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differential_expression.R
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#' @include generics.R
#'
NULL
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Functions
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
globalVariables(
names = c('myAUC', 'p_val', 'avg_logFC'),
package = 'Seurat',
add = TRUE
)
#' Gene expression markers for all identity classes
#'
#' Finds markers (differentially expressed genes) for each of the identity classes in a dataset
#'
#' @inheritParams FindMarkers
#' @param node A node to find markers for and all its children; requires
#' \code{\link{BuildClusterTree}} to have been run previously; replaces \code{FindAllMarkersNode}
#' @param return.thresh Only return markers that have a p-value < return.thresh, or a power > return.thresh (if the test is ROC)
#'
#' @return Matrix containing a ranked list of putative markers, and associated
#' statistics (p-values, ROC score, etc.)
#'
#' @importFrom stats setNames
#'
#' @export
#'
#' @aliases FindAllMarkersNode
#' @concept differential_expression
#'
#' @examples
#' data("pbmc_small")
#' # Find markers for all clusters
#' all.markers <- FindAllMarkers(object = pbmc_small)
#' head(x = all.markers)
#' \dontrun{
#' # Pass a value to node as a replacement for FindAllMarkersNode
#' pbmc_small <- BuildClusterTree(object = pbmc_small)
#' all.markers <- FindAllMarkers(object = pbmc_small, node = 4)
#' head(x = all.markers)
#' }
#'
FindAllMarkers <- function(
object,
assay = NULL,
features = NULL,
logfc.threshold = 0.25,
test.use = 'wilcox',
slot = 'data',
min.pct = 0.1,
min.diff.pct = -Inf,
node = NULL,
verbose = TRUE,
only.pos = FALSE,
max.cells.per.ident = Inf,
random.seed = 1,
latent.vars = NULL,
min.cells.feature = 3,
min.cells.group = 3,
pseudocount.use = 1,
mean.fxn = NULL,
fc.name = NULL,
base = 2,
return.thresh = 1e-2,
densify = FALSE,
...
) {
MapVals <- function(vec, from, to) {
vec2 <- setNames(object = to, nm = from)[as.character(x = vec)]
vec2[is.na(x = vec2)] <- vec[is.na(x = vec2)]
return(unname(obj = vec2))
}
if ((test.use == "roc") && (return.thresh == 1e-2)) {
return.thresh <- 0.7
}
if (is.null(x = node)) {
idents.all <- sort(x = unique(x = Idents(object = object)))
} else {
if (!PackageCheck('ape', error = FALSE)) {
stop(cluster.ape, call. = FALSE)
}
tree <- Tool(object = object, slot = 'BuildClusterTree')
if (is.null(x = tree)) {
stop("Please run 'BuildClusterTree' before finding markers on nodes")
}
descendants <- DFT(tree = tree, node = node, include.children = TRUE)
all.children <- sort(x = tree$edge[, 2][!tree$edge[, 2] %in% tree$edge[, 1]])
descendants <- MapVals(
vec = descendants,
from = all.children,
to = tree$tip.label
)
drop.children <- setdiff(x = tree$tip.label, y = descendants)
keep.children <- setdiff(x = tree$tip.label, y = drop.children)
orig.nodes <- c(
node,
as.numeric(x = setdiff(x = descendants, y = keep.children))
)
tree <- ape::drop.tip(phy = tree, tip = drop.children)
new.nodes <- unique(x = tree$edge[, 1, drop = TRUE])
idents.all <- (tree$Nnode + 2):max(tree$edge)
}
genes.de <- list()
messages <- list()
for (i in 1:length(x = idents.all)) {
if (verbose) {
message("Calculating cluster ", idents.all[i])
}
genes.de[[i]] <- tryCatch(
expr = {
FindMarkers(
object = object,
assay = assay,
ident.1 = if (is.null(x = node)) {
idents.all[i]
} else {
tree
},
ident.2 = if (is.null(x = node)) {
NULL
} else {
idents.all[i]
},
features = features,
logfc.threshold = logfc.threshold,
test.use = test.use,
slot = slot,
min.pct = min.pct,
min.diff.pct = min.diff.pct,
verbose = verbose,
only.pos = only.pos,
max.cells.per.ident = max.cells.per.ident,
random.seed = random.seed,
latent.vars = latent.vars,
min.cells.feature = min.cells.feature,
min.cells.group = min.cells.group,
pseudocount.use = pseudocount.use,
mean.fxn = mean.fxn,
fc.name = fc.name,
base = base,
densify = densify,
...
