Calculate bootstrapped confidence intervals of SPLS coefficients

Description

Calculate bootstrapped confidence intervals of coefficients of the selected predictors and generate confidence interval plots.

Usage

ci.spls( object, coverage=0.95, B=1000,
        plot.it=FALSE, plot.fix="y",
        plot.var=NA, K=object$K, fit=object$fit )

Arguments

Value

Invisibly returns a list with components:

Author(s)

Dongjun Chung, Hyonho Chun, and Sunduz Keles.

References

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3–25.

See Also

correct.spls and spls.

Examples

data(mice)
# SPLS with eta=0.6 & 1 hidden components
f <- spls( mice$x, mice$y, K=1, eta=0.6 )
# Calculate confidence intervals of coefficients
ci.f <- ci.spls( f, plot.it=TRUE, plot.fix="x", plot.var=20 )
# Bootstrapped confidence intervals
cis <- ci.f$cibeta
cis[[20]]   # equivalent, 'cis$1422478_a_at'

Plot estimated coefficients of the SPLS object

Description

Plot estimated coefficients of the selected predictors in the SPLS object.

Usage

coefplot.spls( object, nwin=c(2,2),
            xvar=c(1:length(object$A)), ylimit=NA )

Arguments

Details

This plot is useful for visualizing coefficient estimates of a variable for different responses. Hence, the function is applicable only with multivariate response SPLS.

Value

NULL.

Author(s)

Dongjun Chung, Hyonho Chun, and Sunduz Keles.

References

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3–25.

See Also

ci.spls, and correct.spls and plot.spls.

Examples

data(yeast)
# SPLS with eta=0.7 & 8 hidden components
f <- spls( yeast$x, yeast$y, K=8, eta=0.7 )
# Draw estimated coefficient plot of the first four variables
# among the selected predictors
coefplot.spls( f, xvar=c(1:4), nwin=c(2,2) )

Correct the initial SPLS coefficient estimates based on bootstrapped confidence intervals

Description

Correct initial SPLS coefficient estimates of the selected predictors based on bootstrapped confidence intervals and draw heatmap of original and corrected coefficient estimates.

Usage

correct.spls( object, plot.it=TRUE )

Arguments

Details

The set of the selected variables is updated by setting the coefficients with zero-containing confidence intervals to zero.

Value

Invisibly returns a matrix of corrected coefficient estimates.

Author(s)

Dongjun Chung, Hyonho Chun, and Sunduz Keles.

References

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3–25.

See Also

ci.spls.

Examples

data(mice)
# SPLS with eta=0.6 & 1 latent components
f <- spls( mice$x, mice$y, K=1, eta=0.6 )
# Calculate confidence intervals of coefficients
ci.f <- ci.spls(f)
# Corrected coefficient estimates
cf <- correct.spls( ci.f )
cf[20,1:5]

Compute and plot the cross-validated error for SGPLS classification

Description

Draw heatmap of v-fold cross-validated misclassification rates and return optimal eta (thresholding parameter) and K (number of hidden components).

Usage

cv.sgpls( x, y, fold=10, K, eta, scale.x=TRUE, plot.it=TRUE,
        br=TRUE, ftype='iden', n.core=8 )

Arguments

Details

Parallel computing can be utilized for faster computation. Users can change the number of CPUs to be used by changing the argument n.core.

Value

Invisibly returns a list with components:

Author(s)

Dongjun Chung and Sunduz Keles.

References

Chung D and Keles S (2010), "Sparse partial least squares classification for high dimensional data", Statistical Applications in Genetics and Molecular Biology, Vol. 9, Article 17.

See Also

print.sgpls, predict.sgpls, and coef.sgpls.

Examples

    data(prostate)
    set.seed(1)

    # misclassification rate plot. eta is searched between 0.1 and 0.9 and
    # number of hidden components is searched between 1 and 5
    ## Not run: 
        cv <- cv.sgpls(prostate$x, prostate$y, K = c(1:5), eta = seq(0.1,0.9,0.1),
            scale.x=FALSE, fold=5)
    
## End(Not run)
    
    

    (sgpls(prostate$x, prostate$y, eta=cv$eta.opt, K=cv$K.opt, scale.x=FALSE))

Compute and plot cross-validated mean squared prediction error for SPLS regression

Description

Draw heatmap of v-fold cross-validated mean squared prediction error and return optimal eta (thresholding parameter) and K (number of hidden components).

Usage

cv.spls( x, y, fold=10, K, eta, kappa=0.5,
        select="pls2", fit="simpls",
        scale.x=TRUE, scale.y=FALSE, plot.it=TRUE )

Arguments

Value

Invisibly returns a list with components:

Author(s)

Dongjun Chung, Hyonho Chun, and Sunduz Keles.

