Seurat包学习笔记（八）：Cell-Cycle Scorin
2020-04-15 本文已影响0人
Davey1220
在本教程中，我们将学习如何通过一些经典的marker基因计算细胞周期阶段评分，并在预处理过程中将其从scRNA-seq数据中进行回归处理，来减轻细胞周期异质性对下游数据分析的影响。
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加载所需的R包和数据
library(Seurat)
# Read in the expression matrix The first row is a header row, the first column is rownames
exp.mat <- read.table(file = "../data/nestorawa_forcellcycle_expressionMatrix.txt", header = TRUE,
as.is = TRUE, row.names = 1)
# A list of cell cycle markers, from Tirosh et al, 2015, is loaded with Seurat. We can segregate this list into markers of G2/M phase and markers of S phase
# 查看细胞周期marker基因
cc.genes
$s.genes
[1] "MCM5" "PCNA" "TYMS" "FEN1" "MCM2" "MCM4" "RRM1"
[8] "UNG" "GINS2" "MCM6" "CDCA7" "DTL" "PRIM1" "UHRF1"
[15] "MLF1IP" "HELLS" "RFC2" "RPA2" "NASP" "RAD51AP1" "GMNN"
[22] "WDR76" "SLBP" "CCNE2" "UBR7" "POLD3" "MSH2" "ATAD2"
[29] "RAD51" "RRM2" "CDC45" "CDC6" "EXO1" "TIPIN" "DSCC1"
[36] "BLM" "CASP8AP2" "USP1" "CLSPN" "POLA1" "CHAF1B" "BRIP1"
[43] "E2F8"
$g2m.genes
[1] "HMGB2" "CDK1" "NUSAP1" "UBE2C" "BIRC5" "TPX2" "TOP2A"
[8] "NDC80" "CKS2" "NUF2" "CKS1B" "MKI67" "TMPO" "CENPF"
[15] "TACC3" "FAM64A" "SMC4" "CCNB2" "CKAP2L" "CKAP2" "AURKB"
[22] "BUB1" "KIF11" "ANP32E" "TUBB4B" "GTSE1" "KIF20B" "HJURP"
[29] "CDCA3" "HN1" "CDC20" "TTK" "CDC25C" "KIF2C" "RANGAP1"
[36] "NCAPD2" "DLGAP5" "CDCA2" "CDCA8" "ECT2" "KIF23" "HMMR"
[43] "AURKA" "PSRC1" "ANLN" "LBR" "CKAP5" "CENPE" "CTCF"
[50] "NEK2" "G2E3" "GAS2L3" "CBX5" "CENPA"
# 获取S期marker基因
s.genes <- cc.genes$s.genes
# 获取G2M期marker基因
g2m.genes <- cc.genes$g2m.genes
构建Seurat对象并进行数据预处理
# Create our Seurat object and complete the initalization steps
marrow <- CreateSeuratObject(counts = exp.mat)
marrow <- NormalizeData(marrow)
marrow <- FindVariableFeatures(marrow, selection.method = "vst")
marrow <- ScaleData(marrow, features = rownames(marrow))
PCA降维可视化
我们使用FindVariableFeatures函数筛选出的高变异基因进行PCA降维可视化，可以发现尽管大多数变异都能被解释，但是在PC8和PC10中存在一些细胞周期基因，如TOP2A和MKI67。因此，我们试图通过回归处理减少细胞周期异质性对下游分析的影响。
marrow <- RunPCA(marrow, features = VariableFeatures(marrow), ndims.print = 6:10, nfeatures.print = 10)