)
},
error = function(cond) {
return(cond$message)
}
)
if (is.character(x = genes.de[[i]])) {
messages[[i]] <- genes.de[[i]]
genes.de[[i]] <- NULL
}
}
gde.all <- data.frame()
for (i in 1:length(x = idents.all)) {
if (is.null(x = unlist(x = genes.de[i]))) {
next
}
gde <- genes.de[[i]]
if (nrow(x = gde) > 0) {
if (test.use == "roc") {
gde <- subset(
x = gde,
subset = (myAUC > return.thresh | myAUC < (1 - return.thresh))
)
} else if (is.null(x = node) || test.use %in% c('bimod', 't')) {
gde <- gde[order(gde$p_val, -gde[, 2]), ]
gde <- subset(x = gde, subset = p_val < return.thresh)
}
if (nrow(x = gde) > 0) {
gde$cluster <- idents.all[i]
gde$gene <- rownames(x = gde)
}
if (nrow(x = gde) > 0) {
gde.all <- rbind(gde.all, gde)
}
}
}
if ((only.pos) && nrow(x = gde.all) > 0) {
return(subset(x = gde.all, subset = gde.all[, 2] > 0))
}
rownames(x = gde.all) <- make.unique(names = as.character(x = gde.all$gene))
if (nrow(x = gde.all) == 0) {
warning("No DE genes identified", call. = FALSE, immediate. = TRUE)
}
if (length(x = messages) > 0) {
warning("The following tests were not performed: ", call. = FALSE, immediate. = TRUE)
for (i in 1:length(x = messages)) {
if (!is.null(x = messages[[i]])) {
warning("When testing ", idents.all[i], " versus all:\n\t", messages[[i]], call. = FALSE, immediate. = TRUE)
}
}
}
if (!is.null(x = node)) {
gde.all$cluster <- MapVals(
vec = gde.all$cluster,
from = new.nodes,
to = orig.nodes
)
}
return(gde.all)
}
#' Finds markers that are conserved between the groups
#'
#' @inheritParams FindMarkers
#' @param ident.1 Identity class to define markers for
#' @param ident.2 A second identity class for comparison. If NULL (default) -
#' use all other cells for comparison.
#' @param grouping.var grouping variable
#' @param assay of assay to fetch data for (default is RNA)
#' @param meta.method method for combining p-values. Should be a function from
#' the metap package (NOTE: pass the function, not a string)
#' @param \dots parameters to pass to FindMarkers
#'
#' @return data.frame containing a ranked list of putative conserved markers, and
#' associated statistics (p-values within each group and a combined p-value
#' (such as Fishers combined p-value or others from the metap package),
#' percentage of cells expressing the marker, average differences). Name of group is appended to each
#' associated output column (e.g. CTRL_p_val). If only one group is tested in the grouping.var, max
#' and combined p-values are not returned.
#'
#' @export
#' @concept differential_expression
#'
#' @examples
#' \dontrun{
#' data("pbmc_small")
#' pbmc_small
#' # Create a simulated grouping variable
#' pbmc_small[['groups']] <- sample(x = c('g1', 'g2'), size = ncol(x = pbmc_small), replace = TRUE)
#' FindConservedMarkers(pbmc_small, ident.1 = 0, ident.2 = 1, grouping.var = "groups")
#' }
#'
FindConservedMarkers <- function(
object,
ident.1,
ident.2 = NULL,
grouping.var,
assay = 'RNA',
slot = 'data',
meta.method = metap::minimump,
verbose = TRUE,
...
