Finds markers (differentially expressed genes) for identity classes

FindMarkers(object, ...)

# S3 method for default
FindMarkers(
  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,
  ...
)

# S3 method for Assay
FindMarkers(
  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,
  ...
)

# S3 method for DimReduc
FindMarkers(
  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,
  ...
)

# S3 method for Seurat
FindMarkers(
  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,
  ...
)

Arguments

object

An object

...

Arguments passed to other methods and to specific DE methods

slot

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

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

cells.1

Vector of cell names belonging to group 1

cells.2

Vector of cell names belonging to group 2

features

Genes to test. Default is to use all genes

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.

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)

  • "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

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

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

verbose

Print a progress bar once expression testing begins

only.pos

Only return positive markers (FALSE 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)

random.seed

Random seed for downsampling

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

pseudocount.use

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

fc.results

data.frame from FoldChange

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

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

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".

base

The base with respect to which logarithms are computed.

ident.1

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

ident.2

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

group.by

Regroup cells into a different identity class prior to performing differential expression (see example)

subset.ident

Subset a particular identity class prior to regrouping. Only relevant if group.by is set (see example)

assay

Assay to use in differential expression testing

reduction

Reduction to use in differential expression testing - will test for DE on cell embeddings

Value

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

Details

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.

References

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

See also

FoldChange

Examples

data("pbmc_small") # Find markers for cluster 2 markers <- FindMarkers(object = pbmc_small, ident.1 = 2) head(x = markers)
#> p_val avg_log2FC pct.1 pct.2 p_val_adj #> HLA-DPB1 2.667056e-08 2.523409 0.947 0.410 6.134228e-06 #> MS4A1 7.532290e-08 6.540876 0.526 0.033 1.732427e-05 #> HLA-DQB1 2.436852e-07 2.956945 0.789 0.246 5.604759e-05 #> HLA-DRB1 1.341885e-06 2.393562 0.842 0.410 3.086335e-04 #> HLA-DRA 4.618445e-06 2.614768 0.895 0.623 1.062242e-03 #> TCL1A 6.943137e-06 5.886009 0.368 0.016 1.596922e-03
# 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)
#> p_val avg_log2FC pct.1 pct.2 p_val_adj #> GSTP1 0.01601528 2.539192 0.7 0.111 1 #> LINC00936 0.02048683 4.090856 0.5 0.000 1 #> TPM4 0.02048683 4.381751 0.5 0.000 1 #> LGALS2 0.04515259 4.300590 0.4 0.000 1 #> IFI30 0.04515259 4.676111 0.4 0.000 1 #> RHOC 0.04515259 3.933927 0.4 0.000 1
# Pass 'clustertree' or an object of class phylo to ident.1 and # a node to ident.2 as a replacement for FindMarkersNode pbmc_small <- BuildClusterTree(object = pbmc_small) markers <- FindMarkers(object = pbmc_small, ident.1 = 'clustertree', ident.2 = 5) head(x = markers)
#> p_val avg_log2FC pct.1 pct.2 p_val_adj #> S100A8 2.040476e-08 7.719466 0.96 0.053 4.693094e-06 #> TYROBP 3.997264e-08 3.468113 1.00 0.263 9.193708e-06 #> S100A9 3.142412e-07 6.631748 0.88 0.105 7.227549e-05 #> FCER1G 3.173930e-07 2.879840 1.00 0.316 7.300038e-05 #> TYMP 1.145674e-06 2.576133 1.00 0.263 2.635050e-04 #> S100A11 5.701667e-06 2.263775 1.00 0.421 1.311383e-03