In 2018 Clarke et presented a paper on bioRxiv
Jevin Z Clark, Lihe Chen, Chung-Lin Chou, Hyun Jun Jung, Mark A Knepper. Cell-Type Selective Markers Represented in Whole-Kidney RNA-Seq Data bioRxiv 348615; doi: https://doi.org/10.1101/348615
In this paper they describe several tables of marker gene for different normal kidney cell types
https://www.biorxiv.org/content/early/2018/06/15/348615
Brief Method: Kidneys were removed from 5 week male mice and immediately placed in ice-cold PBS. cDNA libraries were constructed for paired-end sequencing and sequenced on Illumina HiSeq3000 platform. Reads were mapped to mouse Ensembl Genome by STAR and transcript abundances were calculated in the units of transcripts per million (TPM) using RSEM (https://github.com/deweylab/RSEM).
The supplementary data source provided mean TPM values for cell type selective markers expressed in mouse whole kidney samples. The number of replicates is n=3.
I downloaded the supplementary files
library(KidneyMarkerGenes) data(clarkeGenes) clarkeGenes[1:2,]
Extract the gene lists from the supplementary file as Gene Symbols or EnsEMBL ids, as matrix or list. Either keep NA, or convert NA to zero
clarkeGenesSigs(id= "Symbol",matrix=TRUE)[1:4,1:4]
Format the gene signature for Gene Set Analysis (as Gene Symbols)
head(clarkeGenesSigs(id= "Ensembl",matrix=FALSE))
Each gene in the list as a weight (TPM in normal kidney), visualize these weights
sigsSym<-clarkeGenesSigs(id= "Symbol",matrix=TRUE,convertNA=TRUE)
Use complex heatmap to visualize the TPM of each gene in each cell type
require(circlize) require(ComplexHeatmap) ComplexHeatmap::Heatmap(excludeZeroSumRowCol(sigsSym),col = colorRamp2(c(0, 0.1, .5,1), c("white", "cyan", "navy", "black")) , clustering_method_columns = "ward.D2", clustering_method_rows = "ward.D2" , row_names_side = "left", column_names_gp = gpar(fontsize = 10), row_names_gp = gpar(fontsize = 4), column_title = 'Clarke et al.,Whole Kidney RNAseq markers', heatmap_legend_param = list(title = "Whole.Kidney.TPM"))
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