cell cycle regression Seurat object
Ehsan.Rz
06/05/2022
As the "cc.genes$s.genes/g2m.genes have been written for Human, we are supposed to change the organism here, i.e., we need to align all genes in mice. To this end, we should use the following functions.
library(biomaRt)
library(Seurat)## Attaching SeuratObjectlibrary(org.Hs.eg.db)## Loading required package: AnnotationDbi## Loading required package: stats4## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, append, as.data.frame, basename, cbind, colnames,
## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which.max, which.min## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.## Loading required package: IRanges## Loading required package: S4Vectors##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:base':
##
## expand.grid## library(org.Mm.eg.db)## library(Nebulosa)## Loading required package: ggplot2## Loading required package: patchworklibrary(devtools)## Loading required package: usethisobj <- readRDS("/mnt/nectar_volume/home/eraz0001/new/alberto final files/C1L.rds")Cell cycle Please estimate cell cycle phase of each cell and make a table to describe how many cells per phase.
convertHumanGeneList <- function(x){
require("biomaRt")
human = useMart("ensembl", dataset = "hsapiens_gene_ensembl", host = "https://dec2021.archive.ensembl.org/")
mouse = useMart("ensembl", dataset = "mmusculus_gene_ensembl",host = "https://dec2021.archive.ensembl.org/")
genesV2 = getLDS(attributes = c("hgnc_symbol"),
filters = "hgnc_symbol",
values = x ,
mart = human,
attributesL = c("mgi_symbol"),
martL = mouse, uniqueRows=T)
humanx <- unique(genesV2[, 2])
return(humanx)}s_gene <- convertHumanGeneList(cc.genes$s.genes)
g2m_gene <- convertHumanGeneList(cc.genes$g2m.genes)obj <- CellCycleScoring(obj, s.features = s_gene, g2m.features = g2m_gene, set.ident = TRUE)a3 <- DimPlot(obj)
a3
We can regress out the data pertinet to cell cycle using the following command:
table(obj@meta.data$Phase)##
## G1 G2M S
## 4051 1728 2465head(obj)## orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8
## AAACCCAAGACCTGGA-1_1_1_1_1 C1L 5447 1683 6
## AAACCCACAATACCCA-1_1_1_1_1 C1L 8203 2268 11
## AAACCCACAGTACTAC-1_1_1_1_1 C1L 3869 1309 3
## AAACCCAGTTATGGTC-1_1_1_1_1 C1L 9335 2557 2
## AAACCCAGTTGAGAGC-1_1_1_1_1 C1L 2544 1060 4
## AAACCCAGTTGCTCAA-1_1_1_1_1 C1L 13549 3026 3
## AAACCCATCAACTCTT-1_1_1_1_1 C1L 6375 1843 4
## AAACGAAAGCTTGTGT-1_1_1_1_1 C1L 2104 953 5
## AAACGAAAGGTGCTAG-1_1_1_1_1 C1L 3107 1218 5
## AAACGAAAGTCCGCCA-1_1_1_1_1 C1L 991 512 8
## seurat_clusters nCount_SCT nFeature_SCT
## AAACCCAAGACCTGGA-1_1_1_1_1 3 3033 1626
## AAACCCACAATACCCA-1_1_1_1_1 17 3269 1826
## AAACCCACAGTACTAC-1_1_1_1_1 4 3257 1309
## AAACCCAGTTATGGTC-1_1_1_1_1 1 3217 2068
## AAACCCAGTTGAGAGC-1_1_1_1_1 10 2640 1060
## AAACCCAGTTGCTCAA-1_1_1_1_1 2 2936 1724
## AAACCCATCAACTCTT-1_1_1_1_1 6 3018 1578
## AAACGAAAGCTTGTGT-1_1_1_1_1 