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Construct heatmap for non-steady state (NSS) analysis

Source: R/NSSHeat.R
NSSHeat.Rd

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

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

# Analyze data with bakRFit
Fit <- bakRFit(sim$bakRData)
#> Finding reliable Features
#> Filtering out unwanted or unreliable features
#> Processing data...
#> Estimating pnew with likelihood maximization
#> Estimating unlabeled mutation rate with -s4U data
#> Estimated pnews and polds for each sample are:
#> # A tibble: 4 × 4
#> # Groups:   mut [2]
#>     mut  reps   pnew    pold
#>   <int> <dbl>  <dbl>   <dbl>
#> 1     1     1 0.0500 0.00103
#> 2     1     2 0.0496 0.00103
#> 3     2     1 0.0499 0.00103
#> 4     2     2 0.0500 0.00103
#> Estimating fraction labeled
#> Estimating per replicate uncertainties
#> Estimating read count-variance relationship
#> Averaging replicate data and regularizing estimates
#> Assessing statistical significance
#> All done! Run QC_checks() on your bakRFit object to assess the 
#>             quality of your data and get recommendations for next steps.

# 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")
#> Combining bakR and DE analyses
#> Calculating mechanism p-value. This could take several minutes...
#> Assessing differential synthesis
#> Constructing Heatmap_df

# }