • Create heatmap

    • Description: Generate heatmap of differentially expressed genes
    • Default value: true

  • Adjusted p-value cutoff

    • Description: Adjusted p-value significance threshold required to be called differentially expressed
    • Default value: 0.05

  • Fold change cutoff

    • Description: log2 Fold Change threshold required to be called differentially expressed
    • Default value: 1

  • Adjusted p-value minimum

    • Description: Reassign adjusted p-values below this value to this value. Set at 0 to disable
    • Default value: 0

  • Fold change maximum

    • Description: Reassign fold changes (log2) greater than this value to this value. Set to Inf to disable
    • Default value: Inf

  • Convert gene names

    • Description: Convert between different gene identifier types
    • Default value: false

  • From

    • Description: Name used in count file
    • Available options: ENSEMBL, ENTREZID, SYMBOL (default)

  • To

    • Description: Name to use in plots
    • Available options: ENSEMBL (default), ENTREZID, SYMBOL

  • Number Labeled

    • Description: Number of significant genes to label. Set to Inf to label all. Set to 0 to turn labels off
    • Default value: Inf

  • Highlighted Genes

    • Description: Space separated list of genes to label regardless of significance
    • Default value: (empty)

  • Create additional volcano plots for highlighted genes only

    • Description: Generate separate volcano plots showing only highlighted genes
    • Default value: false

  • Create additional MA plots for highlighted genes only

    • Description: Generate separate MA plots showing only highlighted genes
    • Default value: false

References & Resources

  • Tool Documentation: Contact the team for details on prepare_limmaVoom.py
  • Related Papers: Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43(7), e47. DOI: 10.1093/nar/gkv007