## ----knitr-options, echo=FALSE, warning=FALSE--------------------------------- ## To render an HTML version that works nicely with github and web pages, do: ## rmarkdown::render("vignettes/vignette.Rmd", "all") library(knitr) opts_chunk$set(fig.align = 'center', fig.width = 6, fig.height = 5, dev = 'png') #knitr::opts_chunk$set(echo=FALSE, fig.path='cellity/plot-', cache=TRUE) library(ggplot2) theme_set(theme_bw(12)) ## ----eval=TRUE, message=FALSE, warning=FALSE---------------------------------- library(cellity) data(sample_counts) data(sample_stats) ## ----eval=TRUE, message=FALSE, warning=FALSE, results='hide', error=FALSE----- sample_counts_nm <- normalise_by_factor(sample_counts, colSums(sample_counts)) ## ----eval=TRUE, message=FALSE, warning=FALSE, results='hide', error=FALSE----- sample_features <- extract_features(sample_counts_nm, sample_stats) ## ----eval=TRUE, message=FALSE, warning=FALSE, error=FALSE--------------------- if (requireNamespace("org.Hs.eg.db", quietly = TRUE)) { #MAKE SURE YOU HAVE THE APPROPRIATE ORGANISM INSTALLED #You would instsall the library below by: #if (!requireNamespace("BiocManager", quietly=TRUE)) #install.packages("BiocManager") #BiocManager::install("org.Hs.eg.db") library(org.Hs.eg.db) data("extra_human_genes") data("feature_info") GO_terms <- feature_info[[1]] common_features <- feature_info[[2]] features_human <- extract_features( sample_counts_nm, sample_stats, common_features = common_features, GO_terms = GO_terms, extra_genes = extra_human_genes, organism = "human") } ## ----eval=TRUE---------------------------------------------------------------- sample_features_all <- sample_features[[1]] sample_qual_pca <- assess_cell_quality_PCA(sample_features_all) ## ----eval=TRUE---------------------------------------------------------------- data(training_mES_features) training_mES_features_all <- training_mES_features[[1]] training_quality_PCA_allF <- assess_cell_quality_PCA( training_mES_features_all, file = "./training_quality_PCA_allF.pdf") ## ----eval=TRUE, warning=FALSE, message=FALSE---------------------------------- if (requireNamespace("caret", quietly = TRUE)) { library(caret) data(training_mES_labels) lvs <- c("0", "1") truth <- factor(training_mES_labels[,2],levels = rev(lvs)) pred <- factor(training_quality_PCA_allF[,2], levels = rev(lvs)) confusionMatrix(pred, truth) } ## ----eval=TRUE---------------------------------------------------------------- training_mES_features_common <- training_mES_features[[2]] training_quality_PCA_commonF <- assess_cell_quality_PCA( training_mES_features_common, file = "./training_quality_PCA_commonF.pdf") ## ----eval=TRUE, , warning=FALSE, message=FALSE-------------------------------- if (requireNamespace("caret", quietly = TRUE)) { pred <- factor(training_quality_PCA_commonF[,2], levels = rev(lvs)) confusionMatrix(pred, truth) } ## ----eval=TRUE---------------------------------------------------------------- data(mES1_features) data(mES1_labels) ## ----eval=TRUE---------------------------------------------------------------- data(param_mES_all) mES1_features_all <- mES1_features[[1]] mES1_quality_SVM <- assess_cell_quality_SVM( training_mES_features_all, training_mES_labels[,2], param_mES_all, mES1_features_all) ## ----eval=TRUE---------------------------------------------------------------- if (requireNamespace("caret", quietly = TRUE)) { truth <- factor(mES1_labels[,2],levels = rev(lvs)) pred <- factor(mES1_quality_SVM[,2], levels = rev(lvs)) confusionMatrix(pred, truth) } ## ----eval=TRUE---------------------------------------------------------------- data(param_mES_common) training_mES_features_common <- training_mES_features[[2]] mES1_features_common <- mES1_features[[2]] mES1_quality_SVM_common <- assess_cell_quality_SVM( training_mES_features_common, training_mES_labels[,2], param_mES_common, mES1_features_common) ## ----eval=TRUE---------------------------------------------------------------- if (requireNamespace("caret", quietly = TRUE)) { truth <- factor(mES1_labels[,2],levels = rev(lvs)) pred <- factor(mES1_quality_SVM_common[,2], levels = rev(lvs)) confusionMatrix(pred, truth) } ## ----eval=TRUE---------------------------------------------------------------- #PCA QUALITY mES1_quality_PCA<-assess_cell_quality_PCA(mES1_features_all) mES1_quality_SVM <- assess_cell_quality_SVM( training_mES_features_all, training_mES_labels[,2], param_mES_all, mES1_features_all) if (requireNamespace("caret", quietly = TRUE)) { truth <- factor(mES1_labels[,2],levels = rev(lvs)) pred_PCA <- factor(mES1_quality_PCA[,2], levels = rev(lvs)) pred_SVM <- factor(mES1_quality_SVM[,2], levels = rev(lvs)) c_PCA<-confusionMatrix(pred_PCA, truth) print("PCA accuracy") print(c_PCA$byClass[1:2]) c_SVM<-confusionMatrix(pred_SVM, truth) print("SVM accuracy") print(c_SVM$byClass[1:2]) } ## ----eval=TRUE---------------------------------------------------------------- print(length(which((mES1_quality_PCA[,2]==mES1_quality_SVM[,2])==TRUE))/nrow(mES1_labels))