finding_PDD: =========================================================================...

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

========================================================================= finding_PDD

finding_PDD Flags potential candidates for post transcription decay

Description

'finding_PDD' uses 'score_fun_linear_PDD' to make groups by the difference to the slope. The slope is further checked for steepness to decide for PDD. 'PDD' is added to the 'flag' column. Post transcription decay is characterized by a strong decrease of intensity by position. The rowRanges need to contain at least 'ID', 'intensity', 'position' and 'position_segment'!

Usage

finding_PDD(inp, cores = 1, pen = 2, pen_out = 1, thrsh = 0.001)

Arguments

inp	SummarizedExperiment: the input.
cores	integer: the number of assigned cores for the task
pen	numeric: an internal parameter for the dynamic programming. Higher values result in fewer fragments. Advised to be kept at 2. Default is 2.
pen_out	numeric: an internal parameter for the dynamic programming. Higher values result in fewer possible outliers. Advised to be kept at 1. Default is 1.
thrsh	numeric: an internal parameter that allows fragments with slopes steeper than the thrsh to be flagged with 'PDD'. Higher values result in fewer candidates. Advised to be kept at 0.001. Default is 0.001.

Value

The SummarizedExperiment object: with "PDD" added to the flag column.

Examples

data(preprocess_minimal)
finding_PDD(inp = preprocess_minimal, cores = 2, pen = 2,
pen_out = 1, thrsh = 0.001)

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