The ppc Package - University of Aucklandftp.auckland.ac.nz/software/CRAN/doc/packages/ppc.pdf · The ppc Package October 11, 2004 Title Peak Probability Contrasts ... Function to

The ppc PackageOctober 11, 2004

Title Peak Probability Contrasts

Version 1.01

Author Balasubramanian Narasimhan, R. Tibshirani, T. Hastie

Description Sample classification of protein mass spectra by peak probabilty contrasts

Maintainer Rob Tibshirani <[email protected]>

License GPL2.0

URL http://www-stat.stanford.edu/˜tibs/PPC

R topics documented:

ppc.cv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2ppc.fdr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3ppc.find.splits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4ppc-internal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5ppc.make.centroid.list . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5ppc.make.peaklist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6ppc.peak.summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7ppc.peaks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8ppc.plot.hist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8ppc.plotcv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9ppc.plotcvprob . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10ppc.plotfdr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11ppc.predict . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11ppc.predict.peaks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12ppc.predict.peaks1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13ppc.predict1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14ppc.read.peaks.batch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15ppc.read.peaks.nobatch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15ppc.read.raw.batch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16ppc.read.raw.nobatch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17ppc.remove.beforeslash.and.suffix . . . . . . . . . . . . . . . . . . . . . . . . . . . 18ppc.remove.suffix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18ppc.subset.and.reshape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19ppc.subset.and.reshape.peakdata . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Index 21

1

2 ppc.cv

ppc.cv Cross-validation for PPC analysis

Description

This function does K-fold cross-validation for PPC analysis

Usage

ppc.cv(ppc.fit, data, user.parms)

Arguments

ppc.fit Result of call to ppc.predict

data List containing mass spec data

user.parms List of user defiend parameters

Details

Value

err CV error rate for each threshold value

se se of CV error rate

confusion Cnfusion matrix for each threshold value

threshold Threshold vlaues used

yhat Predicted values from CV

prob Cv probabilities

y Training set outcome values

folds Indices defining CV folds

numsites Number of m/z sites surviving shrinkage at each threshold value

Author(s)

Balasubramanian Narasimhan and Rob Tibshirani

Examples

## for a complete worked example of this function in a PPC analysis see

## http://www-stat.stanford.edu/~tibs/PPC/Rdist/Rscript.rawdata

ppc.fdr 3

ppc.fdr Function to estimate False Discovery rates for peaks in PPCanalysis

Description

Estimate False Discovery rates for peaks in FDR analysis, using permutations of the samplelabels

Usage

ppc.fdr(data, centroid.fit, peak.fit, split.fit, ppc.fit, user.parms)

Arguments


centroid.fit Result of call to ppc.make.centroid.list

peak.fit Result of call to ppc.predict.peaks

split.fit Result of call to ppc.find.splits


user.parms List of user defined parameters

Details

Value

results Matrix with columns- threshold used, number of peaks found, FDR

pi0 Esimate of proportion of truly null peaks

threshold Vector of thresholds used

Author(s)


Examples



4 ppc.find.splits

ppc.find.splits Function to find best discriminating split points for training datain mass spec

Description

Find best discriminating split points for training data (to separate the classes). Takescentroids.fit- result of call to make.centroids.list and peaks.fit- result of call to predict.peaks

Usage

ppc.find.splits(centroid.fit, peak.fit, data, user.parms)

Arguments





Value

prhat Proportion of samples beyond optimal cutpoint in each outcome class

pr Proportion of samples beyond cutpoints in each outcome class

n.class number of samples in each outcome class

cutpoints Cutpoints (split points) tried

cuthat Optimal cut points

prclose Indicators for split points with prob difference within 10 percent of thatof the optimal split point

nsplits Number of cutpoints tried

fix.at.one Was the optimal cutpoint fixed at one? (i.e no peak vs peak)

Author(s)


Examples



ppc-internal 5

ppc-internal Internal ppc functions

Description

Internal ppc functions

Usage

ppc.make.centroids(clust.tree, x, user.parms)which.is.min(x)medoid(x)permute.rows(x)balanced.folds(y, nfolds)hclust.1d(x, debug=FALSE)ppc.read.peaks.file(filename)

Details

These are not to be called by the user.

Author(s)


ppc.make.centroid.list

Function to make a list of peak centroids, from a set of peaksfrom different spectra

Description

This function starts with a list of peaks from a collection of spectra, and does a one dimen-sional hierarchical clustering. It then cuts off the dendogram at height user.parms$peak.gap,forming clusters of peaks. The medoids of each cluster form hte final list of peak centroids.