## PC_ 6
## Positive: SELL, ARL6IP1, CCL9, CD34, ADGRL4, BPIFC, NUSAP1, FAM64A, CD244, C030034L19RIK
## Negative: LY6C2, AA467197, CYBB, MGST2, ITGB2, PF4, CD74, ATP1B1, GP1BB, TREM3
## PC_ 7
## Positive: HDC, CPA3, PGLYRP1, MS4A3, NKG7, UBE2C, CCNB1, NUSAP1, PLK1, FUT8
## Negative: F13A1, LY86, CFP, IRF8, CSF1R, TIFAB, IFI209, CCR2, TNS4, MS4A6C
## PC_ 8
## Positive: NUSAP1, UBE2C, KIF23, PLK1, CENPF, FAM64A, CCNB1, H2AFX, ID2, CDC20
## Negative: WFDC17, SLC35D3, ADGRL4, VLDLR, CD33, H2AFY, P2RY14, IFI206, CCL9, CD34
## PC_ 9
## Positive: IGKC, JCHAIN, LY6D, MZB1, CD74, IGLC2, FCRLA, IGKV4-50, IGHM, IGHV9-1
## Negative: SLC2A6, HBA-A1, HBA-A2, IGHV8-7, FCER1G, F13A1, HBB-BS, PLD4, HBB-BT, IGFBP4
## PC_ 10
## Positive: CTSW, XKRX, PRR5L, RORA, MBOAT4, A630014C17RIK, ZFP105, COL9A3, CLEC2I, TRAT1
## Negative: H2AFX, FAM64A, ZFP383, NUSAP1, CDC25B, CENPF, GBP10, TOP2A, GBP6, GFRA1
DimHeatmap(marrow, dims = c(8, 10))
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计算细胞周期评分
首先，我们基于G2/M和S期的经典marker基因的表达，计算每个细胞可能所处的细胞周期的分数。这些marker基因集应该与它们的表达水平是成反相关的关系，而那些都不表达这些marker基因的细胞可能处于G1期。
我们使用CellCycleScoring函数计算每个细胞的细胞周期得分，并将计算出的S期和G2/M期的评分保存在metadata中，以及细胞处于G2M，S或G1期的预测分类。通过设置set.ident = TRUE，则CellCycleScoring将Seurat对象中每个细胞的分组信息设置为其所处的细胞周期阶段。
# 使用CellCycleScoring函数计算细胞周期评分
marrow <- CellCycleScoring(marrow, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE)
# view cell cycle scores and phase assignments
head(marrow[[]])
orig.ident nCount_RNA nFeature_RNA S.Score G2M.Score Phase old.ident
Prog_013 Prog 2563089 10211 -0.1424869 -0.4680395 G1 Prog
Prog_019 Prog 3030620 9991 -0.1691579 0.5851766 G2M Prog
Prog_031 Prog 1293487 10192 -0.3462704 -0.3971879 G1 Prog
Prog_037 Prog 1357987 9599 -0.4427021 0.6820229 G2M Prog
Prog_008 Prog 4079891 10540 0.5585405 0.1284359 S Prog
Prog_014 Prog 2569783 10788 0.0711622 0.3166073 G2M Prog
# Visualize the distribution of cell cycle markers across
RidgePlot(marrow, features = c("PCNA", "TOP2A", "MCM6", "MKI67"), ncol = 2)
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# Running a PCA on cell cycle genes reveals, unsurprisingly, that cells separate entirely by phase
marrow <- RunPCA(marrow, features = c(s.genes, g2m.genes))
# 数据可视化，可以看到细胞按不同的细胞周期进行了分群
DimPlot(marrow)
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在数据标准化时消除细胞周期的影响
For each gene, Seurat models the relationship between gene expression and the S and G2M cell cycle scores. The scaled residuals of this model represent a ‘corrected’ expression matrix, that can be used downstream for dimensional reduction.
