plrs: Fit a (constrained) piecewise linear regression spline

In plrs: Piecewise Linear Regression Splines (PLRS) for the association between DNA copy number and gene expression

Description

The function fits a piecewise linear regression spline to explain gene expression by the segmented DNA copy number. The called copy number values are used as a template for model building.

Usage

plrs(expr, cghseg, cghcall=NULL, probloss = NULL, probnorm = NULL,
probgain = NULL, probamp = NULL, knots = NULL, continuous = FALSE,
constr = TRUE, constr.slopes = 2, constr.intercepts = TRUE,
min.obs = 3, discard.obs = TRUE)

Arguments

expr	Vector of gene expression values
cghseg	Vector of segmented copy number values
cghcall	Vector of called copy number values. If not provided, we are reduced to a simple linear model.
probloss	Vector of call probabilities associated with state "loss". Default is NULL.
probnorm	Vector of call probabilities associated with state "normal". Default is NULL.
probgain	Vector of call probabilities associated with state "gain". Default is NULL.
probamp	Vector of call probabilities associated with state "amplification". Default is NULL.
knots	knots or change points. If NULL (default), there are estimated. See details.
continuous	Logical, whether the model is continuous (no jump) or not.
constr	Logical, whether the model is constrained or not. (this has been implemented to turn on and off easily the constraints)
constr.slopes	Type of non-negativity constraints applied on slopes. Either 1 or 2 (default). See details.
constr.intercepts	If TRUE (default) jumps from state to state are also constrained to be non-negative
min.obs	See modify.conf
discard.obs	See modify.conf

Details

If cghcall=NULL, discrete copy number values are omitted, which results in fitting a simple linear model.

If constr.slopes=1, all slopes are constrained to be non-negative. If constr.slopes=2, the slope associated with state "normal" is constrained to be non-negative and all others are forced to be at least equal to the latter.

Two methods are implemented for the estimation of knots. If call probabilities are provided, a knot is determined so that the sum of (the two adjacent) states membership probabilities is maximized. Otherwise, this is defined as the midpoint of the interval between the two consecutive states.

The constrained least squares problem is solved using function solve.QP of package quadprog.

Value

An object of class plrs-class

Author(s)

Gwenael G.R. Leday [e-mail]

Examples

# Simulate data
sim <- plrs.sim(n=80, states=4, sigma=0.5)

# Fit a model 
model <- plrs(expr=sim$expr, cghseg=sim$seg, cghcall=sim$cal)
model

# Methods
coef(model)
effects(model)
fitted(model)
knots(model)
model.matrix(model)
plot(model)
predict(model, newcghseg=seq(0,5, length.out=100))   
residuals(model)
summary(model)

plrs documentation built on April 28, 2020, 6:09 p.m.