LRcellTypeMarkers: Marker gene information for LRcell R Bioconductor package using Bioconductor’s ExperimentHub
Download cell-type specific marker gene information
LRcellTypeMarkers currently provides gene enrichment scores for mouse
whole brain and human prefrontal cortex. These gene enrichment scores
were calculated using the algorithm proposed in (Marques et al.
2016). The detailed procedures are described in
scripts/make-data.R. Users are encouraged to download the
data, generate customized cell-type specific marker genes and apply it
in on bulk DE genes using LRcell.
The example below shows how to download the data using ExperimentHub.
## Loading required package: BiocGenerics## Loading required package: generics##
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## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, saveRDS, scale, sequence, table,
## tapply, transform, unique, unsplit, which.max, which.min## Loading required package: AnnotationHub## Loading required package: BiocFileCache## Loading required package: dbplyr## ExperimentHub with 15 records
## # snapshotDate(): 2026-10-07
## # $dataprovider: Emory University
## # $species: Mus musculus, Homo sapiens
## # $rdataclass: Matrix, list
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH4548"]]'
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## title
## EH4548 | Mouse Frontal Cortex Marker Genes
## EH4549 | Mouse Cerebellum Marker Genes
## EH4550 | Mouse Entopeduncular Marker Genes
## EH4551 | Mouse Globus Pallidus Marker Genes
## EH4552 | Mouse Posterior Cortex Marker Genes
## ... ...
## EH5420 | Human PBMC Marker Genes
## EH6727 | MSigDB C8 MANNO MIDBRAIN
## EH6728 | MSigDB C8 ZHENG CORD BLOOD
## EH6729 | MSigDB C8 FAN OVARY
## EH6730 | MSigDB C8 RUBENSTEIN SKELETAL MUSCLEAccording to the description, users can choose the most suitable marker gene information for further analysis. Here, we used mouse frontal cortex marker genes as an example, which has the title as EH4548.
## see ?LRcellTypeMarkers and browseVignettes('LRcellTypeMarkers') for documentation## downloading 1 resources## retrieving 1 resource## loading from cache## FC_1-1.Interneuron_CGE FC_1-10.Interneuron_CGE
## 0610005C13Rik 0.001478288 0.000000000
## 0610007P14Rik 0.058866903 0.073872567
## 0610009B22Rik 0.113303793 0.240960122
## 0610009E02Rik 0.009086005 0.000000000
## 0610009L18Rik 0.021694610 0.001329023
## 0610009O20Rik 0.027142597 0.023486916
## FC_1-11.Interneuron_CGE FC_1-2.Interneuron_CGE
## 0610005C13Rik 0.00000000 0.000000000
## 0610007P14Rik 0.11023412 0.077231929
## 0610009B22Rik 0.07701829 0.043167790
## 0610009E02Rik 0.00212738 0.006541533
## 0610009L18Rik 0.00000000 0.014922859
## 0610009O20Rik 0.02644789 0.022989316
## FC_1-3.Interneuron_CGE FC_1-4.Interneuron_CGE
## 0610005C13Rik 0.000000000 0.000000000
## 0610007P14Rik 0.076905406 0.044742740
## 0610009B22Rik 0.111148329 0.250265646
## 0610009E02Rik 0.000000000 0.000000000
## 0610009L18Rik 0.000000000 0.005565732
## 0610009O20Rik 0.009772696 0.004191282From the data, we can observe that the rows are gene symbols and the
columns are the sub-cell types (or clusters). The values indicate the
gene enrichment level in a sub-cell type (or cluster). Higher the values
are, more unique the genes are in the certain cell types. Users can sort
the enrichment score in a descending order and select top 100 genes for
each sub-cell type (or cluster) using the following command or the
function named get_markergenes() in LRcell.
