QuantifyDropout: Fit dropout model to quantify dropout frequency

In bakR: Analyze and Compare Nucleotide Recoding RNA Sequencing Datasets

Fit dropout model to quantify dropout frequency

Description

QuantifyDropout estimates the percentage of 4-thiouridine containing RNA that was lost during library preparation (pdo).

Usage

QuantifyDropout(
  obj,
  scale_init = 1.05,
  pdo_init = 0.3,
  keep_data = FALSE,
  no_message = FALSE,
  ...
)

Arguments

obj	bakRFit object
scale_init	Numeric; initial estimate for -s4U/+s4U scale factor. This is the factor difference in RPM normalized read counts for completely unlabeled transcripts (i.e., highly stable transcript) between the +s4U and -s4U samples.
pdo_init	Numeric; initial estimtae for the dropout rate. This is the probability that an s4U labeled RNA molecule is lost during library prepartion.
keep_data	Logical; if TRUE, will return list with two elements. First element is the regular return (data frame with dropout quantified), and the second element will be the data frame that was used for fitting the dropout model. This is useful if wanting to visualize the fit. See Return documetation for more details
no_message	Logical; if TRUE, will not output message regarding estimated rates of dropout in each sample
...	Additional (optional) parameters to be passed to stats::nls()

Value

If keep_data is FALSE, then only a data frame with the dropout rate estimates (pdo) in each sample is returned. If keep_data is TRUE, then a list with two elements is returned. One element is the pdo data frame always returned, and the second is the data frame containing information passed to stats::nls for pdo estimation.

Examples

# Simulate data for 500 genes and 2 replicates with 40% dropout
sim <- Simulate_relative_bakRData(500, depth = 100000,
                                  nreps = 2, p_do = 0.4)

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

# Quantify dropout
Fit <- QuantifyDropout(Fit)

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