) {
metap.installed <- PackageCheck("metap", error = FALSE)
if (!metap.installed[1]) {
stop(
"Please install the metap package to use FindConservedMarkers.",
"\nThis can be accomplished with the following commands: ",
"\n----------------------------------------",
"\ninstall.packages('BiocManager')",
"\nBiocManager::install('multtest')",
"\ninstall.packages('metap')",
"\n----------------------------------------",
call. = FALSE
)
}
if (!is.function(x = meta.method)) {
stop("meta.method should be a function from the metap package. Please see https://cran.r-project.org/web/packages/metap/metap.pdf for a detailed description of the available functions.")
}
object.var <- FetchData(object = object, vars = grouping.var)
object <- SetIdent(
object = object,
cells = colnames(x = object),
value = paste(Idents(object = object), object.var[, 1], sep = "_")
)
levels.split <- names(x = sort(x = table(object.var[, 1])))
num.groups <- length(levels.split)
cells <- list()
for (i in 1:num.groups) {
cells[[i]] <- rownames(
x = object.var[object.var[, 1] == levels.split[i], , drop = FALSE]
)
}
marker.test <- list()
# do marker tests
ident.2.save <- ident.2
for (i in 1:num.groups) {
level.use <- levels.split[i]
ident.use.1 <- paste(ident.1, level.use, sep = "_")
ident.use.1.exists <- ident.use.1 %in% Idents(object = object)
if (!all(ident.use.1.exists)) {
bad.ids <- ident.1[!ident.use.1.exists]
warning(
"Identity: ",
paste(bad.ids, collapse = ", "),
" not present in group ",
level.use,
". Skipping ",
level.use,
call. = FALSE,
immediate. = TRUE
)
next
}
ident.2 <- ident.2.save
cells.1 <- WhichCells(object = object, idents = ident.use.1)
if (is.null(x = ident.2)) {
cells.2 <- setdiff(x = cells[[i]], y = cells.1)
ident.use.2 <- names(x = which(x = table(Idents(object = object)[cells.2]) > 0))
ident.2 <- gsub(pattern = paste0("_", level.use), replacement = "", x = ident.use.2)
if (length(x = ident.use.2) == 0) {
stop(paste("Only one identity class present:", ident.1))
}
} else {
ident.use.2 <- paste(ident.2, level.use, sep = "_")
}
if (verbose) {
message(
"Testing group ",
level.use,
": (",
paste(ident.1, collapse = ", "),
") vs (",
paste(ident.2, collapse = ", "),
")"
)
}
ident.use.2.exists <- ident.use.2 %in% Idents(object = object)
if (!all(ident.use.2.exists)) {
bad.ids <- ident.2[!ident.use.2.exists]
warning(
"Identity: ",
paste(bad.ids, collapse = ", "),
" not present in group ",
level.use,
". Skipping ",
level.use,
call. = FALSE,
immediate. = TRUE
)
next
}
marker.test[[i]] <- FindMarkers(
object = object,
assay = assay,
slot = slot,
ident.1 = ident.use.1,
ident.2 = ident.use.2,
verbose = verbose,
...
