In espors/idepGolem: Integrated Differential Expression and Pathway Analysis

knitr::opts_chunk$set(echo = params$printcode)

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This html document contains what parameters values were selected on the IDEP interface of the "Clustering" tab. It also includes the plots generated from those selections.

for (i in 1:length(params)) {
  # exclude loaded data & sample info
  if (names(params)[i] != "sample_info" && names(params)[i] != "pre_processed_data" && names(params)[i] != "all_gene_names") {
    cat(paste0(names(params)[i], ": ", params[[i]], "\n"))
  }
}

Pre-processing Summary

r params$descr

Clustering Summary

The following plots were generated using IDEP with the following filters: Include only the top r params$n_genes genes using r params$select_gene_id to label color scheme: r params$heatmap_color_select distance: r switch(params$dist_function, "1" = "Hierarchical", "2" = "K-Means")

Linkage: r params$hclust_function

Cut off Z-score: r params$heatmap_cutoff

Center Genes: r params$gene_centering

Gene normalize: r params$gene_normalize

Do not cluster samples: r params$no_sample_clustering

Show row Dendogram: r params$show_row_dend

Paragraph

The following plots were generated using IDEP, including only the top r params$n_genes genes, using r params$select_gene_id to label, r switch(params$dist_function, "1" = "Hierarchical", "2" = "K-Means") distance,r params$hclust_function linkage, Cut off Z-score: r params$heatmap_cutoff, Center Genes: r params$gene_centering. genes are r if(!params$gene_normalize) paste0(" not ") normalized.

Elbow Plot

heatmap_data <- process_heatmap_data(
  data = params$pre_processed_data,
  n_genes_max = params$n_genes,
  # n_genes_min = 50,
  gene_centering = params$gene_centering,
  gene_normalize = params$gene_normalize,
  sample_centering = TRUE,
  sample_normalize = TRUE,
  all_gene_names = params$all_gene_names,
  select_gene_id = params$select_gene_id
)

k_means_elbow(heatmap_data = heatmap_data)

Heatmap

ht <- heatmap_main(
  data = heatmap_data,
  cluster_meth = params$cluster_meth,
  heatmap_cutoff = params$heatmap_cutoff,
  sample_info = params$sample_info,
  select_factors_heatmap = params$list_factors_heatmap,
  dist_funs = dist_functions(),
  dist_function = params$dist_function,
  hclust_function = params$hclust_function,
  sample_clustering = params$sample_clustering,
  heatmap_color_select = params$heatmap_color_select,
  row_dend = params$show_row_dend,
  k_clusters = params$k_clusters,
  re_run = FALSE,
  selected_genes = params$selected_genes
)

SD Density

sd_density(
  data = params$pre_processed_data,
  n_genes_max = params$n_genes
)

r ifelse(as.character(params$dist_function) == "1","## Tree (Hierarchical)","")

if (params$dist_function == 1) {
  draw_sample_tree(
    tree_data = params$pre_processed_data,
    gene_centering = params$gene_centering,
    gene_normalize = params$gene_normalize,
    sample_centering = FALSE,
    sample_normalize = FALSE,
    hclust_funs = hcluster_functions(),
    hclust_function = params$hclust_function,
    dist_funs = dist_functions(),
    dist_function = params$dist_function
  )
}