Cell-Cycle Scoring and Regression
We demonstrate how to mitigate the effects of cell cycle heterogeneity in scRNA-seq data by calculating cell cycle phase scores based on canonical markers, and regressing these out of the data during pre-processing. We demonstrate this on a dataset of murine hematopoietic progenitors (Nestorowa et al., Blood 2016.You can download the files needed to run this vignette here.
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))
If we run a PCA on our object, using the variable genes we found in
FindVariableFeatures above, we see that while most of the variance can be explained by lineage, PC8 and PC10 are split on cell-cycle genes including TOP2A and MKI67. We will attempt to regress this signal from the data, so that cell-cycle heterogneity does not contribute to PCA or downstream analysis.
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: F13A1, LY86, CFP, IRF8, CSF1R, TIFAB, IFI209, CCR2, TNS4, MS4A6C ## Negative: HDC, CPA3, PGLYRP1, MS4A3, NKG7, UBE2C, CCNB1, NUSAP1, PLK1, FUT8 ## 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: H2AFX, FAM64A, ZFP383, NUSAP1, CDC25B, CENPF, GBP10, TOP2A, GBP6, GFRA1 ## Negative: CTSW, XKRX, PRR5L, RORA, MBOAT4, A630014C17RIK, ZFP105, COL9A3, CLEC2I, TRAT1
DimHeatmap(marrow, dims = c(8, 10))
Assign Cell-Cycle Scores
First, we assign each cell a score, based on its expression of G2/M and S phase markers. These marker sets should be anticorrelated in their expression levels, and cells expressing neither are likely not cycling and in G1 phase.
We assign scores in the
CellCycleScoring function, which stores S and G2/M scores in object meta data, along with the predicted classification of each cell in either G2M, S or G1 phase.
CellCycleScoring can also set the identity of the Seurat object to the cell-cycle phase by passing
set.ident = TRUE (the original identities are stored as
old.ident). Please note that Seurat does not use the discrete classifications (G2M/G1/S) in downstream cell cycle regression. Instead, it uses the quantitative scores for G2M and S phase. However, we provide our predicted classifications in case they are of interest.
marrow <- CellCycleScoring(marrow, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE) # view cell cycle scores and phase assignments head(marrow[])
# Visualize the distribution of cell cycle markers across RidgePlot(marrow, features = c("PCNA", "TOP2A", "MCM6", "MKI67"), ncol = 2)