BinaryPCA {scBFA} | R Documentation

## Performs Binary PCA (as outlined in our paper). This function take the input of gene expression profile and perform PCA on gene detection pattern

### Description

Performs Binary PCA (as outlined in our paper). This function take the input of gene expression profile and perform PCA on gene detection pattern

### Usage

    BinaryPCA(scData, X = NULL, scale. = FALSE, center = TRUE)

### Arguments

`scData` | can be a raw count matrix, in which rows are genes and columns are cells; can be a seurat object; can be a SingleCellExperiment object.
`X` | N by C covariate matrix,e.g batch effect, in which rows are cells,columns are number of covariates. If no such covariates available X = NULL
`scale.` | Logical value isndicating whether the variables should be scaled to have unit variance before the analysis takes place. In general scaling is not advisable, since we think the variance in the gene detection space is potentially associated with celltypes (e.g cell type specific markers)
`center` | Logical value indicating whether the variables should be shifted to be zero centered

### Value

A list with class "prcomp",containing the following components:

sdev: the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix).

rotation: the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors). The function princomp returns this in the element loadings.

x: the rotated data (the centred (and scaled if requested) data multiplied by the rotation matrix) is returned. Hence, cov(x) is the diagonal matrix diag(sdev^2).

center, scale. centering and scaling used, or FALSE.

### Examples

    ## Working with Seurat or SingleCellExperiment object

    library(Seurat)
    library(SingleCellExperiment)

    ## Input expression profile, 5 genes x 3 cells

    GeneExpr = matrix(rpois(15,1),nrow = 5,ncol = 3)
    rownames(GeneExpr) = paste0("gene",seq_len(nrow(GeneExpr)))
    colnames(GeneExpr) = paste0("cell",seq_len(ncol(GeneExpr)))
    celltype = as.factor(sample(c(1,2,3),3,replace = TRUE))

    ## Create cell level technical batches

    batch = sample(c("replicate 1","replicate 2","replicate 2"))
    X = matrix(NA,nrow = length(batch),ncol = 1)
    X[which(batch =="replicate 1"), ] = 0
    X[which(batch =="replicate 2"), ] = 1
    rownames(X) = colnames(GeneExpr)

    ##run BFA with raw count matrix

    bpca_model = BinaryPCA(scData = GeneExpr,X = scale(X))

    ## Create Seurat object for input to BFA

    scData = CreateSeuratObject(counts = GeneExpr,project = "sc",min.cells = 0)

    ## Standardize the covariate matrix should be a default operation

    bpca_model = BinaryPCA(scData = scData, X = scale(X))

    ## Build the SingleCellExperiment object for input to BFA

    ## Set up SingleCellExperiment class

    sce <- SingleCellExperiment(assay = list(counts = GeneExpr))

    ## Standardize the covariate matrix should be a default operation

    bpca_model = BinaryPCA(scData = sce, X = scale(X))

[Package scBFA version 1.4.0 Index]