Here, we take a glance at the first 6 clusters and the first 6 marker genes in each cluster.
library(LRcell)
FC_marker_genes <- get_markergenes(mouse_FC, method="LR", topn=100)
head(lapply(FC_marker_genes, head)) # glance at the marker genes## $`FC_1-1.Interneuron_CGE`
## [1] "Npy" "Pde11a" "Kit" "Gad2" "Fam46a" "Pnoc"
##
## $`FC_1-10.Interneuron_CGE`
## [1] "Krt73" "Npas1" "Yjefn3" "Dlx6os1" "Igf1" "Sln"
##
## $`FC_1-11.Interneuron_CGE`
## [1] "Htr3a" "Tnfaip8l3" "Npy2r" "Adarb2" "Npas1" "Crabp1"
##
## $`FC_1-2.Interneuron_CGE`
## [1] "Ndnf" "Kit" "Reln" "Npy" "Cplx3" "Gad1"
##
## $`FC_1-3.Interneuron_CGE`
## [1] "Calb2" "Crh" "Nr2f2" "Dlx1" "Penk" "Pcdh18"
##
## $`FC_1-4.Interneuron_CGE`
## [1] "Htr3a" "Tac2" "Vip" "Crh" "Crispld2" "Adarb2"Note: For MSigDB C8 datasets, the marker genes are only available for Logistic Regression (LR) analysis.
Usage of cell-type specific markers
LRcellTypeMarkers package is a data source for running LRcell analysis which identifies (sub-)cell types are transcriptionally enriched in bulk differentially expressed genes (DEGs) experiments. Here, we demonstrate an example on using provided mouse brain Frontal Cortex marker genes on Alzheimer’s Disease (AD) to identify Microglia.
First, we load the sample bulk DEGs vector provided by LRcell package which contrasts AD mice with wild type mouse at the timepoint of 6 months. The names of the vector are the genes and values are the p-value calculated from DESeq2.
## Xkr4 Rp1 Sox17 Mrpl15 Lypla1
## 1.742186e-05 4.103134e-02 9.697389e-02 1.206500e-02 6.609558e-01Then, we applied LRcell analysis on this DEG list using the FC marker genes we just acquired from the last section. Please note that the method for acquiring marker genes and running LRcell should be the same.
res <- LRcellCore(gene.p = example_gene_pvals,
marker.g = FC_marker_genes,
method = "LR", min.size = 5,
sig.cutoff = 0.05)
## curate cell types
res$cell_type <- unlist(lapply(strsplit(res$ID, '\\.'), '[', 2))
head(subset(res, select=-lead_genes))## ID genes_num coef odds_ratio p.value
## 1 FC_1-1.Interneuron_CGE 98 0.011908189 1.076812 0.006035417
## 2 FC_1-10.Interneuron_CGE 92 0.006805227 1.043199 0.358384832
## 3 FC_1-11.Interneuron_CGE 95 0.006160366 1.039027 0.441406086
## 4 FC_1-2.Interneuron_CGE 98 0.009983947 1.064012 0.039942444
## 5 FC_1-3.Interneuron_CGE 85 0.004671113 1.029455 0.651009137
## 6 FC_1-4.Interneuron_CGE 98 0.006406322 1.040616 0.400647777
## FDR cell_type
## 1 0.01357969 Interneuron_CGE
## 2 0.41470245 Interneuron_CGE
## 3 0.48316072 Interneuron_CGE
## 4 0.05882433 Interneuron_CGE
## 5 0.66749038 Interneuron_CGE
## 6 0.45707704 Interneuron_CGEPlot out the result using a function provided by LRcell:
For further usage of LRcell, please check LRcell Tutorial.
sessionInfo()
## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 26.04.1 LTS
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] LRcell_1.21.0 LRcellTypeMarkers_1.21.0 ExperimentHub_3.3.2
## [4] AnnotationHub_4.3.2 BiocFileCache_3.3.0 dbplyr_2.6.0
## [7] BiocGenerics_0.59.12 generics_0.1.4 BiocStyle_2.41.0
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