使用ScaleData函数进行数据标准化，并设置vars.to.regress参数指定对细胞周期评分进行回归处理，消除细胞周期异质性的影响
marrow <- ScaleData(marrow, vars.to.regress = c("S.Score", "G2M.Score"), features = rownames(marrow))
# Now, a PCA on the variable genes no longer returns components associated with cell cycle
marrow <- RunPCA(marrow, features = VariableFeatures(marrow), nfeatures.print = 10)
## PC_ 1
## Positive: BLVRB, CAR2, KLF1, AQP1, CES2G, ERMAP, CAR1, FAM132A, RHD, SPHK1
## Negative: TMSB4X, H2AFY, CORO1A, PLAC8, EMB, MPO, PRTN3, CD34, LCP1, BC035044
## PC_ 2
## Positive: ANGPT1, ADGRG1, MEIS1, ITGA2B, MPL, DAPP1, APOE, RAB37, GATA2, F2R
## Negative: LY6C2, ELANE, HP, IGSF6, ANXA3, CTSG, CLEC12A, TIFAB, SLPI, ALAS1
## PC_ 3
## Positive: APOE, GATA2, NKG7, MUC13, MS4A3, RAB44, HDC, CPA3, FCGR3, TUBA8
## Negative: FLT3, DNTT, LSP1, WFDC17, MYL10, GIMAP6, LAX1, GPR171, TBXA2R, SATB1
## PC_ 4
## Positive: CSRP3, ST8SIA6, DNTT, MPEG1, SCIN, LGALS1, CMAH, RGL1, APOE, MFSD2B
## Negative: PROCR, MPL, HLF, MMRN1, SERPINA3G, ESAM, GSTM1, D630039A03RIK, MYL10, LY6A
## PC_ 5
## Positive: PF4, GP1BB, SDPR, F2RL2, RAB27B, SLC14A1, TREML1, PBX1, F2R, TUBA8
## Negative: CPA3, LMO4, IKZF2, IFITM1, FUT8, MS4A2, SIGLECF, CSRP3, HDC, RAB44
# When running a PCA on only cell cycle genes, cells no longer separate by cell-cycle phase
marrow <- RunPCA(marrow, features = c(s.genes, g2m.genes))
DimPlot(marrow)
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再次根据细胞周期相关基因进行PCA分析时，没有按照不同的细胞周期进行分群了，说明消除细胞周期影响的效果还是比较好的。
可选择的替代方法
在上述的分析过程中，我们消除了与细胞周期相关的所有信号。但是，在某些情况下，我们发现这会对下游的分析产生一定的负面影响，尤其是在细胞分化过程中（如鼠类造血过程）。在此过程中干细胞处于静止状态，而分化的细胞正在增殖。在这种情况下，清除所有细胞周期效应也会使干细胞和祖细胞之间的区别模糊。
作为替代方案，我们建议逐步消除G2M和S期评分之间的差异。这意味着将保持非周期细胞和周期细胞的组分差异，但是增殖细胞之间的细胞周期阶段的差异将从数据中去除。
# 计算分数差异
marrow$CC.Difference <- marrow$S.Score - marrow$G2M.Score
# 数据标准化消除分数差异
marrow <- ScaleData(marrow, vars.to.regress = "CC.Difference", features = rownames(marrow))
# cell cycle effects strongly mitigated in PCA
marrow <- RunPCA(marrow, features = VariableFeatures(marrow), nfeatures.print = 10)
## PC_ 1
## Positive: BLVRB, KLF1, ERMAP, FAM132A, CAR2, RHD, CES2G, SPHK1, AQP1, SLC38A5
## Negative: TMSB4X, CORO1A, PLAC8, H2AFY, LAPTM5, CD34, LCP1, TMEM176B, IGFBP4, EMB
## PC_ 2
## Positive: APOE, GATA2, RAB37, ANGPT1, ADGRG1, MEIS1, MPL, F2R, PDZK1IP1, DAPP1
## Negative: CTSG, ELANE, LY6C2, HP, CLEC12A, ANXA3, IGSF6, TIFAB, SLPI, MPO
## PC_ 3
## Positive: APOE, GATA2, NKG7, MUC13, ITGA2B, TUBA8, CPA3, RAB44, SLC18A2, CD9
## Negative: DNTT, FLT3, WFDC17, LSP1, MYL10, LAX1, GIMAP6, IGHM, CD24A, MN1
## PC_ 4
## Positive: CSRP3, ST8SIA6, SCIN, LGALS1, APOE, ITGB7, MFSD2B, RGL1, DNTT, IGHV1-23
## Negative: MPL, MMRN1, PROCR, HLF, SERPINA3G, ESAM, PTGS1, D630039A03RIK, NDN, PPIC
## PC_ 5
## Positive: GP1BB, PF4, SDPR, F2RL2, TREML1, RAB27B, SLC14A1, PBX1, PLEK, TUBA8
## Negative: HDC, LMO4, CSRP3, IFITM1, FCGR3, HLF, CPA3, PROCR, PGLYRP1, IKZF2
# when running a PCA on cell cycle genes, actively proliferating cells remain distinct from G1 cells however, within actively proliferating cells, G2M and S phase cells group together
marrow <- RunPCA(marrow, features = c(s.genes, g2m.genes))
DimPlot(marrow)
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可以看到PCA降维分群后，G1期的细胞被区分开，G2/M和S期的细胞聚在一起。
参考来源：https://satijalab.org/seurat/v3.1/cell_cycle_vignette.html
