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Differential gene test

Source: R/SCP-analysis.R
RunDEtest.Rd

This function utilizes the Seurat package to perform a differential expression (DE) test on gene expression data. Users have the flexibility to specify custom cell groups, marker types, and various options for DE analysis.

Usage

RunDEtest(
  srt,
  group_by = NULL,
  group1 = NULL,
  group2 = NULL,
  cells1 = NULL,
  cells2 = NULL,
  features = NULL,
  markers_type = c("all", "paired", "conserved", "disturbed"),
  grouping.var = NULL,
  meta.method = c("maximump", "minimump", "wilkinsonp", "meanp", "sump", "votep"),
  test.use = "wilcox",
  only.pos = TRUE,
  fc.threshold = 1.5,
  base = 2,
  pseudocount.use = 1,
  mean.fxn = NULL,
  min.pct = 0.1,
  min.diff.pct = -Inf,
  max.cells.per.ident = Inf,
  latent.vars = NULL,
  min.cells.feature = 3,
  min.cells.group = 3,
  norm.method = "LogNormalize",
  p.adjust.method = "bonferroni",
  slot = "data",
  assay = NULL,
  BPPARAM = BiocParallel::bpparam(),
  seed = 11,
  verbose = TRUE,
  ...
)

Arguments

srt

A Seurat object.

group_by

A grouping variable in the dataset to define the groups or conditions for the differential test. If not provided, the function uses the "active.ident" variable in the Seurat object.

group1

A vector of cell IDs or a character vector specifying the cells that belong to the first group. If both group_by and group1 are provided, group1 takes precedence.

group2

A vector of cell IDs or a character vector specifying the cells that belong to the second group. This parameter is only used when group_by or group1 is provided.

cells1

A vector of cell IDs specifying the cells that belong to group1. If provided, group1 is ignored.

cells2

A vector of cell IDs specifying the cells that belong to group2. This parameter is only used when cells1 is provided.

features

A vector of gene names specifying the features to consider for the differential test. If not provided, all features in the dataset are considered.

markers_type

A character value specifying the type of markers to find. Possible values are "all", "paired", "conserved", and "disturbed".

grouping.var

A character value specifying the grouping variable for finding conserved or disturbed markers. This parameter is only used when markers_type is "conserved" or "disturbed".

meta.method

A character value specifying the method to use for combining p-values in the conserved markers test. Possible values are "maximump", "minimump", "wilkinsonp", "meanp", "sump", and "votep".

test.use

Denotes which test to use. Available options are:

  • "wilcox" : Identifies differentially expressed genes between two groups of cells using a Wilcoxon Rank Sum test (default); will use a fast implementation by Presto if installed

  • "wilcox_limma" : Identifies differentially expressed genes between two groups of cells using the limma implementation of the Wilcoxon Rank Sum test; set this option to reproduce results from Seurat v4

  • "bimod" : Likelihood-ratio test for single cell gene expression, (McDavid et al., Bioinformatics, 2013)

  • "roc" : Identifies 'markers' of gene expression using ROC analysis. For each gene, evaluates (using AUC) a classifier built on that gene alone, to classify between two groups of cells. An AUC value of 1 means that expression values for this gene alone can perfectly classify the two groupings (i.e. Each of the cells in cells.1 exhibit a higher level than each of the cells in cells.2). An AUC value of 0 also means there is perfect classification, but in the other direction. A value of 0.5 implies that the gene has no predictive power to classify the two groups. Returns a 'predictive power' (abs(AUC-0.5) * 2) ranked matrix of putative differentially expressed genes.

  • "t" : Identify differentially expressed genes between two groups of cells using the Student's t-test.

  • "negbinom" : Identifies differentially expressed genes between two groups of cells using a negative binomial generalized linear model. Use only for UMI-based datasets

  • "poisson" : Identifies differentially expressed genes between two groups of cells using a poisson generalized linear model. Use only for UMI-based datasets

  • "LR" : Uses a logistic regression framework to determine differentially expressed genes. Constructs a logistic regression model predicting group membership based on each feature individually and compares this to a null model with a likelihood ratio test.

  • "MAST" : Identifies differentially expressed genes between two groups of cells using a hurdle model tailored to scRNA-seq data. Utilizes the MAST package to run the DE testing.

