trendy: Trendy
In Trendy: Breakpoint analysis of time-course expression data

Description Usage Arguments Value Author(s) Examples

Segmented regression models are fit for each gene. The number of model fits is 1 -> maxK.

trendy(Data = NULL, tVectIn = NULL, saveObject = FALSE,
  fileName = NULL, meanCut = 10, maxK = 3, minNumInSeg = 5,
  pvalCut = 0.1, numTry = 5, keepFit = FALSE, NCores = NULL,
  featureNames = NULL)

`Data`	a matrix of normalized expression measurements. Rows are genes/features and columns are samples.
`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).
`saveObject`	if TRUE then the trendy object produced will be saved to use in the Shiny app (default is FALSE).
`fileName`	the file name (and file path) to save the Trendy object, only used if saveObject=TRUE (default name is trendyOutputForShiny.RData).
`meanCut`	genes whose mean is less than MeanCut will not be considered, default is 10.
`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).
`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).
`NCores`	number of cores to use, default is detectCores() - 1.
`featureNames`	optional parameter to specify an explicit subset of features/genes to fit the segmented regression model to.

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 squared value of the model Fit: fit object

Ning Leng and Rhonda Bacher

 m1 <- matrix(c(c(rnorm(50,5,1),sort(rnorm(50, 15, 5))), rnorm(100, 50,10)), 2, 100, TRUE)
 rownames(m1) <- c("g1","g2")
 colnames(m1) <- paste0("time", seq_len(100))
 myTrends <- trendy(m1)