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.


# 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:  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))

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
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)