Find Expressed Markers

Usage

FindExpressedMarkers(
  object,
  ident.1 = NULL,
  ident.2 = NULL,
  cells.1 = NULL,
  cells.2 = NULL,
  features = NULL,
  assay = NULL,
  layer = "data",
  min.expression = 0,
  test.use = "wilcox",
  logfc.threshold = 0.25,
  base = 2,
  pseudocount.use = 1,
  mean.fxn = NULL,
  fc.name = NULL,
  min.pct = 0.1,
  min.diff.pct = -Inf,
  max.cells.per.ident = Inf,
  latent.vars = NULL,
  only.pos = FALSE,
  min.cells.group = 3,
  min.cells.feature = 3,
  norm.method = "LogNormalize",
  seed = 11,
  verbose = TRUE,
  ...
)

Arguments

object

An object

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

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

assay

Assay to use in differential expression testing

layer

The layer used.

min.expression

The min.expression used.

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

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 0.1 Increasing logfc.threshold speeds up the function, but can miss weaker signals. If the slot parameter is "scale.data" no filtering is performed.

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.

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".

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'

only.pos

Only return positive markers (FALSE by default)

min.cells.group

Minimum number of cells in one of the groups

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

norm.method

Normalization method for fold change calculation when slot is “data”

seed

The seed used.

verbose

Print a progress bar once expression testing begins

...

