scSigR is an R package for generation of signature matrices from single cell data.
Install the development version directly from github (requires devtools)
require(devtools) devtools::install_github("ruppinlab/scSigR") # If you don't want to install the package, load the function using devtools devtools::load_all()library(scSigR) ?scSigR04-13-2021 scSigR can now be used with a Seurat object!
signature <- scSigR::RunSigR( seurat_obj, cell_types=c("CD8T", "CD4Tconv", "Mono/Macro")) > signature[1:5, 1:3] CD8T CD4Tconv Mono/Macro CCR7 3.407659 0.6894390 0.17944693 LEF1 2.566739 0.5078555 0.04899431 MAL 2.775638 0.7251862 0.10114963 MYC 3.341631 1.0512256 0.20400130 CD27 5.074077 2.5608664 0.69854288Alternatively, the input can be a matrix and a meta file with cell ids and cell type information. We use the following input file as an example:
livnat_mat <- readRDS("GBM_forSignature_CV.rds") livnat_meta <- readRDS("GBM_meta_forSignature_CV.rds") > livnat_mat[1:3, 1:3] MGH102-P1-A01 MGH102-P1-A02 MGH102-P1-A03 A1BG 0 0 0 A1BG-AS1 0 0 0 A1CF 0 0 0 > livnat_meta[1:3, 1:4] Cell Sample GBM_Type Cell_Type 1 MGH102-P1-A01 MGH102 Adult Malignant 2 MGH102-P1-A02 MGH102 Adult Malignant 3 MGH102-P1-A03 MGH102 Adult MalignantStep 1: Aggregate Gene expression profiles (default 5, num_gep).
geps <- scSigR::aggregateGEP( livnat_mat, livnat_meta, cell_types=names(table(livnat_meta$Cell_Type)), celltype_column="Cell_Type", scale_factor=10000, num_gep=5, verbose = TRUE) > geps[1:5, 1:3] Macrophage.1 Macrophage.2 Macrophage.3 A1BG 0.018044 0.015958 0.028440 A1BG-AS1 0.014134 0.033334 0.045636 A1CF 0.000578 0.001956 0.000988 A2M 17.243004 21.166668 16.667274 A2M-AS1 0.007664 0.006702 0.009444Step 2: Perform differential expression. One cell type again all others.
degs <- scSigR::deg( gep=as.matrix(geps), cell_types=names(table(livnat_meta$Cell_Type)), num_gep=5, test_alternative="greater", verbose=TRUE) > str(degs) List of 4 $ Macrophage :'data.frame':21394 obs. of 3 variables: ..$ p_value: num [1:21394] 0.166657 0.269061 0.154511 0.000594 0.126444 ... ..$ log2fc : num [1:21394] -0.00547 0.00256 0.000599 4.125409 0.001353 ... ..$ BH : num [1:21394] 0.71124 0.9841 0.66929 0.00798 0.63921 ... $ Malignant :'data.frame':21394 obs. of 3 variables: ..$ p_value: num [1:21394] 0.854381 0.759161 0.006627 0.146536 0.000772 ... ..$ log2fc : num [1:21394] -0.0288 -0.01232 0.00155 -2.3772 0.02977 ... ..$ BH : num [1:21394] 0.9029 0.8149 0.0137 0.1879 0.002 ...Step 3: Find matrix with lowest condition number.
kappas <- scSigR::getKappa( agg_ct=geps, diff_expr=degs, cell_types=names(table(livnat_meta$Cell_Type)), qvalue=0.01, log2fc=1, num_gep=5, G_min=300, G_max=500, verbose=TRUE) > head(kappas) gene_iteration condition_number 28 327 3.522208 30 329 3.522257 29 328 3.522266 31 330 3.522270 1 300 3.522290 2 301 3.522322Get lowest condition number
optimalG <- kappas[kappas$condition_number==min(kappas$condition_number), ]$gene_iterationStep 4: Get signature matrix.
signature <- scSigR::getSignatureMatrix( agg_ct=geps, diff_expr = degs, cell_types = names(table(livnat_meta$Cell_Type)), qvalue=0.01, log2fc=1, optimalG=optimalG, num_gep=5) > signature[1:5, 1:4] Macrophage Malignant Oligodendrocyte T-cell C1QB 69.74890 3.772358e-05 0.0046928 0.00000 CCL3 61.06000 3.427602e-03 0.0000000 0.00000 FCGR3A 42.27122 4.432520e-04 0.0000000 0.00000 HLA-DRA 100.50797 4.126496e-02 0.0000000 4.17466 FCER1G 40.07535 1.700346e-02 0.0000000 0.00000