Usage

ppc.make.centroid.list(data, user.parms)

Arguments

data List containing mass spc data

user.parms List of user-defined parameters

6 ppc.make.peaklist

Value

cent Matrix of centroids, one per row

peaklist Peaklist used

clust.tree Dendrogram from hclust.1d

all.peaks Vector of m/z values of all peaks used

peak.gap Peak width used

recluster Not currently used

Author(s)


Examples



ppc.make.peaklist Function to extract peaks from raw mass spec data

Description

This function to extracts peaks from raw mass spec data. It uses a very simple peak finder”peaks”- looking for sites where the intensity is higher than it is elsewhere in a user-definedwindow surrounding that site.

Usage

ppc.make.peaklist(data, user.parms)

Arguments

data List containing raw mass spec data

user.parms List of user parameters

Value

List of peaks- one component per spectrum. Each component is a matrix of log m/z valuesand peak intensity.

Author(s)


Examples



ppc.peak.summary 7

ppc.peak.summary Produce summary of peaks from PPC analysis

Description

This function produces a summary of peaks from a PPC analysis

Usage

ppc.peak.summary(centroid.fit, peak.fit, data, user.parms, split.fit)

Arguments






Details

Value

Matrix containing on row per peak. Columns are peak.position, min, max of peak.position,number.of.spectra in which the peak occurs, rank.of.peak, in discriminatory power, splitpoint for peak height, proportion of samples in class 1 exceeding split point, proportion ofsamples in class 2 exceeding split point etc, pairwise differences in these probabilities, peakinfo for each sample

Author(s)


Examples



8 ppc.plot.hist

ppc.peaks Find local maxima

Description

Finds the local maxima in a vector. This is an R implementation of the Splus functionpeaks. . Note that the span parameter is a proportion between 0 and 1, rather than thenumber of x values (as in the Splus function). Note also that it only handles a vector asinput.

Usage

ppc.peaks(x, span)

Arguments

x A vector. Peaks will find the local maxima in x.

span A peak is defined as an element in a sequence which is greater than allother elements within a window of width length(x)*span centered at thatelement.

Details

All elements within a halfspan of the end of a sequence or within a halfspan of a missingvalue are FALSE.

Value

vector of logical values, indicating whether there is a peak at each location

Author(s)


Examples

x<-rnorm(1000)

a<-ppc.peaks(x, .02)

ppc.plot.hist Plot peak histograms from PPC analysis

Description

This function plots the histograms of peaks from a PPC analysis. They are laid out in orderof discriminatory power, starting at the top left and moving down the leftmost column. Atmost 25 sites are plotted.

Usage

ppc.plot.hist(peak.fit, ppc.fit, centroid.fit, split.fit, data, first.site = 1, last.site = 25, title.plot = NULL)

ppc.plotcv 9

Arguments






first.site Integer- first site to plot. Default 1

last.site Integer- last site to plot. Default 1.

title.plot Character title for plot. Default NULL

Details

Value

Author(s)


Examples


## http://www-stat.stanford.edu/~tibs/PPC/Rscript.rawdata

ppc.plotcv Function to plot CV curves from PPC analysis

Description

Function to plot CV curves from PPC analysis

Usage

ppc.plotcv(fit)

Arguments

fit Result of call to ppc.cv

Details

Value

10 ppc.plotcvprob

Author(s)


Examples



ppc.plotcvprob Function to plot CV probabilities from PPC analysis

Description

Function to plot CV probabilities from PPC analysis

Usage

ppc.plotcvprob(fit, data, threshold)

Arguments

fit Result of call to ppc.cv


threshold Value of threshold to use for predictions

Details

Value

Author(s)


Examples



ppc.plotfdr 11

ppc.plotfdr Function to plot FDR results from PPC analysis

Description

Function to plot FDR results from PPC analysis

Usage

ppc.plotfdr(fdrfit)

Arguments

fdrfit Result of call to ppc.fdr

Details

Value

Author(s)


Examples



ppc.predict Function to do test set prediction for the PPC method

Description

This function does test set prediction for PPC method. It predicts outcome classes for alist of peaks from test set spectra.

