## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, message = TRUE, warning = FALSE, cache = FALSE, fig.align = 'center', fig.width = 5, fig.height = 4, crop = NULL ) ## ----installation, eval=FALSE------------------------------------------------- # if(!requireNamespace('BiocManager', quietly = TRUE)) # install.packages('BiocManager') # # BiocManager::install("BioNERO") ## ----load_package------------------------------------------------------------- # Load package after installation library(BioNERO) set.seed(12) # for reproducibility ## ----inspect_data------------------------------------------------------------- data(zma.se) zma.se data(osa.se) osa.se ## ----create_list_consensus---------------------------------------------------- # Preprocess data and keep top 2000 genes with highest variances filt_zma <- exp_preprocess(zma.se, variance_filter = TRUE, n = 2000) # Create different subsets by resampling data zma_set1 <- filt_zma[, sample(colnames(filt_zma), size=22, replace=FALSE)] zma_set2 <- filt_zma[, sample(colnames(filt_zma), size=22, replace=FALSE)] colnames(zma_set1) colnames(zma_set2) # Create list zma_list <- list(set1 = zma_set1, set2 = zma_set2) length(zma_list) ## ----consensus_sft, message=FALSE--------------------------------------------- cons_sft <- consensus_SFT_fit(zma_list, setLabels = c("Maize 1", "Maize 2"), cor_method = "pearson") ## ----sft_results, fig.width=8------------------------------------------------- powers <- cons_sft$power powers cons_sft$plot ## ----consensus_modules_identification----------------------------------------- consensus <- consensus_modules(zma_list, power = powers, cor_method = "pearson") names(consensus) head(consensus$genes_cmodules) ## ----consensus_trait_cor, fig.width=5, fig.height=5--------------------------- consensus_trait <- consensus_trait_cor(consensus, cor_method = "pearson") head(consensus_trait) ## ----plot-consensus-trait-cor, fig.width = 5, fig.height = 6------------------ plot_module_trait_cor(consensus_trait) ## ----load_orthogroups--------------------------------------------------------- data(og.zma.osa) head(og.zma.osa) ## ----genes2orthogorups-------------------------------------------------------- # Store SummarizedExperiment objects in a list zma_osa_list <- list(osa = osa.se, zma = zma.se) # Collapse gene-level expression to orthogroup-level ortho_exp <- exp_genes2orthogroups(zma_osa_list, og.zma.osa, summarize = "mean") # Inspect new expression data ortho_exp$osa[1:5, 1:5] ortho_exp$zma[1:5, 1:5] ## ----create_list_preservation------------------------------------------------- # Preprocess data and keep top 1000 genes with highest variances ortho_exp <- lapply(ortho_exp, exp_preprocess, variance_filter=TRUE, n=1000) # Check orthogroup number sapply(ortho_exp, nrow) ## ----gcn_inference------------------------------------------------------------ # Calculate SFT power power_ortho <- lapply(ortho_exp, SFT_fit, cor_method="pearson") # Infer GCNs gcns <- lapply(seq_along(power_ortho), function(n) exp2gcn(ortho_exp[[n]], SFTpower = power_ortho[[n]]$power, cor_method = "pearson") ) length(gcns) ## ----preservation------------------------------------------------------------- # Using rice as reference and maize as test pres <- module_preservation(ortho_exp, ref_net = gcns[[1]], test_net = gcns[[2]], algorithm = "netrep") ## ----singleton---------------------------------------------------------------- # Sample 50 random genes genes <- sample(rownames(zma.se), size = 50) is_singleton(genes, og.zma.osa) ## ----sessionInfo, echo=FALSE-------------------------------------------------- sessionInfo()