Identify Marker Features in Bulk Expression Data — find_markers_in

Identifies informative marker features across groups from bulk gene expression or signature score matrices using Seurat workflows. Performs feature selection, scaling, PCA, clustering, and marker discovery.

Usage

find_markers_in_bulk(
  pdata,
  eset,
  group,
  id_pdata = "ID",
  nfeatures = 2000,
  top_n = 20,
  thresh.use = 0.25,
  only.pos = TRUE,
  min.pct = 0.25,
  npcs = 30
)

Arguments

pdata: Data frame. Sample metadata.
eset: Matrix. Gene expression or signature score matrix.
group: Character. Column name in pdata specifying grouping variable.
id_pdata: Character. Column name for sample IDs. Default is "ID".
nfeatures: Integer. Number of top variable features to select. Default is 2000.
top_n: Integer. Number of top markers to retain per cluster. Default is 20.
thresh.use: Numeric. Threshold for marker selection. Default is 0.25.
only.pos: Logical. Whether to retain only positive markers. Default is TRUE.
min.pct: Numeric. Minimum expression percentage threshold. Default is 0.25.
npcs: Integer. Number of principal components to use. Default is 30.

Value

List with components: `sce` (Seurat object), `markers` (all markers), `top_markers` (top markers per group).

Examples

eset_tme_stad <- load_data("eset_tme_stad")
#> ℹ Trying mirror 1/4: <https://github.com>
#> ✔ Download complete: "eset_tme_stad"
colnames(eset_tme_stad) <- substring(colnames(eset_tme_stad), 1, 12)
pdata_sig_tme <- load_data("pdata_sig_tme")
#> ℹ Trying mirror 1/4: <https://github.com>
#> ✔ Download complete: "pdata_sig_tme"
# \donttest{
res <- find_markers_in_bulk(
  pdata = pdata_sig_tme, eset = eset_tme_stad,
  group = "TMEcluster"
)
#> Using Seurat v5+ workflow
#> Normalizing layer: counts
#> Centering and scaling data matrix
#> Finding variable features for layer counts
#> PC_ 1 
#> Positive:  HSPB1, CNN1, ACTG2, TAGLN, ACTA2, TWIST2, RPA4, FOXF1, TNS1, TPM1 
#> 	   ZEB1, ROR2, VIM, TWIST1, CLDN3, PXDC1, CDKN2A, GTF2H5, CTGF, AXL 
#> 	   FSTL3, PER1, NEIL1, ZEB2, HLA-A, MGMT, SHFM1, FAM101B, GATA6, CDKN1A 
#> Negative:  TOPBP1, MSH2, POLE, MSH6, PRKDC, BRCA1, TDP1, RAD54L, BRIP1, DCLRE1A 
#> 	   FANCD2, FANCI, PALB2, GTF2H3, CTPS1, FANCM, FANCA, RIF1, E2F3, POLQ 
#> 	   ERCC3, XRCC5, DCLRE1B, FANCB, EXO1, RPA1, DDB1, XRCC2, CHEK1, BRCA2 
#> PC_ 2 
#> Positive:  RECQL4, RPA3, EME1, NUDT1, RAD54B, RDM1, PCNA, H2AFX, RAD51, EXO1 
#> 	   FEN1, CHEK2, SHFM1, RAD54L, CCNE1, XRCC2, POLQ, CLDN7, HIST1H2BL, CHEK1 
#> 	   FANCI, NEIL3, FANCG, BLM, CHAF1A, MUTYH, CDH1, FANCD2, FANCA, CLDN4 
#> Negative:  ZEB1, ZEB2, TNS1, ACTA2, TAGLN, AXL, VIM, ADAM19, ROR2, FOXF1 
#> 	   CNN1, TWIST2, TPM1, ACTG2, CTGF, PDCD1LG2, PER1, FAP, SH3PXD2A, POLK 
#> 	   TGFBI, FAM101B, REV3L, HAVCR2, COL4A1, ATM, ERCC4, HELQ, XPC, FSTL3 
#> PC_ 3 
#> Positive:  GZMB, LAG3, CXCL10, GZMA, PRF1, IFNG, CXCL9, CD8A, TAP1, PDCD1 
#> 	   TBX21, HAVCR2, HLA-C, HLA-B, TIGIT, CD274, HLA-A, B2M, PDCD1LG2, H2AFX 
#> 	   TAP2, MPG, CTLA4, DUT, NUDT1, NTHL1, ALKBH2, PCNA, RAD23A, RPA3 
#> Negative:  NEIL1, LIG4, MSH5, SHPRH, FAN1, POLI, ERCC6, HLTF, ATM, MRE11A 
#> 	   RAD52, MLH3, HUS1, TP53BP1, ERCC5, UVSSA, CLK2, REV3L, POLK, WRN 
#> 	   TREX1, RPA4, RAD50, GTF2H4, PMS1, MSH3, LIG3, SH3PXD2A, SPRTN, ATR 
#> PC_ 4 
#> Positive:  IFNG, B2M, GZMA, ERCC5, CD274, RAD17, CXCL9, RRM2B, CXCL10, CCNH 
#> 	   UBE2B, TIGIT, GZMB, MDM2, FBXW7, CTLA4, FANCL, PMS1, TBX21, LIG4 
#> 	   TAP2, RB1, NBN, ATM, CD8A, GEN1, POLK, ATR, WRN, PDCD1LG2 
#> Negative:  NTHL1, MPG, ERCC1, PRPF19, HSPB1, FSTL3, RAD23A, MUS81, MAD2L2, TWIST1 
#> 	   ERCC2, CLDN3, PNKP, ROR2, H2AFX, HIST1H2BL, SEMA7A, XAB2, SMUG1, HIST2H2BF 
#> 	   NUDT1, TAGLN, OGG1, POLL, LOXL2, SH3PXD2A, XRCC1, TWIST2, POLM, FOXF1 
#> PC_ 5 
#> Positive:  UBE2B, UBE2V2, UBE2N, CDK7, APEX1, MNAT1, FAP, RAD51C, POLB, TGFBI 
#> 	   CCNH, ADAM12, CETN2, RAD1, TDP2, ALKBH2, LOXL2, GTF2H5, XRCC4, TWIST1 
#> 	   XRCC6, PARP2, XRCC5, UBE2A, ERCC8, RNF8, RAD23B, FSTL3, RPA2, CHEK1 
#> Negative:  EME2, RECQL5, TBX21, PDCD1, PNKP, TIGIT, POLM, NEIL1, UVSSA, XRCC3 
#> 	   POLG, ATRIP, CD8A, MSH5, FBXW7, RAD9A, GATA6, PER1, ENDOV, MUTYH 
#> 	   CTLA4, DCLRE1C, POLD1, ATM, POLE, HLA-A, RAD52, PRF1, XAB2, DDB2 
#> Calculating cluster IA
#> For a (much!) faster implementation of the Wilcoxon Rank Sum Test,
#> (default method for FindMarkers) please install the presto package
#> --------------------------------------------
#> install.packages('devtools')
#> devtools::install_github('immunogenomics/presto')
#> --------------------------------------------
#> After installation of presto, Seurat will automatically use the more 
#> efficient implementation (no further action necessary).
#> This message will be shown once per session
#> Calculating cluster IE
#> Calculating cluster IS
# Extract top markers per cluster using base R
top_markers <- res$top_markers
# }