Usage

ppc.predict(centroid.fit, split.fit, logmz, peaklist.te, n.threshold = 30, threshold = NULL, metric = c("binomial", "euclidean", "absolute"), summ = c("mean", "median"))

12 ppc.predict.peaks

Arguments

centroid.fit Result of a call to ppc.make.centroid.list

split.fit Result of a call to ppc.find.splits

logmz log of m/z values from training data

peaklist.te List of peaks from test set- each component is a matrix of log m/z valuesand peak intensities

n.threshold Number of shrinkage thresholds to use

threshold Threshold values to use

metric ”binomial”,”euclidean”, or ”absolute”

summ ”mean” or median”

Value

yhat Matrix of predicted classes

threshold Threshold values used.

numsites Number of sites surviving the threshold for each shrinkage value

sites List of sites surviving the threshold for each shrinkage value

ind Indicator matrix of event ( peak intensity at site > cutpoint

ind0 Indicator matrix of event ( peak intensity at site > cutpoint

prob Matrix of estimated class probabilities

ht Matrix of peak intensities

Author(s)


Examples



ppc.predict.peaks A function to find centroid peaks, in a list of individual peaks

Description

Takes centroid.fit (result of a call to make.centroids.list) and looks for these m peaks inpeaklist, a list of length n returns ind - an m by n matrix of TRUE/FALSE values andht, the matrix of corresponding peak heights. A peak is considered present if it is withinuser.parms$peak.gap units from the centroid.

Usage

ppc.predict.peaks(centroid.fit, data)

ppc.predict.peaks1 13

Arguments

centroid.fit Result of call to make.centroid.listdata List containing the mass spec peaks data

Value

ind indicator matrix of presence/absence of peakht matrix of heights. Note: peak is not present if ind=0, even if ht is >0

Author(s)


Examples



ppc.predict.peaks1 A function to find centroid peaks, in a list of individual peaksfrom one spectrum

Description

Takes centroid.fit (result of a call to make.centroids.list) and looks for these m peaks ina peaklist from one spectrum. returns ind - an m-vector of TRUE/FALSE values andht, the vector of corresponding peak heights. A peak is considered present if it is withinuser.parms$peak.gap units from the centroid.

Usage

ppc.predict.peaks1(centroid.fit, logmz, peaklist.new)

Arguments

centroid.fit Result of call to make.centroid.listlogmz Log of m/z valuespeaklist.new Matrix of peaks from a single spectrum. Rows are (log m/z value, peak

intensity)

Value

ind indicator vector of presence/absence of peakht Vector of heights. Note: peak is not present if ind=0, even if ht is >0

Author(s)


Examples



14 ppc.predict1

ppc.predict1 Function to do a single test set prediction for the PPC method

Description

This function does test set prediction for PPC method. It predicts outcome classes for alist of peaks from a single test set spectrum.

Usage

ppc.predict1(centroid.fit, split.fit, logmz, peaklist.te, threshold, metric = c("binomial", "euclidean", "absolute"), summ = c("mean", "median"))

Arguments

centroid.fit Result of a call to ppc.make.centroid.list

split.fit Result of a call to ppc.find.splits

logmz log of m/z values from training data

peaklist.te List of peaks from test set- each component is a matrix of log m/z valuesand peak intensities

threshold Threshold values to use

metric ”binomial”,”euclidean”, or ”absolute”

summ ”mean” or median”

Value

yhat Matrix of predicted classes

threshold Threshold values used.

numsites Number of sites surviving the threshold for each shrinkage value

sites List of sites surviving the threshold for each shrinkage value

ind Indicator matrix of event ( peak intensity at site > cutpoint

ind0 Indicator matrix of event ( peak intensity at site > cutpoint

prob Matrix of estimated class probabilities

ht Matrix of peak intensities

Author(s)


Examples



ppc.read.peaks.batch 15

ppc.read.peaks.batch Read in mass spec peak data, with batches

Description

A function to read in protein mass spec peaks data, with batches

Usage

ppc.read.peaks.batch(dir, batches)

Arguments

dir Name of directory containing the data

batches Vector of batch names

Details

Value

peaklist List of peaks for each sample. Each component is a matrix- one row perm/z site, consiting of log m/z and peak intensity

filenames List of filenames read in

logmz log m/z values

Author(s)


Examples


## http://www-stat.stanford.edu/~tibs/PPC/Rdist/Rscript.peakdata

ppc.read.peaks.nobatch

Read in mass spec peak data, without batches

Description

A function to read in protein mass spec peaks data

Usage

ppc.read.peaks.nobatch(dir)

Arguments


16 ppc.read.raw.batch

Details

Value

peaklist List of peaks for each sample. Each component is a matrix- one row perm/z site, consiting of log m/z and peak intensity


logmz log m/z values

Author(s)


Examples



ppc.read.raw.batch A function to read in raw protein mass spec data, with batches

Description

This function reads in raw protein mass spec data from a directory. The directory is assumedto have on subdirectory per class (eg control or disease). The subdirectories like ”control”have further subdirectories, one per batch. So the structure looks lik control/batch1/file.csv,control/batch2/file.csv, disease/batch1/file.csv, etc. There is one comma-separated (csv)file.csv in the subdirectory per spectrum, having lines of the form m/ value, intensity (oneline per m/z site)

Usage

ppc.read.raw.batch(dir, batches, mz = NULL)

Arguments


batches Vector of character names of batches

mz Optional vector of m/z values. Default NULL. If NULL, m/z values areread in from files. Otherwise the values in mz are used.