  • "DESeq2" : Identifies differentially expressed genes between two groups of cells based on a model using DESeq2 which uses a negative binomial distribution (Love et al, Genome Biology, 2014).This test does not support pre-filtering of genes based on average difference (or percent detection rate) between cell groups. However, genes may be pre-filtered based on their minimum detection rate (min.pct) across both cell groups. To use this method, please install DESeq2, using the instructions at https://bioconductor.org/packages/release/bioc/html/DESeq2.html

only.pos

Only return positive markers (FALSE by default)

fc.threshold

A numeric value used to filter genes for testing based on their average fold change between/among the two groups. By default, it is set to 1.5

base

The base with respect to which logarithms are computed.

pseudocount.use

Pseudocount to add to averaged expression values when calculating logFC. 1 by default.

mean.fxn

Function to use for fold change or average difference calculation. The default depends on the the value of fc.slot:

  • "counts" : difference in the log of the mean counts, with pseudocount.

  • "data" : difference in the log of the average exponentiated data, with pseudocount. This adjusts for differences in sequencing depth between cells, and assumes that "data" has been log-normalized.

  • "scale.data" : difference in the means of scale.data.

min.pct

only test genes that are detected in a minimum fraction of min.pct cells in either of the two populations. Meant to speed up the function by not testing genes that are very infrequently expressed. Default is 0.01

min.diff.pct

only test genes that show a minimum difference in the fraction of detection between the two groups. Set to -Inf by default

max.cells.per.ident

Down sample each identity class to a max number. Default is no downsampling. Not activated by default (set to Inf)

latent.vars

Variables to test, used only when test.use is one of 'LR', 'negbinom', 'poisson', or 'MAST'

min.cells.feature

Minimum number of cells expressing the feature in at least one of the two groups, currently only used for poisson and negative binomial tests

min.cells.group

Minimum number of cells in one of the groups

norm.method

Normalization method for fold change calculation when slot is 'data'. Default is "LogNormalize".

p.adjust.method

A character value specifying the method to use for adjusting p-values. Default is "bonferroni".

slot

Slot to pull data from; note that if test.use is "negbinom", "poisson", or "DESeq2", slot will be set to "counts"

assay

Assay to use in differential expression testing

BPPARAM

A BiocParallelParam object specifying the parallelization parameters for the differential test. Default is BiocParallel::bpparam().

seed

An integer value specifying the seed. Default is 11.

verbose

A logical value indicating whether to display progress messages during the differential test. Default is TRUE.

...

Additional arguments to pass to the FindMarkers function.

See also

RunEnrichment RunGSEA GroupHeatmap

Examples

library(dplyr)
data("pancreas_sub")
pancreas_sub <- RunDEtest(pancreas_sub, group_by = "SubCellType")
#> Warning: The following arguments are not used: drop
#> Warning: The following arguments are not used: drop
#> Error in as.vector(data): no method for coercing this S4 class to a vector
AllMarkers <- filter(pancreas_sub@tools$DEtest_SubCellType$AllMarkers_wilcox, p_val_adj < 0.05 & avg_log2FC > 1)
#> Error in UseMethod("filter"): no applicable method for 'filter' applied to an object of class "NULL"
table(AllMarkers$group1)
#> Error: object 'AllMarkers' not found
ht1 <- GroupHeatmap(pancreas_sub, features = AllMarkers$gene, feature_split = AllMarkers$group1, group.by = "SubCellType")
#> Error: object 'AllMarkers' not found
ht1$plot
#> Error: object 'ht1' not found

TopMarkers <- AllMarkers %>%
  group_by(gene) %>%
  top_n(1, avg_log2FC) %>%
  group_by(group1) %>%
  top_n(3, avg_log2FC)
#> Error: object 'AllMarkers' not found
ht2 <- GroupHeatmap(pancreas_sub, features = TopMarkers$gene, feature_split = TopMarkers$group1, group.by = "SubCellType", show_row_names = TRUE)
#> Error: object 'TopMarkers' not found
ht2$plot
#> Error: object 'ht2' not found

pancreas_sub <- RunDEtest(pancreas_sub, group_by = "SubCellType", markers_type = "paired")
#> Warning: The following arguments are not used: drop
#> Warning: The following arguments are not used: drop
#> Error in as.vector(data): no method for coercing this S4 class to a vector
PairedMarkers <- filter(pancreas_sub@tools$DEtest_SubCellType$PairedMarkers_wilcox, p_val_adj < 0.05 & avg_log2FC > 1)
#> Error in UseMethod("filter"): no applicable method for 'filter' applied to an object of class "NULL"
table(PairedMarkers$group1)
#> Error: object 'PairedMarkers' not found
ht3 <- GroupHeatmap(pancreas_sub, features = PairedMarkers$gene, feature_split = PairedMarkers$group1, group.by = "SubCellType")
#> Error: object 'PairedMarkers' not found
ht3$plot
#> Error: object 'ht3' not found

data("panc8_sub")
panc8_sub <- Integration_SCP(panc8_sub, batch = "tech", integration_method = "Seurat")
#> [2025-09-08 16:13:37.251881] Start Seurat_integrate
#> Error in invoke(.fn = paste0(integration_method, "_integrate"), .args = args[names(args) %in%     formalArgs(paste0(integration_method, "_integrate"))]): could not find function "invoke"
CellDimPlot(panc8_sub, group.by = c("celltype", "tech"))
#> Error in DefaultReduction(srt): Unable to find any reductions.

