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Single Cell Pathway Analysis (SCPA)

website·Indexed 30 days ago

https://jackbibby1.github.io/SCPA//index.html

An R package for comparing gene sets and pathways in single-cell data. It provides tools to extract data from Seurat and SingleCellExperiment objects, create pathway matrices, and perform pathway comparisons. The package includes visualization functions such as plot_heatmap and plot_rank to analyze q-values and pathway rankings.

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What's inside SCPA

  1. Overview of Single Cell Pathway Analysis (SCPA)

    SCPA is a method for pathway analysis in single-cell RNA-seq data. Unlike traditional methods that rely on gene enrichment or over-representation, SCPA defines pathway activity as a change in the multivariate distribution of a pathway across conditions. This allows for the identification of pathways that show transcriptional changes independent of overall enrichment. SCPA supports multisample testing (e.g., comparing multiple time points or pseudotime trajectory phases) and can be applied to Seurat objects, SingleCellExperiment (SCE) objects, or manually subsetted expression matrices.

  2. Overview of Single Cell Pathway Analysis (SCPA)

    SCPA is a method for pathway analysis in single-cell RNA-seq data. Unlike traditional methods that rely on gene enrichment or over-representation, SCPA defines pathway activity as a change in the multivariate distribution of a given pathway across conditions. This allows for the identification of pathways that show transcriptional changes independent of overall enrichment. SCPA supports multisample testing (e.g., comparing multiple time points or pseudotime trajectories) and can be applied to Seurat objects, SingleCellExperiment (SCE) objects, or manually subsetted expression matrices.

  3. Overview of Single Cell Pathway Analysis (SCPA)

    SCPA is a method for pathway analysis in single-cell RNA-seq data. Unlike traditional methods that rely on gene enrichment or over-representation, SCPA defines pathway activity as a change in the multivariate distribution of a pathway across conditions. This allows for the identification of pathways that show transcriptional changes independent of overall enrichment. SCPA supports multisample testing (e.g., comparing multiple time points or pseudotime trajectories) and is compatible with Seurat objects, SingleCellExperiment (SCE) objects, and manually subsetted expression matrices.

  4. Understand GNU General Public License (GPL) v3 basic permissions

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    The GNU General Public License v3 is a free, copyleft license designed to guarantee the freedom to share and change all versions of a program. It ensures that software remains free for all users by requiring that any distributed copies or modified versions pass on the same freedoms to recipients, including access to the source code. Key protections include prohibiting the use of software patents to make the program non-free and preventing manufacturers from blocking users from installing modified software versions on their devices.

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    <one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year>  <name of author>

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
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This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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You should have received a copy of the GNU General Public License
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  7. Example: Subset Seurat data by Hour and Cell Type

    This example demonstrates how to extract an expression matrix for CD4 cells collected at hour 12 from a Seurat object.
    cd4 <- seurat_extract(
   Seurat_Object,
   meta1 = "Hour",
   value_meta1 = 12,
   meta2 = "Cell_Type",
   value_meta2 = "CD4"
)

  8. Requirements for conveying verbatim copies of GPLv3 software

    To convey verbatim copies of the Program's source code, you must:

    1. Conspicuously and appropriately publish an appropriate copyright notice on each copy.
    2. Keep all notices intact that state this License and any non-permissive terms added under section 7 apply to the code.
    3. Keep all notices of the absence of any warranty intact.
    4. Provide all recipients with a copy of the GPLv3 License along with the Program.

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    For 'User Products' (consumer products used for personal/household purposes or designed for dwellings), if you convey object code as part of a transaction transferring possession in perpetuity or for a fixed term, you must provide Installation Information.

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  11. Use CSV files as gene sets in SCPA

    SCPA can read CSV files formatted like classical GMT files: the first column must be the pathway name, and subsequent columns must contain the genes belonging to that pathway. You can provide the file path directly to the compare_pathways() function.
    # Specify the path to the CSV gene set file
pathways <- "path/to/geneset/file.csv"

# Pass the path to compare_pathways
scpa_out <- compare_pathways(samples = samples, pathways = pathways)

  12. Perform systems-level pathway comparison across cell types

    To characterize pathway perturbations in a disease, you can loop the compare_pathways function across different cell types. This involves extracting expression matrices for both healthy and disease states for each cell type and comparing them against a set of pathways (e.g., from MSigDB). For large datasets, parallel processing can be enabled using the parallel = TRUE and cores = x arguments.
    # Assume blood_atlas is a SplitObject by 'Status' (e.g., Healthy vs COVID)
# pathways is a path to a CSV of gene sets

scpa_out <- list()
for (i in cell_types) {
  # Extract expression matrices for the specific cell type
  healthy <- seurat_extract(blood_atlas$Healthy, 
                            meta1 = "cell.type.fine", value_meta1 = i)
  
  covid <- seurat_extract(blood_atlas$COVID, 
                          meta1 = "cell.type.fine", value_meta1 = i)
  
  # Compare pathways and keep only Pathway and qval
  scpa_out[[i]] <- compare_pathways(list(healthy, covid), pathways) %>%
    select(Pathway, qval) %>%
    set_colnames(c("Pathway", paste(i, "qval", sep = "_")))
}