NSSHeat: Construct heatmap for non-steady state (NSS) analysis

In bakR: Analyze and Compare Nucleotide Recoding RNA Sequencing Datasets

Construct heatmap for non-steady state (NSS) analysis

Description

This uses the output of bakR and a differential expression analysis software to construct a dataframe that can be passed to pheatmap::pheatmap(). This heatmap will display the result of a steady-state quasi-independent analysis of NR-seq data.

Usage

NSSHeat(
  bakRFit,
  DE_df,
  bakRModel = c("MLE", "Hybrid", "MCMC"),
  DE_cutoff = 0.05,
  bakR_cutoff = 0.05,
  Exp_ID = 2,
  lid = 4
)

Arguments

bakRFit	bakRFit object
DE_df	dataframe of required format with differential expression analysis results. See Further-Analyses vignette for details on what this dataframe should look like
bakRModel	Model fit from which bakR implementation should be used? Options are MLE, Hybrid, or MCMC
DE_cutoff	padj cutoff for calling a gene differentially expressed
bakR_cutoff	padj cutoff for calling a fraction new significantly changed
Exp_ID	Exp_ID of experimental sample whose comparison to the reference sample you want to use. Only one reference vs. experimental sample comparison can be used at a time
lid	Maximum absolute value for standardized score present in output. This is for improving aesthetics of any heatmap generated with the output.

Value

returns data frame that can be passed to pheatmap::pheatmap()

Examples

# Simulate small dataset
sim <- Simulate_bakRData(100, nreps = 2)

# Analyze data with bakRFit
Fit <- bakRFit(sim$bakRData)

# Number of features that made it past filtering
NF <- nrow(Fit$Fast_Fit$Effects_df)

# Simulate mock differential expression data frame
DE_df <- data.frame(XF = as.character(1:NF),
                       L2FC_RNA = stats::rnorm(NF, 0, 2))

DE_df$DE_score <- DE_df$L2FC_RNA/0.5
DE_df$DE_se <- 0.5

DE_df$DE_pval <- 2*stats::dnorm(-abs(DE_df$DE_score))
DE_df$DE_padj <- 2*stats::p.adjust(DE_df$DE_pval, method = "BH")

# perform NSS analysis
NSS_analysis <- DissectMechanism(bakRFit = Fit,
               DE_df = DE_df,
               bakRModel = "MLE")

bakR documentation built on June 22, 2024, 6:55 p.m.