)
names(x = marker.test)[i] <- levels.split[i]
}
marker.test <- Filter(f = Negate(f = is.null), x = marker.test)
genes.conserved <- Reduce(
f = intersect,
x = lapply(
X = marker.test,
FUN = function(x) {
return(rownames(x = x))
}
)
)
markers.conserved <- list()
for (i in 1:length(x = marker.test)) {
markers.conserved[[i]] <- marker.test[[i]][genes.conserved, ]
colnames(x = markers.conserved[[i]]) <- paste(
names(x = marker.test)[i],
colnames(x = markers.conserved[[i]]),
sep = "_"
)
}
markers.combined <- Reduce(cbind, markers.conserved)
pval.codes <- colnames(x = markers.combined)[grepl(pattern = "*_p_val$", x = colnames(x = markers.combined))]
if (length(x = pval.codes) > 1) {
markers.combined$max_pval <- apply(
X = markers.combined[, pval.codes, drop = FALSE],
MARGIN = 1,
FUN = max
)
combined.pval <- data.frame(cp = apply(
X = markers.combined[, pval.codes, drop = FALSE],
MARGIN = 1,
FUN = function(x) {
return(meta.method(x)$p)
}
))
meta.method.name <- as.character(x = formals()$meta.method)
if (length(x = meta.method.name) == 3) {
meta.method.name <- meta.method.name[3]
}
colnames(x = combined.pval) <- paste0(meta.method.name, "_p_val")
markers.combined <- cbind(markers.combined, combined.pval)
markers.combined <- markers.combined[order(markers.combined[, paste0(meta.method.name, "_p_val")]), ]
} else {
warning("Only a single group was tested", call. = FALSE, immediate. = TRUE)
}
return(markers.combined)
}
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Methods for Seurat-defined generics
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#' @param cells.1 Vector of cell names belonging to group 1
#' @param cells.2 Vector of cell names belonging to group 2
#' @param counts Count matrix if using scale.data for DE tests. This is used for
#' computing pct.1 and pct.2 and for filtering features based on fraction
#' expressing
#' @param features Genes to test. Default is to use all genes
#' @param logfc.threshold Limit testing to genes which show, on average, at least
#' X-fold difference (log-scale) between the two groups of cells. Default is 0.25
#' Increasing logfc.threshold speeds up the function, but can miss weaker signals.
#' @param test.use Denotes which test to use. Available options are:
#' \itemize{
#' \item{"wilcox"} : Identifies differentially expressed genes between two
#' groups of cells using a Wilcoxon Rank Sum test (default)
#' \item{"bimod"} : Likelihood-ratio test for single cell gene expression,
#' (McDavid et al., Bioinformatics, 2013)
#' \item{"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.
#' \item{"t"} : Identify differentially expressed genes between two groups of
#' cells using the Student's t-test.
#' \item{"negbinom"} : Identifies differentially expressed genes between two
#' groups of cells using a negative binomial generalized linear model.
#' Use only for UMI-based datasets
#' \item{"poisson"} : Identifies differentially expressed genes between two
#' groups of cells using a poisson generalized linear model.
#' Use only for UMI-based datasets
#' \item{"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.
#' \item{"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.
#' \item{"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
#' }
#' @param 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 0.1
#' @param 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
#' @param only.pos Only return positive markers (FALSE by default)
#' @param verbose Print a progress bar once expression testing begins
#' @param max.cells.per.ident Down sample each identity class to a max number.
#' Default is no downsampling. Not activated by default (set to Inf)
#' @param random.seed Random seed for downsampling
#' @param latent.vars Variables to test, used only when \code{test.use} is one of
#' 'LR', 'negbinom', 'poisson', or 'MAST'
#' @param 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
#' @param min.cells.group Minimum number of cells in one of the groups
#' @param pseudocount.use Pseudocount to add to averaged expression values when
#' calculating logFC. 1 by default.
#' @param fc.results data.frame from FoldChange
#' @param densify Convert the sparse matrix to a dense form before running the DE test. This can provide speedups but might require higher memory; default is FALSE
#'
#'
#' @importFrom Matrix rowMeans
#' @importFrom stats p.adjust
#'
#' @rdname FindMarkers
#' @concept differential_expression
#' @export
#' @method FindMarkers default
#'
FindMarkers.default <- function(
object,
slot = "data",
counts = numeric(),
cells.1 = NULL,
cells.2 = NULL,
features = NULL,
logfc.threshold = 0.25,
test.use = 'wilcox',
min.pct = 0.1,
min.diff.pct = -Inf,
verbose = TRUE,
only.pos = FALSE,
max.cells.per.ident = Inf,
random.seed = 1,
latent.vars = NULL,
min.cells.feature = 3,
min.cells.group = 3,
pseudocount.use = 1,
fc.results = NULL,
densify = FALSE,
...
