Trendy: Breakpoint analysis of time-course expression data

fitSegBIC

R Documentation

Fit segmented regression models on a feature/gene

Description

fits segmented regression models

Usage

fitSegBIC(Data, maxK = 2, tVectIn = NULL, minNumInSeg = 5,
  pvalCut = 0.1, numTry = 5, keepFit = FALSE)

Arguments

`Data`	a matrix of normalized expression measurements. Rows are genes/features and columns are samples.
`maxK`	maximum number of breakpoints to consider. For each gene, trendy will fit maxK + 1 models containing 0 -> maxK breakpoints (1 -> (maxK + 1) segments). The model with the lowest BIC value will be selected (unless forceRsq = TRUE, see below).
`tVectIn`	a numerical vector indicating the time-points or the order of samples. If it is NULL (default), then the time/order will be assumed to be equaly spaced from 1:N (N is number of samples).
`minNumInSeg`	minimum number of samples required to be within a segment. If a breakpoint model has a segment with fewer than minNumInSeg point in any segment, then the model is not considered valid.
`pvalCut`	p-value cutoff. If the p-value of a segment is greater than PvalCut, then the segment will be called as 'no change'.
`numTry`	the number of different seeds to try. If all numTry runs fail, then the linear regression (no breakpoints, one segment) model will be returned.
`keepFit`	whether to report the fitted object (default is FALSE).

Value

Trend: direction of each sample; -1: down, 0: no change, 1: up Slope: fitted slopes, Slope.Trend: sign of fitted slopes, Slope.Pvalue: p value of each segment, Breakpoint: estimated breakpoints, Fitted.Values: fitted values AdjustedR2: adjusted r value of the model Fit: fit object