## ----message=FALSE, warning=FALSE, include=FALSE------------------------------ library(wheatmap) library(dplyr) options(rmarkdown.html_vignette.check_title = FALSE) ## ----nh1, message=FALSE, warning=FALSE---------------------------------------- library(sesame) sesameDataCache() ## ----nh3, message=FALSE, eval=FALSE------------------------------------------- # betas = openSesame(sprintf("~/Downloads/GSM4411982", tmp), prep="SHCDPM") ## ----nh4, eval=FALSE---------------------------------------------------------- # ## equivalent to the above openSesame call # betas = getBetas(matchDesign(pOOBAH(dyeBiasNL(inferInfiniumIChannel( # prefixMaskButC(inferSpecies(readIDATpair( # "~/Downloads/GSM4411982")))))))) ## ----nh13, message=FALSE, eval=TRUE------------------------------------------- sdf = sesameDataGet("Mammal40.1.SigDF") # an example SigDF inferSpecies(sdf, return.species = TRUE) ## ----nh14, message=FALSE------------------------------------------------------ ## showing the candidate species with top scores head(sort(inferSpecies(sdf, return.auc = TRUE), decreasing=TRUE)) ## ----nh15, eval=FALSE--------------------------------------------------------- # sdf_mouse <- updateSigDF(sdf, species="mus_musculus") ## ----nh2, message=FALSE, eval=FALSE------------------------------------------- # betas = openSesame("~/Downloads/204637490002_R05C01", prep="TQCDPB") ## ----nh9, message=FALSE------------------------------------------------------- sdf = sesameDataGet("MM285.1.SigDF") # an example dataset inferStrain(sdf, return.strain = TRUE) # return strain as a string sdf_after = inferStrain(sdf) # update mask and col by strain inference sum(sdf$mask) # before strain inference sum(sdf_after$mask) # after strain inference ## ----nh10, fig.width=6, fig.height=4, message=FALSE--------------------------- library(ggplot2) p = inferStrain(sdf, return.probability = TRUE) df = data.frame(strain=names(p), probs=p) ggplot(data = df, aes(x = strain, y = probs)) + geom_bar(stat = "identity", color="gray") + ggtitle("Strain Probabilities") + ylab("Probability") + xlab("") + scale_x_discrete(position = "top") + theme(axis.text.x = element_text(angle = 90, vjust=0.5, hjust=0), legend.position = "none") ## ----nh7, message=FALSE------------------------------------------------------- sdf = sesameDataGet('MM285.1.SigDF') sum(is.na(openSesame(sdf, prep="TQCDPB"))) sum(is.na(openSesame(sdf, prep="TQCD0PB"))) ## ----------------------------------------------------------------------------- sessionInfo()