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.
library(ExperimentHub)
## Loading required package: BiocGenerics
##
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
##
## Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
## as.data.frame, basename, cbind, colnames, dirname, do.call,
## duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
## lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
## pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
## tapply, union, unique, unsplit, which.max, which.min
## Loading required package: AnnotationHub
## Loading required package: BiocFileCache
## Loading required package: dbplyr
## Warning: replacing previous import 'utils::findMatches' by
## 'S4Vectors::findMatches' when loading 'AnnotationDbi'
eh <- ExperimentHub()
eh <- query(eh, "LRcellTypeMarkers")
eh ## show entries of LRcellTypeMarkers package
## ExperimentHub with 15 records
## # snapshotDate(): 2023-04-24
## # $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"]]'
##
## 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 MUSCLE
According 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.
mouse_FC <- eh[['EH4548']]
## see ?LRcellTypeMarkers and browseVignettes('LRcellTypeMarkers') for documentation
## loading from cache
mouse_FC[1:6, 1:6] # show head of the data
## 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 0.004191282
From 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"
##
## $`FC_1-4.Interneuron_CGE`
## [1] "Htr3a" "Tac2" "Vip" "Crh" "Crispld2" "Adarb2"
Note: For MSigDB C8 datasets, the marker genes are only available for Logistic Regression (LR) analysis.
LRcellTypeMarkers package is a data source for running LRcell analysis which identifies (sub-)cell types are transcriptionally enriched in bulk differentially expressed genes (DEGs) experiments. Here, we demonstrate an example on using provided mouse brain Frontal Cortex marker genes on Alzheimer’s Disease (AD) to identify Microglia.
First, we load the sample bulk DEGs vector provided by LRcell package which contrasts AD mice with wild type mouse at the timepoint of 6 months. The names of the vector are the genes and values are the p-value calculated from DESeq2.
library(LRcell)
data("example_gene_pvals")
head(example_gene_pvals, n=5)
## Xkr4 Rp1 Sox17 Mrpl15 Lypla1
## 1.742186e-05 4.103134e-02 9.697389e-02 1.206500e-02 6.609558e-01
Then, we applied LRcell analysis on this DEG list using the FC marker genes we just acquired from the last section. Please note that the method for acquiring marker genes and running LRcell should be the same.
res <- LRcellCore(gene.p = example_gene_pvals,
marker.g = FC_marker_genes,
method = "LR", min.size = 5,
sig.cutoff = 0.05)
## Warning: glm.fit: algorithm did not converge
## curate cell types
res$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_CGE
Plot out the result using a function provided by LRcell:
plot_manhattan_enrich(res, sig.cutoff = .05, label.topn = 5)
For further usage of LRcell, please check LRcell Tutorial.
sessionInfo()
## R version 4.3.0 RC (2023-04-13 r84269)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] LRcell_1.8.0 LRcellTypeMarkers_1.8.0 ExperimentHub_2.8.0
## [4] AnnotationHub_3.8.0 BiocFileCache_2.8.0 dbplyr_2.3.2
## [7] BiocGenerics_0.46.0 BiocStyle_2.28.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.0 farver_2.1.1
## [3] dplyr_1.1.2 blob_1.2.4
## [5] filelock_1.0.2 Biostrings_2.68.0
## [7] bitops_1.0-7 fastmap_1.1.1
## [9] RCurl_1.98-1.12 promises_1.2.0.1
## [11] digest_0.6.31 mime_0.12
## [13] lifecycle_1.0.3 ellipsis_0.3.2
## [15] KEGGREST_1.40.0 interactiveDisplayBase_1.38.0
## [17] RSQLite_2.3.1 magrittr_2.0.3
## [19] compiler_4.3.0 rlang_1.1.0
## [21] sass_0.4.5 tools_4.3.0
## [23] utf8_1.2.3 yaml_2.3.7
## [25] knitr_1.42 labeling_0.4.2
## [27] bit_4.0.5 curl_5.0.0
## [29] BiocParallel_1.34.0 withr_2.5.0
## [31] purrr_1.0.1 grid_4.3.0
## [33] stats4_4.3.0 fansi_1.0.4
## [35] xtable_1.8-4 colorspace_2.1-0
## [37] ggplot2_3.4.2 scales_1.2.1
## [39] cli_3.6.1 rmarkdown_2.21
## [41] crayon_1.5.2 generics_0.1.3
## [43] httr_1.4.5 DBI_1.1.3
## [45] cachem_1.0.7 zlibbioc_1.46.0
## [47] parallel_4.3.0 AnnotationDbi_1.62.0
## [49] BiocManager_1.30.20 XVector_0.40.0
## [51] vctrs_0.6.2 jsonlite_1.8.4
## [53] bookdown_0.33 IRanges_2.34.0
## [55] S4Vectors_0.38.0 ggrepel_0.9.3
## [57] bit64_4.0.5 magick_2.7.4
## [59] jquerylib_0.1.4 glue_1.6.2
## [61] codetools_0.2-19 gtable_0.3.3
## [63] BiocVersion_3.17.1 later_1.3.0
## [65] GenomeInfoDb_1.36.0 munsell_0.5.0
## [67] tibble_3.2.1 pillar_1.9.0
## [69] rappdirs_0.3.3 htmltools_0.5.5
## [71] GenomeInfoDbData_1.2.10 R6_2.5.1
## [73] evaluate_0.20 shiny_1.7.4
## [75] Biobase_2.60.0 highr_0.10
## [77] png_0.1-8 memoise_2.0.1
## [79] httpuv_1.6.9 bslib_0.4.2
## [81] Rcpp_1.0.10 xfun_0.39
## [83] pkgconfig_2.0.3
Marques, Sueli, Amit Zeisel, Simone Codeluppi, David van Bruggen, Ana Mendanha Falcão, Lin Xiao, Huiliang Li, et al. 2016. “Oligodendrocyte Heterogeneity in the Mouse Juvenile and Adult Central Nervous System.” Science 352 (6291): 1326–9.