TI_fit: =========================================================================...

In CyanolabFreiburg/rifi: 'rifi' analyses data from rifampicin time series created by microarray or RNAseq

========================================================================= TI_fit

TI_fit estimates transcription interference and termination factor using nls function for probe or bin flagged as "TI".

Description

TI_fit uses nls2 function to fit the flagged probes or bins with "TI" found using finding_TI.r. It estimates the transcription interference level (referred later to TI) as well as the transcription factor fitting the probes/bins with nls function looping into several starting values.

Usage

TI_fit(
  inp,
  cores = 1,
  restr = 0.2,
  k = seq(0, 1, by = 0.5),
  decay = c(0.05, 0.1, 0.2, 0.5, 0.6),
  ti = seq(0, 1, by = 0.5),
  ti_delay = seq(0, 2, by = 0.5),
  rest_delay = seq(0, 2, by = 0.5),
  bg = 0
)

Arguments

inp	SummarizedExperiment: the input with correct format.
cores	integer: the number of assigned cores for the task.
restr	numeric: a parameter that restricts the freedom of the fit to avoid wrong TI-term_factors, ranges from 0 to 0.2.
k	numeric vector: A sequence of starting values for the synthesis rate. Default is seq(0, 1, by = 0.5).
decay	numeric vector: A sequence of starting values for the decay Default is c(0.05, 0.1, 0.2, 0.5, 0.6).
ti	numeric vector: A sequence of starting values for the delay. Default is seq(0, 1, by = 0.5).
ti_delay	numeric vector: A sequence of starting values for the delay. Default is seq(0, 2, by = 0.5).
rest_delay	numeric vector: A sequence of starting values. Default is seq(0, 2, by = 0.5).
bg	numeric vector: A sequence of starting values. Default is 0.

Details

To determine TI and termination factor, TI_fit function is applied to the flagged probes and to the probes localized 1000 nucleotides upstream. Before applying TI_fit function, some probes/bins are filtered out if they are below the background using generic_filter_BG. The model loops into a dataframe containing sequences of starting values and the coefficients are extracted from the fit with the lowest residuals. When many residuals are equal to 0, the lowest residual can not be determined and the coefficients extracted could be wrong. Therefore, a second filter was developed. First we loop into all starting values, we collect nls objects and the corresponding residuals. They are sorted and residuals non equal to 0 are collected in a vector. If the first residuals are not equal to 0, 20 % of the best residuals are collected in tmp_r_min vector and the minimum termination factor is selected. In case the first residuals are equal to 0 then values between 0 to 20% of the values collected in tmp_r_min vector are gathered. The minimum termination factor coefficient is determined and saved. The coefficients are gathered in res vector and saved as an object.

Value

the SummarizedExperiment object: with delay, decay and TI_termination_factor added to the rowRanges. The full fit data is saved in the metadata as "fit_TI".

Examples

data(preprocess_minimal)
TI_fit(inp = preprocess_minimal, cores=2, restr=0.01)

CyanolabFreiburg/rifi documentation built on May 7, 2023, 7:53 p.m.