Finds markers (differentially expressed genes) for identity classes
FindMarkers(object, ...)# S3 method for default
FindMarkers(
object,
slot = "data",
cells.1 = NULL,
cells.2 = NULL,
features = NULL,
logfc.threshold = 0.1,
test.use = "wilcox",
min.pct = 0.01,
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,
fc.results = NULL,
densify = FALSE,
...
)
# S3 method for Assay
FindMarkers(
object,
slot = "data",
cells.1 = NULL,
cells.2 = NULL,
features = NULL,
test.use = "wilcox",
fc.slot = "data",
pseudocount.use = 1,
norm.method = NULL,
mean.fxn = NULL,
fc.name = NULL,
base = 2,
...
)
# S3 method for SCTAssay
FindMarkers(
object,
cells.1 = NULL,
cells.2 = NULL,
features = NULL,
test.use = "wilcox",
pseudocount.use = 1,
slot = "data",
fc.slot = "data",
mean.fxn = NULL,
fc.name = NULL,
base = 2,
recorrect_umi = TRUE,
...
)
# S3 method for DimReduc
FindMarkers(
object,
cells.1 = NULL,
cells.2 = NULL,
features = NULL,
logfc.threshold = 0.1,
test.use = "wilcox",
min.pct = 0.01,
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,
densify = FALSE,
mean.fxn = rowMeans,
fc.name = NULL,
...
)
# S3 method for Seurat
FindMarkers(
object,
latent.vars = NULL,
ident.1 = NULL,
ident.2 = NULL,
group.by = NULL,
subset.ident = NULL,
assay = NULL,
reduction = NULL,
...
)
data.frame with a ranked list of putative markers as rows, and associated
statistics as columns (p-values, ROC score, etc., depending on the test used (test.use)). The following columns are always present:
avg_logFC: log fold-chage of the average expression between the two groups. Positive values indicate that the gene is more highly expressed in the first group
pct.1: The percentage of cells where the gene is detected in the first group
pct.2: The percentage of cells where the gene is detected in the second group
p_val_adj: Adjusted p-value, based on bonferroni correction using all genes in the dataset
An object
Arguments passed to other methods and to specific DE methods
Slot to pull data from; note that if test.use is
"negbinom", "poisson", or "DESeq2", slot will be set to "counts"
Vector of cell names belonging to group 1
Vector of cell names belonging to group 2
Genes to test. Default is to use all genes
Limit testing to genes which show, on average, at least
X-fold difference (log-scale) between the two groups of cells. Default is 0.1
Increasing logfc.threshold speeds up the function, but can miss weaker signals.
If the slot parameter is "scale.data" no filtering is performed.
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 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
only test genes that show a minimum difference in the fraction of detection between the two groups. Set to -Inf by default
Print a progress bar once expression testing begins
Only return positive markers (FALSE by default)
Down sample each identity class to a max number. Default is no downsampling. Not activated by default (set to Inf)
Random seed for downsampling
Variables to test, used only when test.use is one of
'LR', 'negbinom', 'poisson', or 'MAST'
Minimum number of cells expressing the feature in at least one of the two groups, currently only used for poisson and negative binomial tests
Minimum number of cells in one of the groups
data.frame from FoldChange
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
Slot used to calculate fold-change - will also affect the
default for mean.fxn, see below for more details.
Pseudocount to add to averaged expression values when calculating logFC. 1 by default.
Normalization method for fold change calculation when
slot is “data”
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.
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".
The base with respect to which logarithms are computed.
Recalculate corrected UMI counts using minimum of the median UMIs when performing DE using multiple SCT objects; default is TRUE
Identity class to define markers for; pass an object of class
phylo or 'clustertree' to find markers for a node in a cluster tree;
passing 'clustertree' requires BuildClusterTree to have been run
A second identity class for comparison; if NULL,
use all other cells for comparison; if an object of class phylo or
'clustertree' is passed to ident.1, must pass a node to find markers for
Regroup cells into a different identity class prior to performing differential expression (see example)
Subset a particular identity class prior to regrouping. Only relevant if group.by is set (see example)
Assay to use in differential expression testing
Reduction to use in differential expression testing - will test for DE on cell embeddings
p-value adjustment is performed using bonferroni correction based on the total number of genes in the dataset. Other correction methods are not recommended, as Seurat pre-filters genes using the arguments above, reducing the number of tests performed. Lastly, as Aaron Lun has pointed out, p-values should be interpreted cautiously, as the genes used for clustering are the same genes tested for differential expression.
McDavid A, Finak G, Chattopadyay PK, et al. Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments. Bioinformatics. 2013;29(4):461-467. doi:10.1093/bioinformatics/bts714
Trapnell C, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology volume 32, pages 381-386 (2014)
Andrew McDavid, Greg Finak and Masanao Yajima (2017). MAST: Model-based Analysis of Single Cell Transcriptomics. R package version 1.2.1. https://github.com/RGLab/MAST/
Love MI, Huber W and Anders S (2014). "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2." Genome Biology. https://bioconductor.org/packages/release/bioc/html/DESeq2.html
FoldChange
if (FALSE) {
data("pbmc_small")
# Find markers for cluster 2
markers <- FindMarkers(object = pbmc_small, ident.1 = 2)
head(x = markers)
# Take all cells in cluster 2, and find markers that separate cells in the 'g1' group (metadata
# variable 'group')
markers <- FindMarkers(pbmc_small, ident.1 = "g1", group.by = 'groups', subset.ident = "2")
head(x = markers)
# Pass 'clustertree' or an object of class phylo to ident.1 and
# a node to ident.2 as a replacement for FindMarkersNode
if (requireNamespace("ape", quietly = TRUE)) {
pbmc_small <- BuildClusterTree(object = pbmc_small)
markers <- FindMarkers(object = pbmc_small, ident.1 = 'clustertree', ident.2 = 5)
head(x = markers)
}
}
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