zyPlot: Visualize the posterior probability of being expressed for a...
In haowulab/SC2P: Two phase differential expression for single-cell RNA-seq

Description Usage Arguments Details Author(s) See Also Examples

View source: R/zyPlot.R

This functions plots the posterior probability of being expressed versus expressions level for a particular gene cross many cells.

1	zyPlot(name, sc2p.obj, group.name, log2scale = TRUE, showname = TRUE, annotation = NULL)

`name`	Gene name. This should either match one of the `featureNames` of the `sc2p.obj`, or match a name in a separate `annotation` vector.
`sc2p.obj`	An object of sc2pSet, result from the eset2Phase function.
`group.name`	Character for the name of factors indicating the experimental conditions. This has to be the name of a column in pData(sc2p.obj) data frame.
`log2scale`	Logical to indicate whether to plot expression level in log2 scale. Default is TRUE.
`showname`	Logical to indicate whether to show the name of genes as the tile of the figure.
`annotation`	A character vector of alternative gene names, with length equal the number of genes in the `sc2p.obj`. If left as default `NULL`, the `featureNames` of the `sc2p.obj` is used.

In general, the probability of being expressed for a gene is higher when the expression level is high. However, the probability is also affected by other cell- and gene-specific factors. For example, cell with higher total depth will have greater values for all genes belong to that cell. Genes with higher average expression levels will have higher background, thus the threshold for expression will be higher than genes with lower average expression.

This function provides visualization of the expression status and expression level.

Jamie (Yi) Zhang <yi_zhang@brown.edu>

eset2Phase

## Not run: 
data(brain_scRNAseq)

## assemble expressionset
colnames(Y) <- rownames(design)
phenoData <- new("AnnotatedDataFrame", data=design)
eset <- ExpressionSet(assayData=Y, phenoData=phenoData)

## infer latent status
data <- eset2Phase(eset)

## visualize a gene
zyPlot(rownames(data)[1], data, group.name="celltype")

## End(Not run)