5 2844 952
## AAACGAAAGGTGCTAG-1_1_1_1_1 11 3063 1218
## AAACGAAAGTCCGCCA-1_1_1_1_1 0 2271 546
## SCT_snn_res.0.8 percent.mt nFeature_RNA_MAD
## AAACCCAAGACCTGGA-1_1_1_1_1 3 0.00000000 0.3925254
## AAACCCACAATACCCA-1_1_1_1_1 17 0.00000000 0.9813830
## AAACCCACAGTACTAC-1_1_1_1_1 4 0.03944773 -0.1035465
## AAACCCAGTTATGGTC-1_1_1_1_1 1 0.00000000 1.2181264
## AAACCCAGTTGAGAGC-1_1_1_1_1 10 0.00000000 -0.5200306
## AAACCCAGTTGCTCAA-1_1_1_1_1 2 0.00000000 1.5505464
## AAACCCATCAACTCTT-1_1_1_1_1 6 0.00000000 0.5717895
## AAACGAAAGCTTGTGT-1_1_1_1_1 5 0.04405286 -0.7300729
## AAACGAAAGGTGCTAG-1_1_1_1_1 11 0.07049700 -0.2457732
## AAACGAAAGTCCGCCA-1_1_1_1_1 0 0.00000000 -1.9564436
## nCount_RNA_MAD S.Score G2M.Score Phase
## AAACCCAAGACCTGGA-1_1_1_1_1 0.45828981 -0.078262457 -0.07953102 G1
## AAACCCACAATACCCA-1_1_1_1_1 0.98712902 -0.091223831 -0.08668466 G1
## AAACCCACAGTACTAC-1_1_1_1_1 0.01646269 -0.038280103 -0.02271644 G1
## AAACCCAGTTATGGTC-1_1_1_1_1 1.15409935 0.039875252 0.09625804 G2M
## AAACCCAGTTGAGAGC-1_1_1_1_1 -0.52506485 -0.056810200 -0.02980515 G1
## AAACCCAGTTGCTCAA-1_1_1_1_1 1.63528641 -0.068941917 0.17515311 G2M
## AAACCCATCAACTCTT-1_1_1_1_1 0.66148816 -0.030312564 -0.09154916 G1
## AAACGAAAGCTTGTGT-1_1_1_1_1 -0.77034231 -0.049507830 -0.02134039 G1
## AAACGAAAGGTGCTAG-1_1_1_1_1 -0.26684183 -0.063724244 -0.07334346 G1
## AAACGAAAGTCCGCCA-1_1_1_1_1 -1.74278744 0.009486496 -0.07151220 S
## old.ident
## AAACCCAAGACCTGGA-1_1_1_1_1 3
## AAACCCACAATACCCA-1_1_1_1_1 17
## AAACCCACAGTACTAC-1_1_1_1_1 4
## AAACCCAGTTATGGTC-1_1_1_1_1 1
## AAACCCAGTTGAGAGC-1_1_1_1_1 10
## AAACCCAGTTGCTCAA-1_1_1_1_1 2
## AAACCCATCAACTCTT-1_1_1_1_1 6
## AAACGAAAGCTTGTGT-1_1_1_1_1 5
## AAACGAAAGGTGCTAG-1_1_1_1_1 11
## AAACGAAAGTCCGCCA-1_1_1_1_1 0plot_density(obj, c("Mcm4", "Rrm2" , "Exo1" , "Cdc45" , "Mcm2" , "Uhrf1" , "Chaf1b" , "Gmnn" , "Msh2" , "Fen1"))
g2m_gene## [1] "Cbx5" "Cdc25c" "Smc4" "Gtse1" "Dlgap5" "Ctcf" "Cdca2"
## [8] "Kif20b" "Cks2" "Hmgb2" "Ckap2" "Ncapd2" "Anp32e" "G2e3"
## [15] "Cdk1" "Ttk" "Cdca3" "Top2a" "Gas2l3" "Tacc3" "Cdca8"
## [22] "Ndc80" "Rangap1" "Ect2" "Cks1b" "Anln" "Cenpe" "Birc5"
## [29] "Hjurp" "Lbr" "Tpx2" "Cdc20" "Psrc1" "Kif2c" "Cenpa"
## [36] "Cenpf" "Aurkb" "Kif11" "Nek2" "Ccnb2" "Hmmr" "Kif23"
## [43] "Mki67" "Tubb4b" "Nusap1" "Bub1" "Ckap5" "Nuf2" "Ube2c"
## [50] "Aurka" "Ckap2l"plot_density(obj, c("Tacc3", "Gas2l3", "Hmgb2" , "Cks2" , "Ncapd2" , "Anp32e" , "Ckap2" , "Aurkb" , "Kif11" , "Nek2" , "Ccnb2" , "Kif23" , "Hmmr"))
as<- RidgePlot(obj, features = c("Tacc3", "Gas2l3", "Hmgb2"))as## Picking joint bandwidth of 2.5e-06## Picking joint bandwidth of 2.02e-06## Picking joint bandwidth of 0.0306
Regress out the cell-cycle pertinent markers/genes
obj <- ScaleData(obj, vars.to.regress = c("S.Score", "G2M.Score"), features = rownames(obj))## Regressing out S.Score, G2M.Score## Centering and scaling data matrixtable(obj@meta.data$Phase)##
