TSPC Manual

User Manual:

Open the PDF directly: View PDF .
Page Count: 9

Download
Open PDF In Browser	View PDF

Package ‘TSPC’
July 6, 2015
Type Package
Title Prediction using time-course gene expression
Version 2.0
Date 2014-01-17
Author Yuping Zhang
Maintainer Yuping Zhang 
Description Performs survival and quantitative outcome using
time-course gene expression, described in the following
papers:
Zhang Y, Tibshirani RJ, Davis RW. Predicting patient survival from longitudinal gene expression. Stat Appl Genet Mol Biol. 2010;9(1):Article41. Epub 2010 Nov 22.
Zhang Y, Ouyang Z. Predicting quantitative outcomes of patients using longitudinal gene expression. Sri Lankan Journal of Applied Statistics, 5(4), 117-126.
License GPL (>= 2)
LazyLoad TRUE
Depends superpc, survival
Repository CRAN
Date/Publication 2012-10-29 08:57:45

R topics documented:
TSPC-package
tspc.cv . . . . .
tspc.plotcv . . .
tspc.predict . .
tspc.project . .
tspc.train . . . .

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

Index

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

2
2
4
5
6
7
9

1

2

tspc.cv

TSPC-package

TSPC

Description
Performs survival and quantitative outcome using time-course gene expression, described in the
following papers: Zhang Y, Tibshirani RJ, Davis RW. Predicting patient survival from longitudinal
gene expression. Stat Appl Genet Mol Biol. 2010;9(1):Article41. Epub 2010 Nov 22.\ Zhang Y,
Ouyang Z. Predicting quantitative outcomes of patients using longitudinal gene expression. Sri
Lankan Journal of Applied Statistics, 5(4), 117-126.
Details
Package:
Type:
Version:
Date:
License:
LazyLoad:

TSPC
Package
2.0
2014-11-25
GPL
yes

Author(s)
Yuping Zhang 
References
Zhang Y, Tibshirani RJ, Davis RW. Predicting patient survival from longitudinal gene expression.
Stat Appl Genet Mol Biol. 2010;9(1):Article41. Epub 2010 Nov 22.

tspc.cv

Cross-validation

Description
This function uses a form of cross-validation to estimate the optimal feature threshold in supervised
principal components
Usage

tspc.cv(fit, data, seed = 123, topfea = TRUE, n.topfea = 1000, n.threshold = 20, n.fold = NULL, fold

tspc.cv

3

Arguments
fit

Object returned by tspc.train

data

Data object of form described in tspc.train documentation

seed

A Numeric number

topfea

If it is TRUE, then the tuning paparmeter is the number of features

n.topfea

Maximum number of features used as the tuning parameter

n.threshold

Number of thresholds, when using the number of thresholds as a tuning parameter

n.fold

Number of cross-validation folds

folds

Lists of indices of cross-validation folds (optional)

n.components

Number of cross-validation components to use: 1,2 or 3.

min.features

Minimum number of features to include, in determining range for threshold.
Default 5.

max.features

Maximum number of features to include, in determining range for threshold.
Default is total number of features in the dataset.

type

"survival" or "regression"

Details
This function uses a form of cross-validation to estimate the optimal feature threshold.
Value
list(thresholds = thresholds, n.threshold = n.threshold, nonzero = nonzero, scor = scor, scor.lower
= scor.lower, scor.upper = scor.upper, folds = folds, n.fold = n.fold, featurescores.folds = featurescores.folds, type = type)
thresholds

Vector of thresholds considered

n.threshold

Number of thresholds

nonzero

Number of features exceeding each value of the threshold

scor

Full CV scores

scor.lower

Full CV scores minus one standard error of scores

scor.upper

Full CV scores plus one standard error of scores

folds

Indices of CV folds used

n.fold
Number of folds used in the cross-validation
featurescores.folds
Feature scores for each fold
type
Author(s)
Yuping Zhang

problem type

4

tspc.plotcv

Examples
x = list()
for(i in 1:2){
set.seed(i+123)
x[[i]] = matrix(rnorm(500*100), ncol=100)
}
y = sample(c(5:100), size=100, replace=TRUE)
censoring = sample(c(0,1), size=100, replace=TRUE)

