findMarkers_1vAll: Calculate 1 vs. All standard fold change for each gene x cell...

In lahuuki/DeconvoBuddies: Helper Functions for LIBD Deconvolution

Calculate 1 vs. All standard fold change for each gene x cell type, wrapper function for scran::findMarkers().

Description

For each cell type, this function computes the statistics comparing that cell type (the "1") against all other cell types combined ("All").

Usage

findMarkers_1vAll(
  sce,
  assay_name = "counts",
  cellType_col = "cellType",
  add_symbol = FALSE,
  mod = NULL,
  verbose = TRUE,
  direction = "up"
)

Arguments

sce	A SingleCellExperiment object.
assay_name	Name of the assay to use for calculation. See see assayNames(sce) for possible values.
cellType_col	Column name on colData(sce) that denotes the cell type.
add_symbol	A logical(1) indicating whether to add the gene symbol column to the marker stats table.
mod	A character(1) string specifying the model used as design in scran::findMarkers(). It can be NULL (default) if there are no blocking terms with uninteresting factors as documented at pairwiseTTests.
verbose	A logical(1) choosing whether to print progress messages or not.
direction	A character(1) for the choice of direction tested for gene cell type markers: "up" (default), "any", or "down". Impacts p-values: if "up" genes with logFC < 0 will have p.value = 1.

Details

See https://github.com/MarioniLab/scran/issues/57 for a more in depth discussion about the standard log fold change statistics provided by scran::findMarkers().

See also https://youtu.be/IaclszgZb-g for a LIBD rstats club presentation on "Finding and interpreting marker genes in sc/snRNA-seq data". The companion notes are available at https://docs.google.com/document/d/1BeMtKgE7gpmNywInndVC9o_ufopn-U2EZHB32bO7ObM/edit?usp=sharing.

Value

A tibble::tibble() of 1 vs. ALL standard log fold change + p-values for each gene x cell type.

• gene is the name of the gene (from rownames(sce)).

• logFC the log fold change from the DE test

• log.p.value the log of the p-value of the DE test

• log.FDR the log of the False Discovery Rate adjusted p.value

• std.logFC the standard logFC.

• cellType.target the cell type we're finding marker genes for

• std.logFC.rank the rank of std.logFC for each cell type

• std.logFC.anno is an annotation of the std.logFC value helpful for plotting.

Examples

## load example SingleCellExperiment
if (!exists("sce_DLPFC_example")) sce_DLPFC_example <- fetch_deconvo_data("sce_DLPFC_example")
## Explore properties of the sce object
sce_DLPFC_example

## this data contains logcounts of gene expression
SummarizedExperiment::assays(sce_DLPFC_example)$logcounts[1:5, 1:5]

## nuclei are classified in to cell types
table(sce_DLPFC_example$cellType_broad_hc)

## Get the 1vALL stats for each gene for each cell type defined in
## `cellType_broad_hc`
marker_stats_1vAll <- findMarkers_1vAll(
    sce = sce_DLPFC_example,
    assay_name = "logcounts",
    cellType_col = "cellType_broad_hc",
    mod = "~BrNum"
)

## explore output, top markers have high logFC
head(marker_stats_1vAll)

lahuuki/DeconvoBuddies documentation built on Nov. 2, 2024, 10:40 p.m.