A simplized version of R package scHCL, only keep scHCL function but make it faster.
The origianl R package scHCL is here, great respect to Human Cell Landscape project.
# devtools required
> install.packages("devtools")
> devtools::install_github("sajuukLyu/scHCLlite")> library(scHCLlite)
# hcl_lung is an example expression matrix from HCL project.
> data(hcl_lung)
> dim(hcl_lung)
[1] 2884 160
# 2884 genes expression value of 80 cells
> head(rownames(hcl_lung))
[1] "Mbp" "Tspan2" "Plp1" "Ermn" "Mag" "Mal"Like scHCL, scHCLlite has two parameters: scdata, a single-cell gene expression matrix with each row a gene and each column a cell and num, a number setting how many most similar cell types should be returned.
However, the gene names should be the symbol name of human gene. So we need to uppercase the row names of input data.
> rownames(hcl_lung) <- toupper(rownames(hcl_lung))
> head(rownames(hcl_lung))
[1] "MBP" "TSPAN2" "PLP1" "ERMN" "MAG" "MAL"
# now we can run scHCLlite.
> hcl_result <- scHCLlite(scdata = hcl_lung, num = 3)
1471 genes used, calculating correlation coefficient...
20 types expected, extracting top 3 results...The return of scHCLlite() is a list which contains 4 parts, very similar to scHCL().
- cors_matrix: the pearson correlation coefficient matrix of each cell and cell type.
- top_cors: equals to
num. - scHCL: the most relevant cell type for each query cell.
- scHCL_similarity: the top n relevant cell types for each query cell.