In espors/idepGolem: Integrated Differential Expression and Pathway Analysis

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

::: {style="color: Blue"} ## If this site has contributed to your work, please cite our article: Ge, S.X., Son, E.W. & Yao, R. iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics 19, 534 (2018). ::: ##

This html document contains what parameters values were selected on the IDEP interface. 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] != "loaded_data") {
    cat(paste0(names(params)[i], ": ", params[[i]], "\n"))
  }
}

# devtools::load_all()
library(idepGolem)

Summary

r switch(params$data_file_format, "1" = "Read Counts data", "2" = "Normalized expression values", "3" = "Log Fold Change and corrected P values data") were analyzed using iDEP v1.0 [Citation]. r switch(params$data_file_format, "1" = paste0("The data was first filtered to remove reads below ", params$min_counts," CPM in at least ", params$n_min_samples_count, " sample(s). Then the data was transformed with ", switch(params$counts_transform, "1" = paste0("EdgeR using a pseudocount of ", params$counts_log_start), "2" = "VST: Variance Stabilizing Transformation", "3" = "Regularized log"), ". Missing values were imputed using ", params$missing_value, "."), "2" = paste0("The data was ",switch(toString(params$log_transform_fpkm), "FALSE" = "not log transformed.", "TRUE" = paste0(" log transformed with a psuedocount of ", params$log_start_fpkm, ".")), " Then it was filtered to only keep genes above level ", params$low_filter_fpkm, " in at least ", params$n_min_samples_fpkm, " sample(s). Missing values were imputed using ", params$missing_value, "."))

Plots

processed_data <- idepGolem::pre_process(
  data = params$loaded_data,
  missing_value = params$missing_value,
  data_file_format = params$data_file_format,
  low_filter_fpkm = params$low_filter_fpkm,
  n_min_samples_fpkm = params$n_min_samples_fpkm,
  log_transform_fpkm = params$log_transform_fpkm,
  log_start_fpkm = params$log_start_fpkm,
  min_counts = params$min_counts,
  n_min_samples_count = params$n_min_samples_count,
  counts_transform = params$counts_transform,
  counts_log_start = params$counts_log_start,
  no_fdr = params$no_fdr
)

if (params$data_file_format == 1) {
  idepGolem::total_counts_ggplot(
    counts_data = processed_data$raw_counts,
    sample_info = params$sample_info,
    type = "RAW",
    plots_color_select = params$plots_color_select
  )
}

idepGolem::eda_scatter(
  processed_data = processed_data$data,
  plot_xaxis = params$scatter_x,
  plot_yaxis = params$scatter_y
)

idepGolem::eda_boxplot(
  processed_data = processed_data$data,
  sample_info = params$sample_info,
  plots_color_select = params$plots_color_select
)

idepGolem::eda_density(
  processed_data = processed_data$data,
  sample_info = params$sample_info,
  plots_color_select = params$plots_color_select
)

idepGolem::mean_sd_plot(
  processed_data = processed_data$data,
  heat_cols = params$sd_color,
  rank = params$rank
)

idepGolem::gene_counts_ggplot(
  counts_data = params$loaded_data,
  sample_info = params$sample_info,
  type = "Raw",
  all_gene_info = params$all_gene_info,
  plots_color_select = params$plots_color_select
)