) {
ValidateCellGroups(
object = object,
cells.1 = cells.1,
cells.2 = cells.2,
min.cells.group = min.cells.group
)
features <- features %||% rownames(x = object)
# reset parameters so no feature filtering is performed
if (test.use %in% DEmethods_noprefilter()) {
features <- rownames(x = object)
min.diff.pct <- -Inf
logfc.threshold <- 0
}
data <- switch(
EXPR = slot,
'scale.data' = counts,
object
)
# feature selection (based on percentages)
alpha.min <- pmax(fc.results$pct.1, fc.results$pct.2)
names(x = alpha.min) <- rownames(x = fc.results)
features <- names(x = which(x = alpha.min >= min.pct))
if (length(x = features) == 0) {
warning("No features pass min.pct threshold; returning empty data.frame")
return(fc.results[features, ])
}
alpha.diff <- alpha.min - pmin(fc.results$pct.1, fc.results$pct.2)
features <- names(
x = which(x = alpha.min >= min.pct & alpha.diff >= min.diff.pct)
)
if (length(x = features) == 0) {
warning("No features pass min.diff.pct threshold; returning empty data.frame")
return(fc.results[features, ])
}
# feature selection (based on logFC)
if (slot != "scale.data") {
total.diff <- fc.results[, 1] #first column is logFC
names(total.diff) <- rownames(fc.results)
features.diff <- if (only.pos) {
names(x = which(x = total.diff >= logfc.threshold))
} else {
names(x = which(x = abs(x = total.diff) >= logfc.threshold))
}
features <- intersect(x = features, y = features.diff)
if (length(x = features) == 0) {
warning("No features pass logfc.threshold threshold; returning empty data.frame")
return(fc.results[features, ])
}
}
# subsample cell groups if they are too large
if (max.cells.per.ident < Inf) {
set.seed(seed = random.seed)
if (length(x = cells.1) > max.cells.per.ident) {
cells.1 <- sample(x = cells.1, size = max.cells.per.ident)
}
if (length(x = cells.2) > max.cells.per.ident) {
cells.2 <- sample(x = cells.2, size = max.cells.per.ident)
}
if (!is.null(x = latent.vars)) {
latent.vars <- latent.vars[c(cells.1, cells.2), , drop = FALSE]
}
}
de.results <- PerformDE(
object = object,
cells.1 = cells.1,
cells.2 = cells.2,
features = features,
test.use = test.use,
verbose = verbose,
min.cells.feature = min.cells.feature,
latent.vars = latent.vars,
densify = densify,
...
)
de.results <- cbind(de.results, fc.results[rownames(x = de.results), , drop = FALSE])
if (only.pos) {
de.results <- de.results[de.results[, 2] > 0, , drop = FALSE]
}
if (test.use %in% DEmethods_nocorrect()) {
de.results <- de.results[order(-de.results$power, -de.results[, 1]), ]
} else {
de.results <- de.results[order(de.results$p_val, -de.results[, 1]), ]
de.results$p_val_adj = p.adjust(
p = de.results$p_val,
method = "bonferroni",
n = nrow(x = object)
)
}
return(de.results)
}
#' @rdname FindMarkers
#' @concept differential_expression
#' @export
#' @method FindMarkers Assay
#'
FindMarkers.Assay <- function(
object,
slot = "data",
cells.1 = NULL,
cells.2 = NULL,
features = NULL,
logfc.threshold = 0.25,
test.use = 'wilcox',
min.pct = 0.1,
min.diff.pct = -Inf,
verbose = TRUE,
only.pos = FALSE,
max.cells.per.ident = Inf,
random.seed = 1,
latent.vars = NULL,
min.cells.feature = 3,
min.cells.group = 3,
pseudocount.use = 1,
mean.fxn = NULL,
fc.name = NULL,
base = 2,
densify = FALSE,
...
