This function creates a 2-component PCA plot using logit(fn) estimates.
FnPCA has been deprecated in favor of FnPCA2. The latter accepts a full
bakRFit as input and handles imbalanced replicates.
Arguments
- obj
Object contained within output of
bakRFit. So, either Fast_Fit (MLE implementation fit), Stan_Fit (MCMC implementation fit), or Hybrid_Fit (Hybrid implementation fit)- log_kdeg
Boolean; if TRUE, then log(kdeg) estimates used for PCA rather than logit(fn). Currently only compatible with Fast_Fit
Examples
# \donttest{
# Simulate data for 500 genes and 2 replicates
sim <- Simulate_bakRData(500, nreps = 2)
# Fit data with fast implementation
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 0.0502 0.00100
#> 2 1 2 0.0499 0.00100
#> 3 2 1 0.0500 0.00100
#> 4 2 2 0.0501 0.00100
#> 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.
# Fn PCA
FnPCA2(Fit, Model = "MLE")
# log(kdeg) PCA
FnPCA2(Fit, Model = "MLE", log_kdeg = TRUE)
# }