data = list(x = x, y=y, censoring.status=censoring, genenames = as.character(paste("gene", c(1:500), sep=""))
x = list()
for(i in 1:2){
set.seed(i+133)
x[[i]] = matrix(rnorm(500*100), ncol=100)
}
y = sample(c(5:100), size=100, replace=TRUE)
censoring = sample(c(0,1), size=100, replace=TRUE)

data.test = list(x = x, y=y, censoring.status=censoring, genenames = as.character(paste("gene", c(1:500), sep
fit = tspc.train(data, data.test, type="survival")
cv.obj = tspc.cv(fit$fit.obj, data, type="survival", n.fold=2)

Plot output from tspc.cv

tspc.plotcv

Description
Plots pre-validation results from plotcv, to aid in choosing best threshold
Usage
tspc.plotcv(object)
Arguments
object

Object returned by tspc.cv

Author(s)
Yuping Zhang
Examples
x = list()
for(i in 1:2){
set.seed(i+123)
x[[i]] = matrix(rnorm(500*100), ncol=100)
}
y = sample(c(5:100), size=100, replace=TRUE)
censoring = sample(c(0,1), size=100, replace=TRUE)

tspc.predict

5

data = list(x = x, y=y, censoring.status=censoring, genenames = as.character(paste("gene", c(1:500), sep=""))
x = list()
for(i in 1:2){
set.seed(i+133)
x[[i]] = matrix(rnorm(500*100), ncol=100)
}
y = sample(c(5:100), size=100, replace=TRUE)
censoring = sample(c(0,1), size=100, replace=TRUE)

data.test = list(x = x, y=y, censoring.status=censoring, genenames = as.character(paste("gene", c(1:500), sep
fit = tspc.train(data, data.test, type="survival")
cv.obj = tspc.cv(fit$fit.obj, data, type="survival", n.fold=2)
tspc.plotcv(cv.obj)

tspc.predict

Form principal components predictor from a trained tspc object

Description
Computes supervised principal components, using scores from "object"
Usage

tspc.predict(object, data, newdata, threshold, n.components = 3, prediction.type = c("continuous",
Arguments
object
Object fit.obj returned by tspc.train
data
List of projection of training data returned by tspc.train, object proj.obj$wdata.train
newdata
List of projection of test data returned by tspc.train, object proj.obj$wdata.test
threshold
Threshold for scores.
n.components
Number of principal components to compute. Should be 1,2 or 3.
prediction.type
"continuous" for raw principal component(s); "discrete" for principal component categorized in equal bins; "nonzero" for indices of features that pass the
threshold
n.class
Number of classes into which predictor is binned (for prediction.type="discrete"
Value
list(v.pred = out, u = x.sml.svd$u, d = x.sml.svd$d, which.features = which.features, v.pred.1df
= v.pred.1df, n.components = n.pc, coef = result$coef, call = this.call, prediction.type = prediction.type)
v.pred
u
d
which.features
n.components

Supervised principal componients predictor
U matrix from svd of weighted feature matrix
singual values from svd of weighted feature matrix
Indices of features exceeding threshold
Number of supervised principal components requested

6

tspc.project

Author(s)
Yuping Zhang
Examples
x = list()
for(i in 1:2){
set.seed(i+123)
x[[i]] = matrix(rnorm(500*100), ncol=100)
}
y = sample(c(5:100), size=100, replace=TRUE)
censoring = sample(c(0,1), size=100, replace=TRUE)

data = list(x = x, y=y, censoring.status=censoring, genenames = as.character(paste("gene", c(1:500), sep=""))
x = list()
for(i in 1:2){
set.seed(i+133)
x[[i]] = matrix(rnorm(500*100), ncol=100)
}
y = sample(c(5:100), size=100, replace=TRUE)
censoring = sample(c(0,1), size=100, replace=TRUE)

data.test = list(x = x, y=y, censoring.status=censoring, genenames = as.character(paste("gene", c(1:500), sep
fit = tspc.train(data, data.test, type="survival")

predict.obj<- tspc.predict(fit$fit.obj, fit$proj.obj$data.train, fit$proj.obj$data.test, threshold=1.0, n.co

tspc.project

Project time-course gene expression to weighted gene expression

Description
Project time-course gene expression to weighted gene expression
Usage
tspc.project(data, data.test, type = c("survival", "regression"))
Arguments
data