) {
data.slot <- ifelse(
test = test.use %in% DEmethods_counts(),
yes = 'counts',
no = slot
)
data.use <- GetAssayData(object = object, slot = data.slot)
counts <- switch(
EXPR = data.slot,
'scale.data' = GetAssayData(object = object, slot = "counts"),
numeric()
)
fc.results <- FoldChange(
object = object,
slot = data.slot,
cells.1 = cells.1,
cells.2 = cells.2,
features = features,
pseudocount.use = pseudocount.use,
mean.fxn = mean.fxn,
fc.name = fc.name,
base = base
)
de.results <- FindMarkers(
object = data.use,
slot = data.slot,
counts = counts,
cells.1 = cells.1,
cells.2 = cells.2,
features = features,
logfc.threshold = logfc.threshold,
test.use = test.use,
min.pct = min.pct,
min.diff.pct = min.diff.pct,
verbose = verbose,
only.pos = only.pos,
max.cells.per.ident = max.cells.per.ident,
random.seed = random.seed,
latent.vars = latent.vars,
min.cells.feature = min.cells.feature,
min.cells.group = min.cells.group,
pseudocount.use = pseudocount.use,
fc.results = fc.results,
densify = densify,
...
)
return(de.results)
}
#' @importFrom Matrix rowMeans
#' @rdname FindMarkers
#' @concept differential_expression
#' @export
#' @method FindMarkers DimReduc
#'
FindMarkers.DimReduc <- function(
object,
cells.1 = NULL,
cells.2 = NULL,
features = NULL,
logfc.threshold = 0.25,
test.use = "wilcox",
min.pct = 0.1,
min.diff.pct = -Inf,
verbose = TRUE,
only.pos = FALSE,
max.cells.per.ident = Inf,
random.seed = 1,
latent.vars = NULL,
min.cells.feature = 3,
min.cells.group = 3,
pseudocount.use = 1,
mean.fxn = rowMeans,
fc.name = NULL,
densify = FALSE,
...
) {
if (test.use %in% DEmethods_counts()) {
stop("The following tests cannot be used for differential expression on a reduction as they assume a count model: ",
paste(DEmethods_counts(), collapse=", "))
}
data <- t(x = Embeddings(object = object))
ValidateCellGroups(
object = data,
cells.1 = cells.1,
cells.2 = cells.2,
min.cells.group = min.cells.group
)
features <- features %||% rownames(x = data)
# reset parameters so no feature filtering is performed
if (test.use %in% DEmethods_noprefilter()) {
features <- rownames(x = data)
min.diff.pct <- -Inf
logfc.threshold <- 0
}
fc.results <- FoldChange(
object = object,
cells.1 = cells.1,
cells.2 = cells.2,
features = features,
mean.fxn = mean.fxn,
fc.name = fc.name
)
# subsample cell groups if they are too large
if (max.cells.per.ident < Inf) {
set.seed(seed = random.seed)
if (length(x = cells.1) > max.cells.per.ident) {
cells.1 <- sample(x = cells.1, size = max.cells.per.ident)
}
if (length(x = cells.2) > max.cells.per.ident) {
cells.2 <- sample(x = cells.2, size = max.cells.per.ident)
}
if (!is.null(x = latent.vars)) {
latent.vars <- latent.vars[c(cells.1, cells.2), , drop = FALSE]
}
}
de.results <- PerformDE(
object = data,
cells.1 = cells.1,
cells.2 = cells.2,
features = features,
test.use = test.use,
verbose = verbose,
min.cells.feature = min.cells.feature,
latent.vars = latent.vars,
densify = densify,
...