References

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3–25.

See Also

print.spls, plot.spls, predict.spls, and coef.spls.

Examples

    data(yeast)
    set.seed(1)

    # MSPE plot. eta is searched between 0.1 and 0.9 and
    # number of hidden components is searched between 1 and 10

    ## Not run: 
        cv <- cv.spls(yeast$x, yeast$y, K = c(1:10), eta = seq(0.1,0.9,0.1))
        
        # Optimal eta and K
        cv$eta.opt
        cv$K.opt
        (spls(yeast$x, yeast$y, eta=cv$eta.opt, K=cv$K.opt))
    
## End(Not run)

Compute and plot cross-validated error for SPLSDA classification

Description

Draw heatmap of v-fold cross-validated misclassification rates and return optimal eta (thresholding parameter) and K (number of hidden components).

Usage

cv.splsda( x, y, fold=10, K, eta, kappa=0.5,
        classifier=c('lda','logistic'), scale.x=TRUE, plot.it=TRUE, n.core=8 )

Arguments

Details

Parallel computing can be utilized for faster computation. Users can change the number of CPUs to be used by changing the argument n.core.

Value

Invisibly returns a list with components:

Author(s)

Dongjun Chung and Sunduz Keles.

References

Chung D and Keles S (2010), "Sparse partial least squares classification for high dimensional data", Statistical Applications in Genetics and Molecular Biology, Vol. 9, Article 17.

See Also

print.splsda, predict.splsda, and coef.splsda.

Examples

data(prostate)
set.seed(1)
# misclassification rate plot. eta is searched between 0.1 and 0.9 and
# number of hidden components is searched between 1 and 5
## Not run:  cv <- cv.splsda( prostate$x, prostate$y, K = c(1:5), eta = seq(0.1,0.9,0.1),
         scale.x=FALSE, fold=5 )
## End(Not run)

(splsda( prostate$x, prostate$y, eta=cv$eta.opt, K=cv$K.opt, scale.x=FALSE ))

Lymphoma Gene Expression Dataset

Description

This is the Lymphoma Gene Expression dataset used in Chung and Keles (2010).

Usage

 data(lymphoma) 

Format

A list with two components:

x

Gene expression data. A matrix with 62 rows and 4026 columns.

y

Class index. A vector with 62 elements.

Details

The lymphoma dataset consists of 42 samples of diffuse large B-cell lymphoma (DLBCL), 9 samples of follicular lymphoma (FL), and 11 samples of chronic lymphocytic leukemia (CLL). DBLCL, FL, and CLL classes are coded in 0, 1, and 2, respectively, in y vector. Matrix x is gene expression data and arrays were normalized, imputed, log transformed, and standardized to zero mean and unit variance across genes as described in Dettling (2004) and Dettling and Beuhlmann (2002). See Chung and Keles (2010) for more details.

Source

Alizadeh A, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson J Jr, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, and Staudt LM (2000), "Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling", Nature, Vol. 403, pp. 503–511.

References

Chung D and Keles S (2010), "Sparse partial least squares classification for high dimensional data", Statistical Applications in Genetics and Molecular Biology, Vol. 9, Article 17.

Dettling M (2004), "BagBoosting for tumor classification with gene expression data", Bioinformatics, Vol. 20, pp. 3583–3593.

Dettling M and Beuhlmann P (2002), "Supervised clustering of genes", Genome Biology, Vol. 3, pp. research0069.1–0069.15.

Examples

data(lymphoma)
lymphoma$x[1:5,1:5]
lymphoma$y

Mice Dataset

Description

This is the Mice dataset used in Chun and Keles (2010).

Usage

 data(mice) 

Format

A list with two components:

x

Marker map data. A matrix with 60 rows and 145 columns.

y

Gene expression data. A matrix with 60 rows and 83 columns.

Details

The Mice dataset was published by Lan et al. (2006). Matrix x is the marker map consisting of 145 microsatellite markers from 19 non-sex mouse chromosomes. Matrix y is gene expression measurements of the 83 transcripts from liver tissues of 60 mice. This group of the 83 transcripts is one of the clusters analyzed by Chun and Keles (2010). See Chun and Keles (2010) for more details.

Source

Lan H, Chen M, Flowers JB, Yandell BS, Stapleton DS, Mata CM, Mui E, Flowers MT, Schueler KL, Manly KF, Williams RW, Kendziorski C, and Attie AD (2006), "Combined expression trait correlations and expression quantitative trait locus mapping", PLoS Genetics, Vol. 2, e6.

