postProcessLinRegMatrix {CNVScope} | R Documentation |
Takes a linear regression matrix and sets infinites to a finite value, and changes the sign to match the sign of the correlation for each value.
postProcessLinRegMatrix( input_matrix, LM_mat, cor_type = "pearson", inf_replacement_val = 300 )
input_matrix |
The input matrix, which consists of bins and samples (no LM or correlation has been done on the segmentation values) |
LM_mat |
The linear regression matrix, with rows and columns consisting of bins and the values being the negative log p-value between them. |
cor_type |
The correlation type ("pearson" (linear), "spearman" (rank), "kendall"(also rank-based)). Rank correlations capture nonlinear relationships as well as linear. Passed to stats::cor's method parameter. |
inf_replacement_val |
the value for which infinites are replaced, by default 300. |
The output matrix, or if using slurm, the slurm job object (which should be saved as an rds file and reloaded when creating the output matrix).
inputmat<-matrix(runif(15),nrow=3) colnames(inputmat)<-c("chr2_1_1000","chr2_1001_2000","chr2_2001_3000","chr2_3001_4000", "chr2_4001_5000") rownames(inputmat)<-c("PAFPJK","PAKKAT","PUFFUM") outputmat<-matrix(runif(15),nrow=3) outputmat<-cor(inputmat)*matrix(runif(25,-30,500),nrow=5) diag(outputmat)<-Inf postProcessLinRegMatrix(input_matrix=t(inputmat),LM_mat=outputmat,cor_type="pearson", inf_replacement_val=300)