VisualizeDropout fits dropout model with QuantifyDropout,
reports the fit results, and then generates a ggplot object showing the
data used to infer the fit as well as the fitted nonlinear trend.
Arguments
- obj
bakRFit or bakRFnFit object
- keep_data
Logical; if TRUE, will return data used to make plots along with the plots themselves
- no_message
Logical; if TRUE, will not output message regarding estimated rates of dropout in each sample
Value
If keep_data is FALSE, then a list of ggplot objects are returned, one
for each +s4U sample. The plots show the relationship between a feature's fraction new
and the difference between its +s4U and -s4U read coverage. Nonlinear-least squares fit
is plotted on top of points as a blue line. If keep_data is TRUE, then the data used
to make the plots is returned in addition to the list of plots.
Examples
# \donttest{
# Simulate data for 500 genes and 2 replicates with 40% dropout
sim <- Simulate_relative_bakRData(500, 100000, nreps = 2, p_do = 0.4)
# 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 0.0506 0.00100
#> 2 1 2 0.0500 0.00100
#> 3 2 1 0.0501 0.00100
#> 4 2 2 0.0495 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.
# Quantify dropout
DO_plots <- VisualizeDropout(Fit)
#> Estimated rates of dropout are:
#> Exp_ID Replicate pdo
#> 1 1 1 0.3540755
#> 2 1 2 0.4362784
#> 3 2 1 0.4371812
#> 4 2 2 0.3918201
# }