)
de.results <- cbind(de.results, fc.results)
if (only.pos) {
de.results <- de.results[de.results$avg_diff > 0, , drop = FALSE]
}
if (test.use %in% DEmethods_nocorrect()) {
de.results <- de.results[order(-de.results$power, -de.results$avg_diff), ]
} else {
de.results <- de.results[order(de.results$p_val, -de.results$avg_diff), ]
de.results$p_val_adj = p.adjust(
p = de.results$p_val,
method = "bonferroni",
n = nrow(x = object)
)
}
return(de.results)
}
#' @param ident.1 Identity class to define markers for; pass an object of class
#' \code{phylo} or 'clustertree' to find markers for a node in a cluster tree;
#' passing 'clustertree' requires \code{\link{BuildClusterTree}} to have been run
#' @param ident.2 A second identity class for comparison; if \code{NULL},
#' use all other cells for comparison; if an object of class \code{phylo} or
#' 'clustertree' is passed to \code{ident.1}, must pass a node to find markers for
#' @param reduction Reduction to use in differential expression testing - will test for DE on cell embeddings
#' @param group.by Regroup cells into a different identity class prior to performing differential expression (see example)
#' @param subset.ident Subset a particular identity class prior to regrouping. Only relevant if group.by is set (see example)
#' @param assay Assay to use in differential expression testing
#' @param slot Slot to pull data from; note that if \code{test.use} is "negbinom", "poisson", or "DESeq2",
#' \code{slot} will be set to "counts"
#' @param mean.fxn Function to use for fold change or average difference calculation.
#' If NULL, the appropriate function will be chose according to the slot used
#' @param fc.name Name of the fold change, average difference, or custom function column
#' in the output data.frame. If NULL, the fold change column will be named
#' according to the logarithm base (eg, "avg_log2FC"), or if using the scale.data
#' slot "avg_diff".
#' @param base The base with respect to which logarithms are computed.
#'
#' @rdname FindMarkers
#' @concept differential_expression
#' @export
#' @method FindMarkers Seurat
#'
FindMarkers.Seurat <- function(
object,
ident.1 = NULL,
ident.2 = NULL,
group.by = NULL,
subset.ident = NULL,
assay = NULL,
slot = 'data',
reduction = NULL,
features = NULL,
logfc.threshold = 0.25,
test.use = "wilcox",
min.pct = 0.1,
min.diff.pct = -Inf,
verbose = TRUE,
only.pos = FALSE,
max.cells.per.ident = Inf,
random.seed = 1,
latent.vars = NULL,
min.cells.feature = 3,
min.cells.group = 3,
pseudocount.use = 1,
mean.fxn = NULL,
fc.name = NULL,
base = 2,
densify = FALSE,
...
) {
if (!is.null(x = group.by)) {
if (!is.null(x = subset.ident)) {
object <- subset(x = object, idents = subset.ident)
}
Idents(object = object) <- group.by
}
if (!is.null(x = assay) && !is.null(x = reduction)) {
stop("Please only specify either assay or reduction.")