List of training data, of form described in tspc.train documentation

data.test

List of test data, of form described in tspc.test documentation

type

Problem type: "survival" for censored survival outcome, or "regression" for simple quantitative outcome.

tspc.train

7

Value
list(data.train = wdata.train, data.test = wdata.test)
data.train

Projection of training data

data.test

Projection of test data

Author(s)
Yuping Zhang

tspc.train

Prediction using time-course gene expression

Description
Does prediction of a quantitative regression or survival outcome, using the time-course gene expression.
Usage
tspc.train(data, data.test, type = c("survival", "regression"), s0.perc = 0.5)
Arguments
data

Data object with components x- a list of p by n matrix of features, one observation per column, one matrix per time point; y- n-vector of outcome measurements; censoring.status- n-vector of censoring censoring.status (1= died or event
occurred, 0=survived, or event was censored), needed for a censored survival
outcome.

data.test

Data object with components x- a list of p by n matrix of features, one observation per column, one matrix per time point; y- n-vector of outcome measurements; censoring.status- n-vector of censoring censoring.status (1= died or event
occurred, 0=survived, or event was censored), needed for a censored survival
outcome.

type

Problem type: "survival" for censored survival outcome, or "regression" for simple quantitative outcome.

s0.perc

Factor for denominator of score statistic, between 0 and 1: the percentile of
standard deviation values added to the denominator. Default is 0.5 (the median)

Value
proj.obj

projection of training data and test data

fit.obj

fitted object using training data

Author(s)
Yuping Zhang

8

tspc.train

References
Zhang Y, Tibshirani RJ, Davis RW. Predicting patient survival from longitudinal gene expression.
Stat Appl Genet Mol Biol. 2010;9(1):Article41. Epub 2010 Nov 22.
Examples
x = list()
for(i in 1:2){
set.seed(i+123)
x[[i]] = matrix(rnorm(500*100), ncol=100)
}
y = sample(c(5:100), size=100, replace=TRUE)
censoring = sample(c(0,1), size=100, replace=TRUE)

data = list(x = x, y=y, censoring.status=censoring, genenames = as.character(paste("gene", c(1:500), sep=""))
x = list()
for(i in 1:2){
set.seed(i+133)
x[[i]] = matrix(rnorm(500*100), ncol=100)
}
y = sample(c(5:100), size=100, replace=TRUE)
censoring = sample(c(0,1), size=100, replace=TRUE)

data.test = list(x = x, y=y, censoring.status=censoring, genenames = as.character(paste("gene", c(1:500), sep
obj = tspc.train(data, data.test, type="survival")

Index
∗Topic regression
TSPC-package, 2
tspc.cv, 2
tspc.plotcv, 4
tspc.predict, 5
tspc.project, 6
tspc.train, 7
∗Topic survival
TSPC-package, 2
tspc.cv, 2
tspc.plotcv, 4
tspc.predict, 5
tspc.project, 6
tspc.train, 7
TSPC (TSPC-package), 2
TSPC-package, 2
tspc.cv, 2
tspc.plotcv, 4
tspc.predict, 5
tspc.project, 6
tspc.train, 7

9



---

Source Exif Data:

File Type                       : PDF
File Type Extension             : pdf
MIME Type                       : application/pdf
PDF Version                     : 1.5
Linearized                      : No
Page Count                      : 9
Page Mode                       : UseOutlines
Author                          : 
Title                           : 
Subject                         : 
Creator                         : LaTeX with hyperref package
Producer                        : pdfTeX-1.40.15
Create Date                     : 2015:07:06 16:18:52-04:00
Modify Date                     : 2015:07:06 16:18:52-04:00
Trapped                         : False
PTEX Fullbanner                 : This is pdfTeX, Version 3.14159265-2.6-1.40.15 (TeX Live 2014) kpathsea version 6.2.0

EXIF Metadata provided by EXIF.tools