Arguments passed to other methods and to specific DE methods

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-11-13 11:59:59] Start standard scop workflow...
#> ℹ [2025-11-13 11:59:59] Checking a list of <Seurat> object...
#> ! [2025-11-13 11:59:59] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 11:59:59] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 12:00:01] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 12:00:02] Use the separate HVF from srt_list
#> ℹ [2025-11-13 12:00:02] Number of available HVF: 2000
#> ℹ [2025-11-13 12:00:02] Finished check
#> ℹ [2025-11-13 12:00:03] Perform `Seurat::ScaleData()`
#> ℹ [2025-11-13 12:00:03] Perform pca linear dimension reduction
#> StandardPC_ 1 
#> Positive:  Aplp1, Cpe, Gnas, Fam183b, Map1b, Hmgn3, Pcsk1n, Chga, Tuba1a, Bex2 
#> 	   Syt13, Isl1, 1700086L19Rik, Pax6, Chgb, Scgn, Rbp4, Scg3, Gch1, Camk2n1 
#> 	   Cryba2, Pcsk2, Pyy, Tspan7, Mafb, Hist3h2ba, Dbpht2, Abcc8, Rap1b, Slc38a5 
#> Negative:  Spp1, Anxa2, Sparc, Dbi, 1700011H14Rik, Wfdc2, Gsta3, Adamts1, Clu, Mgst1 
#> 	   Bicc1, Ldha, Vim, Cldn3, Cyr61, Rps2, Mt1, Ptn, Phgdh, Nudt19 
#> 	   Smtnl2, Smco4, Habp2, Mt2, Col18a1, Rpl12, Galk1, Cldn10, Acot1, Ccnd1 
#> StandardPC_ 2 
#> Positive:  Rbp4, Tagln2, Tuba1b, Fkbp2, Pyy, Pcsk2, Iapp, Tmem27, Meis2, Tubb4b 
#> 	   Pcsk1n, Dbpht2, Rap1b, Dynll1, Tubb2a, Sdf2l1, Scgn, 1700086L19Rik, Scg2, Abcc8 
#> 	   Atp1b1, Hspa5, Fam183b, Papss2, Slc38a5, Scg3, Mageh1, Tspan7, Ppp1r1a, Ociad2 
#> Negative:  Neurog3, Btbd17, Gadd45a, Ppp1r14a, Neurod2, Sox4, Smarcd2, Mdk, Pax4, Btg2 
#> 	   Sult2b1, Hes6, Grasp, Igfbpl1, Gpx2, Cbfa2t3, Foxa3, Shf, Mfng, Tmsb4x 
#> 	   Amotl2, Gdpd1, Cdc14b, Epb42, Rcor2, Cotl1, Upk3bl, Rbfox3, Cldn6, Cer1 
#> StandardPC_ 3 
#> Positive:  Nusap1, Top2a, Birc5, Aurkb, Cdca8, Pbk, Mki67, Tpx2, Plk1, Ccnb1 
#> 	   2810417H13Rik, Incenp, Cenpf, Ccna2, Prc1, Racgap1, Cdk1, Aurka, Cdca3, Hmmr 
#> 	   Spc24, Kif23, Sgol1, Cenpe, Cdc20, Hist1h1b, Cdca2, Mxd3, Kif22, Ska1 
#> Negative:  Anxa5, Pdzk1ip1, Acot1, Tpm1, Anxa2, Dcdc2a, Capg, Sparc, Ttr, Pamr1 
#> 	   Clu, Cxcl12, Ndrg2, Hnf1aos1, Gas6, Gsta3, Krt18, Ces1d, Atp1b1, Muc1 
#> 	   Hhex, Acadm, Spp1, Enpp2, Bcl2l14, Sat1, Smtnl2, 1700011H14Rik, Tgm2, Fam159a 
#> StandardPC_ 4 
#> Positive:  Glud1, Tm4sf4, Akr1c19, Cldn4, Runx1t1, Fev, Pou3f4, Gm43861, Pgrmc1, Arx 
#> 	   Cd200, Lrpprc, Hmgn3, Ppp1r14c, Pam, Etv1, Tsc22d1, Slc25a5, Akap17b, Pgf 
#> 	   Fam43a, Emb, Jun, Krt8, Dnajc12, Mid1ip1, Ids, Rgs17, Uchl1, Alcam 
#> Negative:  Ins2, Ins1, Ppp1r1a, Nnat, Calr, Sytl4, Sdf2l1, Iapp, Pdia6, Mapt 
#> 	   G6pc2, C2cd4b, Npy, Gng12, P2ry1, Ero1lb, Adra2a, Papss2, Arhgap36, Fam151a 
#> 	   Dlk1, Creld2, Gip, Tmem215, Gm27033, Cntfr, Prss53, C2cd4a, Lyve1, Ociad2 
#> StandardPC_ 5 
#> Positive:  Pdx1, Nkx6-1, Npepl1, Cldn4, Cryba2, Fev, Jun, Chgb, Gng12, Adra2a 
#> 	   Mnx1, Sytl4, Pdk3, Gm27033, Nnat, Chga, Ins2, 1110012L19Rik, Enho, Krt7 
#> 	   Mlxipl, Tmsb10, Flrt1, Pax4, Tubb3, Prrg2, Gars, Frzb, BC023829, Gm2694 
#> Negative:  Irx2, Irx1, Gcg, Ctxn2, Tmem27, Ctsz, Tmsb15l, Nap1l5, Pou6f2, Gria2 
#> 	   Ghrl, Peg10, Smarca1, Arx, Lrpap1, Rgs4, Ttr, Gast, Tmsb15b2, Serpina1b 
#> 	   Slc16a10, Wnk3, Ly6e, Auts2, Sct, Arg1, Dusp10, Sphkap, Dock11, Edn3 
#> ℹ [2025-11-13 12:00:04] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-11-13 12:00:04] Reorder clusters...
#> ℹ [2025-11-13 12:00:04] Perform umap nonlinear dimension reduction
#> ℹ [2025-11-13 12:00:04] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 12:00:04] UMAP will return its model
#> ℹ [2025-11-13 12:00:08] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 12:00:08] UMAP will return its model
#> ✔ [2025-11-13 12:00:11] Run scop standard workflow done
markers <- FindExpressedMarkers(
  pancreas_sub,
  cells.1 = SeuratObject::WhichCells(
    pancreas_sub,
    expression = Phase == "G2M"
  )
)
#> ◌ [2025-11-13 12:00:12] Installing: limma...
#>  
#> → Will install 2 packages.
#> → All 2 packages (0 B) are cached.
#> + limma     3.66.0 [bld][cmp]
#> + statmod   1.5.1  
#>   
#> ℹ No downloads are needed, 2 pkgs are cached
#> ✔ Installed statmod 1.5.1  (1s)
#> ℹ Building limma 3.66.0
#> ✔ Built limma 3.66.0 (9.9s)
#> ✔ Installed limma 3.66.0  (44ms)
#> ✔ 1 pkg + 1 dep: added 2 [11.8s]
#> ✔ [2025-11-13 12:00:24] limma installed successfully
#> ℹ [2025-11-13 12:00:24] Using 1 core
#> ⠙ [2025-11-13 12:00:24] Running [1/6145] ETA: 37s
#> ⠹ [2025-11-13 12:00:24] Running [1263/6145] ETA:  3s
#> ✔ [2025-11-13 12:00:24] Completed 6145 tasks in 3.4s
#> 
#> ℹ [2025-11-13 12:00:24] Building results
head(markers)
#>               p_val avg_log2FC pct.1 pct.2    p_val_adj
#> Hmgb2  1.964385e-37  2.1638592 1.000 0.526 3.142624e-33
#> Tuba1b 3.652521e-33  1.8756996 0.986 0.801 5.843303e-29
#> Ran    2.573020e-31  1.2068807 1.000 0.946 4.116317e-27
#> H2afx  9.845758e-31  1.4231361 0.971 0.519 1.575124e-26
#> Ptma   3.541885e-30  0.9021115 1.000 0.999 5.666307e-26
#> Tubb5  1.550857e-29  1.4861586 1.000 0.940 2.481062e-25

FeatureStatPlot(
  pancreas_sub,
  rownames(markers)[1],
  group.by = "Phase",
  add_point = TRUE
)