Author: Krishan Gupta
ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. Takes in the complete filtered and normalized read count matrix, the location of the two sub-populations and the number of cores to be used.
The developer version of the R package can be installed with the following R commands:
or can be installed with the following R commands:
This vignette uses a tung dataset already inbuilt in same package, to demonstrate a standard pipeline. This vignette can be used as a tutorial as well. Ref: Tung, P.-Y.et al.Batch effects and the effective design of single-cell geneexpression studies.Scientific reports7, 39921 (2017).
Libraries need to be loaded before running.
samples<-list() samples$count<-ROSeq::L_Tung_single$NA19098_NA19101_count samples$group<-ROSeq::L_Tung_single$NA19098_NA19101_group samples$count[1:5,1:5] #> NA19098.r1.A01 NA19098.r1.A02 NA19098.r1.A03 NA19098.r1.A04 #> ENSG00000237683 0 0 0 1 #> ENSG00000187634 0 0 0 0 #> ENSG00000188976 3 6 1 3 #> ENSG00000187961 0 0 0 0 #> ENSG00000187583 0 0 0 0 #> NA19098.r1.A05 #> ENSG00000237683 0 #> ENSG00000187634 0 #> ENSG00000188976 4 #> ENSG00000187961 0 #> ENSG00000187583 0