Seurat4.0系列教程8：细胞周期评分和回归分析
此教程展示了如何通过基于传统细胞周期相关marker计算细胞周期得分，并在预处理过程中将这些分数从数据中回归，以消除 scRNA-seq 数据中细胞周期异质性的影响。我们在小鼠造血祖细胞数据集上证明了这一点。您可以在此处[1]下载运行此教程所需的文件。
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
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes
# 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))
在使用FindVariableFeatures()发现的高变基因在对象上运行PCA，我们可以看到，虽然大多数差异可以用谱系来解释，但在包括TOP2A和MKI67在内的细胞周期基因在PC8和PC10中是分开的。我们将尝试从数据中回归此信号，以便细胞周期异质性不会对 PCA 或下游分析造成影响。
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))
分配细胞周期得分
首先，我们根据每个细胞的G2 / M和S期marker的表达为其分配分数。这些marker集合在表达水平上应该是反相关的，不表达它们的细胞可能不会循环并处于G1期。
我们用CellCycleScoring()函数分配分数，该函数将S和G2 / M分数以及每个细胞在G2M，S或G1阶段的预测分类存储在对象元数据metadata中。CellCycleScoring()还可以通过set.ident = TRUE来将Seurat对象的身份设置为细胞周期分期。请注意，Seurat在下游细胞周期回归中不使用离散分类（G2M / G1 / S）。相反，它使用G2M和S期的定量评分。但是，如果有兴趣，可以使用预测的分类。
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
## Prog_013 Prog 2563089 10211 -0.14248691 -0.4680395 G1
## Prog_019 Prog 3030620 9991 -0.16915786 0.5851766 G2M
## Prog_031 Prog 1293487 10192 -0.34627038 -0.3971879 G1
## Prog_037 Prog 1357987 9599 -0.44270212 0.6820229 G2M
## Prog_008 Prog 4079891 10540 0.55854051 0.1284359 S
## Prog_014 Prog 2569783 10788 0.07116218 0.3166073 G2M
## old.ident
## Prog_013 Prog
## Prog_019 Prog
## Prog_031 Prog
## Prog_037 Prog
## Prog_008 Prog
## Prog_014 Prog
# Visualize the distribution of cell cycle markers across
RidgePlot(marrow, features = c("PCNA", "TOP2A", "MCM6", "MKI67"), ncol = 2)
# Running a PCA on cell cycle genes reveals, unsurprisingly, that cells separate entirely by
# phase
marrow <- RunPCA(marrow, features = c(s.genes, g2m.genes))
DimPlot(marrow)
在数据归一化期间回归得出细胞周期得分
现在，我们尝试从数据中消除（“回归”）这种异质性。对于Seurat v1.X的用户，这是通过RegressOut实现的。但是，由于此过程的结果存储在归一化后的slot中，因此我们现在将此功能合并到ScaleData()函数中。
对于每个基因，Seurat计算模拟基因表达与S和G2M细胞周期得分之间的关系。该模拟的残差表示“校正后的”表达矩阵，可在下游用于降维分析。
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: CPA3, LMO4, IKZF2, IFITM1, FUT8, MS4A2, SIGLECF, CSRP3, HDC, RAB44
## Negative: PF4, GP1BB, SDPR, F2RL2, RAB27B, SLC14A1, TREML1, PBX1, F2R, TUBA8
# 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)
由于最好的细胞周期marker在组织和物种间极度保守，我们发现此流程能够在不同的数据集上可靠地工作。
可选工作流
上面的过程将删除与细胞周期相关的所有信号。在某些情况下，我们发现这会对下游分析产生负面影响，尤其是在分化过程中（如鼠类造血），在此过程中干细胞处于静止状态，分化的细胞正在增殖（反之亦然）。在这种情况下，将所有细胞周期效应消除，也会使干细胞和祖细胞之间的区别变得模糊。
作为替代方案，我们建议消除G2M和S期评分之间的差异。这意味着分离非循环细胞和循环细胞的信号将保留，但是增殖细胞之间的细胞周期差异（通常是不感兴趣的）将被从数据中剔除。
marrow$CC.Difference <- marrow$S.Score - marrow$G2M.Score
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: HDC, LMO4, CSRP3, IFITM1, FCGR3, HLF, CPA3, PROCR, PGLYRP1, IKZF2
## Negative: GP1BB, PF4, SDPR, F2RL2, TREML1, RAB27B, SLC14A1, PBX1, PLEK, TUBA8
# 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)
文中链接
[1]在此处: https://www.dropbox.com/s/3dby3bjsaf5arrw/cell_cycle_vignette_files.zip?dl=1
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