References

Chun H and Keles S (2009), "Expression quantitative trait loci mapping with multivariate sparse partial least squares regression", Genetics, Vol. 182, pp. 79–90.

Examples

data(mice)
mice$x[1:5,1:5]
mice$y[1:5,1:5]

Plot the coefficient path of SPLS regression

Description

Provide the coefficient path plot of SPLS regression as a function of the number of hidden components (K) when eta is fixed.

Usage

## S3 method for class 'spls'
plot( x, yvar=c(1:ncol(x$y)), ... )

Arguments

Details

plot.spls provides the coefficient path plot of SPLS fits. The plot shows how estimated coefficients change as a function of the number of hidden components (K), when eta is fixed at the value used by the original SPLS fit.

Value

NULL.

Author(s)

Dongjun Chung, Hyonho Chun, and Sunduz Keles.

References

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3–25.

See Also

print.spls, predict.spls, and coef.spls.

Examples

data(yeast)
# SPLS with eta=0.7 & 8 hidden components
f <- spls( yeast$x, yeast$y, K=8, eta=0.7 )
# Draw coefficient path plots for the first two responses
plot( f, yvar=c(1:2) )

Make predictions or extract coefficients from a fitted SGPLS model

Description

Make predictions or extract coefficients from a fitted SGPLS object.

Usage

## S3 method for class 'sgpls'
predict( object, newx, type = c("fit","coefficient"),
    fit.type = c("class","response"), ... )
## S3 method for class 'sgpls'
coef( object, ... )

Arguments

Details

Users can input either only selected variables or all variables for newx.

Value

Matrix of coefficient estimates if type="coefficient". Matrix of predicted responses if type="fit" (responses will be predicted classes if fit.type="class" or predicted probabilities if fit.type="response").

Author(s)

Dongjun Chung and Sunduz Keles.

References

Chung D and Keles S (2010), "Sparse partial least squares classification for high dimensional data", Statistical Applications in Genetics and Molecular Biology, Vol. 9, Article 17.

See Also

print.sgpls.

Examples

data(prostate)
# SGPLS with eta=0.55 & 3 hidden components
f <- sgpls( prostate$x, prostate$y, K=3, eta=0.55, scale.x=FALSE )
# Print out coefficients
coef.f <- coef(f)
coef.f[ coef.f!=0, ]
# Prediction on the training dataset
(pred.f <- predict( f, type="fit" ))

Make predictions or extract coefficients from a fitted SPLS model

Description

Make predictions or extract coefficients from a fitted SPLS object.

Usage

## S3 method for class 'spls'
predict( object, newx, type = c("fit","coefficient"), ... )
## S3 method for class 'spls'
coef( object, ... )

Arguments

Details

Users can input either only selected variables or all variables for newx.

Value

Matrix of coefficient estimates if type="coefficient". Matrix of predicted responses if type="fit".

Author(s)

Dongjun Chung, Hyonho Chun, and Sunduz Keles.

References

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3–25.

See Also

plot.spls and print.spls.

Examples

data(yeast)
# SPLS with eta=0.7 & 8 latent components
f <- spls( yeast$x, yeast$y, K=8, eta=0.7 )
# Coefficient estimates of the SPLS fit
coef.f <- coef(f)
coef.f[1:5,]
# Prediction on the training dataset
pred.f <- predict( f, type="fit" )
pred.f[1:5,]

Make predictions or extract coefficients from a fitted SPLSDA model

Description

Make predictions or extract coefficients from a fitted SPLSDA object.

Usage

## S3 method for class 'splsda'
predict( object, newx, type = c("fit","coefficient"),
    fit.type = c("class","response"), ... )
## S3 method for class 'splsda'
coef( object, ... )

Arguments

Details

Users can input either only selected variables or all variables for newx.

Value

Matrix of coefficient estimates if type="coefficient". Matrix of predicted responses if type="fit" (responses will be predicted classes if fit.type="class" or predicted probabilities if fit.type="response").

Author(s)

Dongjun Chung and Sunduz Keles.

References

Chung D and Keles S (2010), "Sparse partial least squares classification for high dimensional data", Statistical Applications in Genetics and Molecular Biology, Vol. 9, Article 17.

See Also

print.splsda.

Examples

data(prostate)
# SPLSDA with eta=0.8 & 3 hidden components
f <- splsda( prostate$x, prostate$y, K=3, eta=0.8, scale.x=FALSE )
# Print out coefficients
coef.f <- coef(f)
coef.f[ coef.f!=0, ]
# Prediction on the training dataset
(pred.f <- predict( f, type="fit" ))

Print function for a SGPLS object

Description

Print out SGPLS fit, the number and the list of selected predictors.

