## ----echo=FALSE--------------------------------------------------------------- htmltools::img(src = knitr::image_uri("islet_hex_2.png"), alt = 'logo', style = 'position:absolute; top:0; left:0; padding:10px; height:280px') ## ----eval = FALSE, message = FALSE-------------------------------------------- # if (!require("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # # BiocManager::install("ISLET") ## ----eval = TRUE, message = FALSE--------------------------------------------- library(ISLET) data(GE600) ls() GE600_se ## ----eval = TRUE, message = FALSE--------------------------------------------- assays(GE600_se)$counts[1:5, 1:6] ## ----eval = TRUE, message = FALSE--------------------------------------------- colData(GE600_se) ## ----eval = TRUE, message = FALSE--------------------------------------------- study123input <- dataPrep(dat_se=GE600_se) ## ----eval = TRUE, message = FALSE--------------------------------------------- study123input ## ----eval = TRUE, message = FALSE--------------------------------------------- #Use ISLET for deconvolution res.sol <- isletSolve(input=study123input) ## ----eval = TRUE, message = FALSE--------------------------------------------- #View the deconvolution results caseVal <- caseEst(res.sol) ctrlVal <- ctrlEst(res.sol) length(caseVal) #For cases, a list of 6 cell types' matrices. length(ctrlVal) #For controls, a list of 6 cell types' matrices. caseVal$Bcells[1:5, 1:4] #view the reference panels for B cells, for the first 5 genes and first 4 subjects, in Case group. ctrlVal$Bcells[1:5, 1:4] #view the reference panels for B cells, for the first 5 genes and first 4 subjects, in Control group. ## ----eval = TRUE, message = FALSE--------------------------------------------- #Test for csDE genes res.test <- isletTest(input=study123input) ## ----eval = TRUE, message = FALSE--------------------------------------------- #View the test p-values head(res.test) ## ----eval = TRUE, message = FALSE--------------------------------------------- #(1) Example dataset for 'slope' test data(GE600age) ls() ## ----eval = TRUE, message = FALSE--------------------------------------------- assays(GE600age_se)$counts[1:5, 1:6] ## ----eval = TRUE, message = FALSE--------------------------------------------- colData(GE600age_se) ## ----eval = TRUE-------------------------------------------------------------- #(2) Data preparation study456input <- dataPrepSlope(dat_se=GE600age_se) ## ----eval = TRUE-------------------------------------------------------------- #(3) Test for slope effect(i.e. age) difference in csDE testing age.test <- isletTest(input=study456input) ## ----eval = TRUE, message = FALSE--------------------------------------------- #View the test p-values head(age.test) ## ----eval = TRUE, message = FALSE--------------------------------------------- dat123 <- implyDataPrep(sim_se=GE600_se) ## ----eval = TRUE, message = FALSE--------------------------------------------- dat123 ## ----eval = TRUE, message = FALSE--------------------------------------------- #Use imply for deconvolution result <- imply(dat123) ## ----eval = TRUE, message = FALSE--------------------------------------------- #View the subject-specific and cell-type-specific reference panels solved #by linear mixed-effect models of the first subject result$p.ref[,,1] ## ----eval = TRUE, message = FALSE--------------------------------------------- #View the improved cell deconvolution results head(result$imply.prop) tail(result$imply.prop) ## ----sessionInfo, echo=FALSE-------------------------------------------------- sessionInfo()