panc8_sub <- RunDEtest(panc8_sub, group_by = "celltype", grouping.var = "tech", markers_type = "conserved")
#> Warning: The following arguments are not used: drop
#> Warning: The following arguments are not used: drop
#> Error in as.vector(data): no method for coercing this S4 class to a vector
ConservedMarkers1 <- filter(panc8_sub@tools$DEtest_celltype$ConservedMarkers_wilcox, p_val_adj < 0.05 & avg_log2FC > 1)
#> Error in UseMethod("filter"): no applicable method for 'filter' applied to an object of class "NULL"
table(ConservedMarkers1$group1)
#> Error: object 'ConservedMarkers1' not found
ht4 <- GroupHeatmap(panc8_sub,
  slot = "data",
  features = ConservedMarkers1$gene, feature_split = ConservedMarkers1$group1,
  group.by = "tech", split.by = "celltype", within_groups = TRUE
)
#> Error: object 'ConservedMarkers1' not found
ht4$plot
#> Error: object 'ht4' not found

panc8_sub <- RunDEtest(panc8_sub, group_by = "tech", grouping.var = "celltype", markers_type = "conserved")
#> Warning: The following arguments are not used: drop
#> Warning: The following arguments are not used: drop
#> Error in as.vector(data): no method for coercing this S4 class to a vector
ConservedMarkers2 <- filter(panc8_sub@tools$DEtest_tech$ConservedMarkers_wilcox, p_val_adj < 0.05 & avg_log2FC > 1)
#> Error in UseMethod("filter"): no applicable method for 'filter' applied to an object of class "NULL"
table(ConservedMarkers2$group1)
#> Error: object 'ConservedMarkers2' not found
ht4 <- GroupHeatmap(panc8_sub,
  slot = "data",
  features = ConservedMarkers2$gene, feature_split = ConservedMarkers2$group1,
  group.by = "tech", split.by = "celltype"
)
#> Error: object 'ConservedMarkers2' not found
ht4$plot
#> Error: object 'ht4' not found

panc8_sub <- RunDEtest(panc8_sub, group_by = "celltype", grouping.var = "tech", markers_type = "disturbed")
#> Warning: The following arguments are not used: drop
#> Warning: The following arguments are not used: drop
#> Error in as.vector(data): no method for coercing this S4 class to a vector
DisturbedMarkers <- filter(panc8_sub@tools$DEtest_celltype$DisturbedMarkers_wilcox, p_val_adj < 0.05 & avg_log2FC > 1 & var1 == "smartseq2")
#> Error in UseMethod("filter"): no applicable method for 'filter' applied to an object of class "NULL"
table(DisturbedMarkers$group1)
#> Error: object 'DisturbedMarkers' not found
ht5 <- GroupHeatmap(panc8_sub,
  slot = "data",
  features = DisturbedMarkers$gene, feature_split = DisturbedMarkers$group1,
  group.by = "celltype", split.by = "tech"
)
#> Error: object 'DisturbedMarkers' not found
ht5$plot
#> Error: object 'ht5' not found

gene_specific <- names(which(table(DisturbedMarkers$gene) == 1))
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'which': object 'DisturbedMarkers' not found
DisturbedMarkers_specific <- DisturbedMarkers[DisturbedMarkers$gene %in% gene_specific, ]
#> Error: object 'DisturbedMarkers' not found
table(DisturbedMarkers_specific$group1)
#> Error: object 'DisturbedMarkers_specific' not found
ht6 <- GroupHeatmap(panc8_sub,
  slot = "data",
  features = DisturbedMarkers_specific$gene, feature_split = DisturbedMarkers_specific$group1,
  group.by = "celltype", split.by = "tech"
)
#> Error: object 'DisturbedMarkers_specific' not found
ht6$plot
#> Error: object 'ht6' not found

ht7 <- GroupHeatmap(panc8_sub,
  slot = "data", aggregate_fun = function(x) mean(expm1(x)) + 1,
  features = DisturbedMarkers_specific$gene, feature_split = DisturbedMarkers_specific$group1,
  group.by = "celltype", grouping.var = "tech", numerator = "smartseq2"
)
#> Error: object 'DisturbedMarkers_specific' not found
ht7$plot
#> Error: object 'ht7' not found