## ---- 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) ## ----sessionInfo, echo=FALSE-------------------------------------------------- sessionInfo()