cyclone: Cell cycle phase classification
In scran: Methods for Single-Cell RNA-Seq Data Analysis

Description Usage Arguments Details Value Description of the score calculation Author(s) References See Also Examples

Classify single cells into their cell cycle phases based on gene expression data.

cyclone(x, ...)

## S4 method for signature 'ANY'
cyclone(
  x,
  pairs,
  gene.names = rownames(x),
  iter = 1000,
  min.iter = 100,
  min.pairs = 50,
  BPPARAM = SerialParam(),
  verbose = FALSE,
  subset.row = NULL
)

## S4 method for signature 'SummarizedExperiment'
cyclone(x, ..., assay.type = "counts")

`x`	A numeric matrix-like object of gene expression values where rows are genes and columns are cells. Alternatively, a SummarizedExperiment object containing such a matrix.
`...`	For the generic, additional arguments to pass to specific methods. For the SummarizedExperiment method, additional arguments to pass to the ANY method.
`pairs`	A list of data.frames produced by `sandbag`, containing pairs of marker genes.
`gene.names`	A character vector of gene names, with one value per row in `x`.
`iter`	An integer scalar specifying the number of iterations for random sampling to obtain a cycle score.
`min.iter`	An integer scalar specifying the minimum number of iterations for score estimation.
`min.pairs`	An integer scalar specifying the minimum number of pairs for cycle estimation.
`BPPARAM`	A BiocParallelParam object to use for parallel processing across cells.
`verbose`	A logical scalar specifying whether diagnostics should be printed to screen.
`subset.row`	See `?"scran-gene-selection"`.
`assay.type`	A string specifying which assay values to use, e.g., `"counts"` or `"logcounts"`.

This function implements the classification step of the pair-based prediction method described by Scialdone et al. (2015). To illustrate, consider classification of cells into G1 phase. Pairs of marker genes are identified with sandbag, where the expression of the first gene in the training data is greater than the second in G1 phase but less than the second in all other phases. For each cell, cyclone calculates the proportion of all marker pairs where the expression of the first gene is greater than the second in the new data x (pairs with the same expression are ignored). A high proportion suggests that the cell is likely to belong in G1 phase, as the expression ranking in the new data is consistent with that in the training data.

Proportions are not directly comparable between phases due to the use of different sets of gene pairs for each phase. Instead, proportions are converted into scores (see below) that account for the size and precision of the proportion estimate. The same process is repeated for all phases, using the corresponding set of marker pairs in pairs. Cells with G1 or G2M scores above 0.5 are assigned to the G1 or G2M phases, respectively. (If both are above 0.5, the higher score is used for assignment.) Cells can be assigned to S phase based on the S score, but a more reliable approach is to define S phase cells as those with G1 and G2M scores below 0.5.

Pre-trained classifiers are provided for mouse and human datasets, see ?sandbag for more details. However, note that the classifier may not be accurate for data that are substantially different from those used in the training set, e.g., due to the use of a different protocol. In such cases, users can construct a custom classifier from their own training data using the sandbag function. This is usually necessary for other model organisms where pre-trained classifiers are not available.

Users should not filter out low-abundance genes before applying cyclone. Even if a gene is not expressed in any cell, it may still be useful for classification if it is phase-specific. Its lack of expression relative to other genes will still yield informative pairs, and filtering them out would reduce power.

A list is returned containing:

phases:: A character vector containing the predicted phase for each cell.
scores:: A data frame containing the numeric phase scores for each phase and cell (i.e., each row is a cell).
normalized.scores:: A data frame containing the row-normalized scores (i.e., where the row sum for each cell is equal to 1).

To make the proportions comparable between phases, a distribution of proportions is constructed by shuffling the expression values within each cell and recalculating the proportion. The phase score is defined as the lower tail probability at the observed proportion. High scores indicate that the proportion is greater than what is expected by chance if the expression of marker genes were independent (i.e., with no cycle-induced correlations between marker pairs within each cell).

By default, shuffling is performed iter times to obtain the distribution from which the score is estimated. However, some iterations may not be used if there are fewer than min.pairs pairs with different expression, such that the proportion cannot be calculated precisely. A score is only returned if the distribution is large enough for stable calculation of the tail probability, i.e., consists of results from at least min.iter iterations.

Note that the score calculation in cyclone is slightly different from that described originally by Scialdone et al. The original code shuffles all expression values within each cell, while in this implementation, only the expression values of genes in the marker pairs are shuffled. This modification aims to use the most relevant expression values to build the null score distribution.

Antonio Scialdone, with modifications by Aaron Lun

Scialdone A, Natarajana KN, Saraiva LR et al. (2015). Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods 85:54–61

sandbag, to generate the pairs from reference data.

set.seed(1000)
library(scuttle)
sce <- mockSCE(ncells=200, ngenes=1000)

# Constructing a classifier:
is.G1 <- which(sce$Cell_Cycle %in% c("G1", "G0"))
is.S <- which(sce$Cell_Cycle=="S")
is.G2M <- which(sce$Cell_Cycle=="G2M")
out <- sandbag(sce, list(G1=is.G1, S=is.S, G2M=is.G2M))

# Classifying a new dataset:
test <- mockSCE(ncells=50)
assignments <- cyclone(test, out)
head(assignments$scores)
table(assignments$phases)