Value

xtr Matrix of intensities- one row per m/z site, one col per spectrum

mz m/z values


Author(s)


ppc.read.raw.nobatch 17

Examples



ppc.read.raw.nobatch A function to read in raw protein mass spec data, with no batches

Description

This function reads in raw protein mass spec data from a directory. The directory is assumedto have on subdirectory per class (eg control or disease). There is one comma-separated(csv) file in the subdirectory per spectrum, having lines of the form m/ value, intensity (oneline per m/z site)

Usage

ppc.read.raw.nobatch(directory, mz = NULL)

Arguments

directory Name of directory containing the data

mz Optional vector of m/z values. Default NULL. If NULL, m/z values areread in from files. Otherwise the values in mz are used.

Value

xtr Matrix of intensities- one row per m/z site, one col per spectrum

mz m/z values

filenames List of names of files that were read in

Author(s)


Examples



18 ppc.remove.suffix

ppc.remove.beforeslash.and.suffix

Remove characters efore a slash and at end

Description

This function takes a character file name or vector of character file names, and removes thecharacter before the rightmost slash, and the suffix after the last dot. Eg ”file/foo/junk.csv”becomes ”junk”

Usage

ppc.remove.beforeslash.and.suffix(x)

Arguments

x Character string or vector of character strings.

Details

Value

Character string or vector of character strings, modified in the manner descibed above.

Author(s)


Examples



ppc.remove.suffix Function to remove the suffix from a file name

Description

This function just removes a .xxx suffix from the end of a filename or a vector of filenames

Usage

ppc.remove.suffix(x)

Arguments

x Filename or vector of filenames

ppc.subset.and.reshape 19

Value

Filename or list of filenames with suffix removed

Author(s)


Examples



ppc.subset.and.reshape

Function to subset and reshape raw mass spec data

Description

This function subsets raw mass spec data, using the m/z limits mz.min and mz.max inuser.parms. If there are batches, it also reshapes the data, concatenating the spectra indifferent batches for a given patient. Thus for each patient it produces one long column ofvalues. The m/z values are strung out as well. Using this trick we can essentially ignorethe presence of batches for the rest of the analysis,

Usage

ppc.subset.and.reshape(data, user.parms)

Arguments

data List containing the mass spec data


Value

List containing the subset and reshaped mass spec data

Author(s)


Examples



20 ppc.subset.and.reshape.peakdata

ppc.subset.and.reshape.peakdata

Function to subset and reshape mass spec peak data

Description

This function subsets mass spec peak data, using the m/z limits mz.min and mz.max inuser.parms. If there are batches, it also reshapes the data, concatenating the spectra indifferent batches for a given patient. Thus for each patient it produces one long vector ofvalues. The m/z values are strung out as well. Using this trick we can essentially ignorethe presence of batches for the rest of the analysis,

Usage

ppc.subset.and.reshape.peakdata(data, user.parms)

Arguments

data List containing the mass spec data


Value

List containing the subset and reshaped mass spec data

Author(s)


Examples



Index

∗Topic internalppc-internal, 4

balanced.folds (ppc-internal), 4

hclust.1d (ppc-internal), 4

medoid (ppc-internal), 4

permute.rows (ppc-internal), 4ppc-internal, 4ppc.cv, 1ppc.fdr, 2ppc.find.splits, 3ppc.make.centroid.list, 4ppc.make.centroids (ppc-internal), 4ppc.make.peaklist, 5ppc.peak.summary, 6ppc.peaks, 7ppc.plot.hist, 7ppc.plotcv, 8ppc.plotcvprob, 9ppc.plotfdr, 10ppc.predict, 10ppc.predict.peaks, 11ppc.predict.peaks1, 12ppc.predict1, 13ppc.read.peaks.batch, 14ppc.read.peaks.file (ppc-internal), 4ppc.read.peaks.nobatch, 14ppc.read.raw.batch, 15ppc.read.raw.nobatch, 16ppc.remove.beforeslash.and.suffix, 17ppc.remove.suffix, 17ppc.subset.and.reshape, 18ppc.subset.and.reshape.peakdata, 19

which.is.min (ppc-internal), 4

21

Documents

The ppc Package - University of Aucklandftp.auckland.ac.nz/software/CRAN/doc/packages/ppc.pdf · The ppc Package October 11, 2004 Title Peak Probability Contrasts ... Function to