## G1 G2M S
## 4051 1728 2465Compare this one with the first table result. Many cells have been deleted!
obj <- RunPCA(obj, features = VariableFeatures(obj), nfeatures.print = 5)## Warning in PrepDR(object = object, features = features, verbose = verbose): The
## following 3 features requested have zero variance (running reduction without
## them): AC166061.1, Klra17, Fam90a1b## PC_ 1
## Positive: Tshz2, Dlc1, Ebf1, Ebf2, Auts2
## Negative: Gm28653, Ryr1, Rreb1, Chd7, Kcnk13
## PC_ 2
## Positive: Mrc1, Dock2, Inpp5d, Adgre1, F13a1
## Negative: Ryr1, Gm28653, Airn, Msi2, Cap2
## PC_ 3
## Positive: Col11a1, Col2a1, Sox6, Wwp2, Chst11
## Negative: Sorcs2, Celf2, Ebf3, Ebf2, Tcf7l2
## PC_ 4
## Positive: Pax7, Npas3, Nectin1, Zfp536, Adcy2
## Negative: Alpk3, Myom3, Ldb3, Trim55, Casq2
## PC_ 5
## Positive: Flt1, Pecam1, Ptprb, Cyyr1, Adgrf5
## Negative: Meg3, Zfp385b, Celf2, Col2a1, Nrg1obj <- RunPCA(obj, features = c(s_gene, g2m_gene))## Warning in irlba(A = t(x = object), nv = npcs, ...): You're computing too large
## a percentage of total singular values, use a standard svd instead.## PC_ 1
## Positive: Cenpf, Ckap2, Cenpe, Dlgap5, Cenpa, Kif23, Gas2l3, Kif20b, Ckap2l, Cdc25c
## Casp8ap2, Ect2, Hmmr, Ccnb2, Msh2, Anln, Tipin, Kif2c, Cdca7, Mcm6
## Ung, G2e3, Cks2, Nasp, Rfc2, Blm, Nusap1, Hells, Psrc1, Nek2
## Negative: Cbx5, Top2a, Anp32e, Smc4, Clspn, Lbr, Ctcf, Hmgb2, Kif11, Ccne2
## Rad51, Rrm2, Ttk, Cdc45, Tyms, Brip1, Exo1, Wdr76, Slbp, Rad51ap1
## Prim1, Mki67, E2f8, Cdca3, Nuf2, Rangap1, Uhrf1, Ncapd2, Mcm2, Cdca2
## PC_ 2
## Positive: Rad51ap1, Clspn, Cdca2, Ncapd2, Cdca8, Top2a, Nusap1, Anln, E2f8, Brip1
## Ndc80, Blm, Rrm2, Cdc45, Rad51, Aurkb, Tacc3, Ckap2l, Kif23, Tpx2
## Slbp, Birc5, Nuf2, Tyms, Gmnn, Kif20b, Ect2, Kif11, Ccne2, Rangap1
## Negative: Cbx5, Nasp, Ctcf, Casp8ap2, Lbr, Ckap5, Ung, Pcna, Hmgb2, Dtl
## G2e3, Anp32e, Mcm6, Hells, Mcm4, Msh2, Gas2l3, Rpa2, Kif2c, Ubr7
## Cdc25c, Smc4, Cdca7, Wdr76, Ccnb2, Cdc6, Ckap2, Hjurp, Tubb4b, Cenpf
## PC_ 3
## Positive: Ect2, Blm, Rpa2, Hjurp, Ckap5, Dscc1, Pola1, Ckap2, Wdr76, Brip1