}
if (length(x = ident.1) == 0) {
stop("At least 1 ident must be specified in `ident.1`")
}
# select which data to use
if (is.null(x = reduction)) {
assay <- assay %||% DefaultAssay(object = object)
data.use <- object[[assay]]
cellnames.use <- colnames(x = data.use)
} else {
data.use <- object[[reduction]]
cellnames.use <- rownames(data.use)
}
cells <- IdentsToCells(
object = object,
ident.1 = ident.1,
ident.2 = ident.2,
cellnames.use = cellnames.use
)
# fetch latent.vars
if (!is.null(x = latent.vars)) {
latent.vars <- FetchData(
object = object,
vars = latent.vars,
cells = c(cells$cells.1, cells$cells.2)
)
}
# check normalization method
norm.command <- paste0("NormalizeData.", assay)
if (norm.command %in% Command(object = object) && is.null(x = reduction)) {
norm.method <- Command(
object = object,
command = norm.command,
value = "normalization.method"
)
if (norm.method != "LogNormalize") {
mean.fxn <- function(x) {
return(log(x = rowMeans(x = x) + pseudocount.use, base = base))
}
}
}
de.results <- FindMarkers(
object = data.use,
slot = slot,
cells.1 = cells$cells.1,
cells.2 = cells$cells.2,
features = features,
logfc.threshold = logfc.threshold,
test.use = test.use,
min.pct = min.pct,
min.diff.pct = min.diff.pct,
verbose = verbose,
only.pos = only.pos,
max.cells.per.ident = max.cells.per.ident,
random.seed = random.seed,
latent.vars = latent.vars,
min.cells.feature = min.cells.feature,
min.cells.group = min.cells.group,
pseudocount.use = pseudocount.use,
mean.fxn = mean.fxn,
base = base,
fc.name = fc.name,
densify = densify,
...
)
return(de.results)
}
#' @param cells.1 Vector of cell names belonging to group 1
#' @param cells.2 Vector of cell names belonging to group 2
#' @param features Features to calculate fold change for.
#' If NULL, use all features
#' @importFrom Matrix rowSums
#' @rdname FoldChange
#' @concept differential_expression
#' @export
#' @method FoldChange default
FoldChange.default <- function(
object,
cells.1,
cells.2,
mean.fxn,
fc.name,
features = NULL,
...
) {
features <- features %||% rownames(x = object)
# Calculate percent expressed
thresh.min <- 0
pct.1 <- round(
x = rowSums(x = object[features, cells.1, drop = FALSE] > thresh.min) /
length(x = cells.1),
digits = 3
)
pct.2 <- round(
x = rowSums(x = object[features, cells.2, drop = FALSE] > thresh.min) /
length(x = cells.2),
digits = 3
)
# Calculate fold change
data.1 <- mean.fxn(object[features, cells.1, drop = FALSE])
data.2 <- mean.fxn(object[features, cells.2, drop = FALSE])
fc <- (data.1 - data.2)
fc.results <- as.data.frame(x = cbind(fc, pct.1, pct.2))
colnames(fc.results) <- c(fc.name, "pct.1", "pct.2")
return(fc.results)
}
#' @importFrom Matrix rowMeans
#' @rdname FoldChange
#' @concept differential_expression
#' @export
#' @method FoldChange Assay
FoldChange.Assay <- function(
object,
cells.1,
cells.2,
features = NULL,
slot = "data",
pseudocount.use = 1,
fc.name = NULL,
mean.fxn = NULL,
base = 2,
...
) {
data <- GetAssayData(object = object, slot = slot)
mean.fxn <- mean.fxn %||% switch(
EXPR = slot,
'data' = function(x) {
return(log(x = rowMeans(x = expm1(x = x)) + pseudocount.use, base = base))
},
'scale.data' = rowMeans,
function(x) {
return(log(x = rowMeans(x = x) + pseudocount.use, base = base))
}
)
# Omit the decimal value of e from the column name if base == exp(1)
base.text <- ifelse(
test = base == exp(1),
yes = "",
no = base
)
fc.name <- fc.name %||% ifelse(
test = slot == "scale.data",
yes = "avg_diff",
no = paste0("avg_log", base.text, "FC")
)
FoldChange(
object = data,
cells.1 = cells.1,
cells.2 = cells.2,
features = features,
mean.fxn = mean.fxn,
fc.name = fc.name
)
}
#' @importFrom Matrix rowMeans
#' @rdname FoldChange
#' @concept differential_expression
#' @export
#' @method FoldChange DimReduc
FoldChange.DimReduc <- function(
object,
cells.1,
cells.2,
features = NULL,
slot = NULL,
pseudocount.use = NULL,
fc.name = NULL,
mean.fxn = NULL,
...
) {
mean.fxn <- mean.fxn %||% rowMeans