## Gas2l3, Tyms, Ctcf, Usp1, Cdk1, Chaf1b, Ccnb2, Dtl, Rad51, Kif2c
## Lbr, Rangap1, Ttk, Aurkb, Anln, Fen1, Kif11, Ndc80, Birc5, Ccne2
## Negative: Mki67, Nasp, Hmmr, Rad51ap1, Tpx2, Bub1, Rrm2, Cenpf, Mcm6, Hmgb2
## Msh2, Exo1, Casp8ap2, Kif23, Cdca2, Rrm1, Pcna, Cdca7, Mcm5, Cdca8
## Clspn, Gmnn, Slbp, Mcm4, Cdc6, Cenpe, Nuf2, Ung, Cdc45, Smc4
## PC_ 4
## Positive: Hells, Wdr76, Tacc3, Cdc45, Casp8ap2, Brip1, Rad51ap1, G2e3, Top2a, Prim1
## Mcm6, Hmgb2, Cenpe, Kif20b, Blm, Ect2, Lbr, E2f8, Slbp, Cks2
## Nuf2, Smc4, Cdca8, Nek2, Ttk, Gas2l3, Ckap2, Mcm2, Dlgap5, Dscc1
## Negative: Pola1, Rrm2, Cdca7, Nusap1, Kif23, Ctcf, Tpx2, Mcm5, Ubr7, Rad51
## Rangap1, Birc5, Tipin, Ccne2, Chaf1b, Dtl, Cbx5, Ckap5, Usp1, Gmnn
## Tyms, Uhrf1, Rpa2, Cenpf, Rfc2, Cenpa, Anp32e, Ncapd2, Ndc80, Gtse1
## PC_ 5
## Positive: Hjurp, Rrm1, Dlgap5, Cdc6, Cdk1, Ckap2l, Cdca8, Ung, Ubr7, G2e3
## Ccnb2, Slbp, Ect2, Mcm5, Gmnn, Rad51, Aurka, Brip1, Ttk, Rad51ap1
## Nasp, Pola1, Kif2c, Lbr, Rpa2, Kif23, Cdc45, Hmgb2, Cdc20, Cdca2
## Negative: Dtl, Rfc2, Cenpe, Mcm2, Ndc80, Nusap1, Ctcf, Nuf2, Blm, Wdr76
## Mki67, Gas2l3, Cenpf, Anln, Tpx2, Psrc1, Kif11, Fen1, Cks2, Pcna
## Casp8ap2, Gtse1, Tyms, Gins2, Tipin, Top2a, Mcm6, Cdca7, E2f8, Mcm4a4 <- DimPlot(obj)
a4
Comparing b4 and after cell cycle regression:
a3 + a4
session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.2 (2020-06-22)
## os Ubuntu 20.04.4 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_AU.UTF-8
## ctype en_AU.UTF-8
## tz Australia/Melbourne
## date 2022-05-06
## pandoc 2.11.4 @ /usr/lib/rstudio-server/bin/pandoc/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
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## SingleCellExperiment 1.12.0 2020-10-27 [1] Bioconductor
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## zoo 1.8-9 2021-03-09 [1] CRAN (R 4.0.2)
##
## [1] /mnt/nectar_volume/home/eraz0001/R/x86_64-pc-linux-gnu-library/4.0
## [2] /mnt/nectar_volume/software/apps/R/4.0.2/lib/R/library
##
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