Usage

## S3 method for class 'sgpls'
print( x, ... )

Arguments

Value

NULL.

Author(s)

Dongjun Chung and Sunduz Keles.

References

Chung D and Keles S (2010), "Sparse partial least squares classification for high dimensional data", Statistical Applications in Genetics and Molecular Biology, Vol. 9, Article 17.

See Also

predict.sgpls and coef.sgpls.

Examples

data(prostate)
# SGPLS with eta=0.55 & 3 hidden components
f <- sgpls( prostate$x, prostate$y, K=3, eta=0.55, scale.x=FALSE )
print(f)

Print function for a SPLS object

Description

Print out SPLS fit, the number and the list of selected predictors.

Usage

## S3 method for class 'spls'
print( x, ... )

Arguments

Value

NULL.

Author(s)

Dongjun Chung, Hyonho Chun, and Sunduz Keles.

References

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection," Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3–25.

See Also

plot.spls, predict.spls, and coef.spls.

Examples

data(yeast)
# SPLS with eta=0.7 & 8 hidden components
f <- spls( yeast$x, yeast$y, K=8, eta=0.7 )
print(f)

Print function for a SPLSDA object

Description

Print out SPLSDA fits, the number and the list of selected predictors.

Usage

## S3 method for class 'splsda'
print( x, ... )

Arguments

Value

NULL.

Author(s)

Dongjun Chung and Sunduz Keles.

References

Chung D and Keles S (2010), "Sparse partial least squares classification for high dimensional data", Statistical Applications in Genetics and Molecular Biology, Vol. 9, Article 17.

See Also

predict.splsda and coef.splsda.

Examples

data(prostate)
# SPLSDA with eta=0.8 & 3 hidden components
f <- splsda( prostate$x, prostate$y, K=3, eta=0.8, scale.x=FALSE )
print(f)

Prostate Tumor Gene Expression Dataset

Description

This is the Prostate Tumor Gene Expression dataset used in Chung and Keles (2010).

Usage

 data(prostate) 

Format

A list with two components:

x

Gene expression data. A matrix with 102 rows and 6033 columns.

y

Class index. A vector with 102 elements.

Details

The prostate dataset consists of 52 prostate tumor and 50 normal samples. Normal and tumor classes are coded in 0 and 1, respectively, in y vector. Matrix x is gene expression data and arrays were normalized, log transformed, and standardized to zero mean and unit variance across genes as described in Dettling (2004) and Dettling and Beuhlmann (2002). See Chung and Keles (2010) for more details.

Source

Singh D, Febbo P, Ross K, Jackson D, Manola J, Ladd C, Tamayo P, Renshaw A, DAmico A, Richie J, Lander E, Loda M, Kantoff P, Golub T, and Sellers W (2002), "Gene expression correlates of clinical prostate cancer behavior", Cancer Cell, Vol. 1, pp. 203–209.

References

Chung D and Keles S (2010), "Sparse partial least squares classification for high dimensional data", Statistical Applications in Genetics and Molecular Biology, Vol. 9, Article 17.

Dettling M (2004), "BagBoosting for tumor classification with gene expression data", Bioinformatics, Vol. 20, pp. 3583–3593.

Dettling M and Beuhlmann P (2002), "Supervised clustering of genes", Genome Biology, Vol. 3, pp. research0069.1–0069.15.

Examples

data(prostate)
prostate$x[1:5,1:5]
prostate$y

Fit SGPLS classification models

Description

Fit a SGPLS classification model.

Usage

sgpls( x, y, K, eta, scale.x=TRUE,
        eps=1e-5, denom.eps=1e-20, zero.eps=1e-5, maxstep=100,
        br=TRUE, ftype='iden' )

Arguments

Details

The SGPLS method is described in detail in Chung and Keles (2010). SGPLS provides PLS-based classification with variable selection, by incorporating sparse partial least squares (SPLS) proposed in Chun and Keles (2010) into a generalized linear model (GLM) framework. y is assumed to have numerical values, 0, 1, ..., G, where G is the number of classes subtracted by one.

Value

A sgpls object is returned. print, predict, coef methods use this object.

Author(s)

Dongjun Chung and Sunduz Keles.

References

Chung D and Keles S (2010), "Sparse partial least squares classification for high dimensional data", Statistical Applications in Genetics and Molecular Biology, Vol. 9, Article 17.

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3–25.

See Also

print.sgpls, predict.sgpls, and coef.sgpls.

Examples

    data(prostate)

    # SGPLS with eta=0.6 & 3 hidden components
    (f <- sgpls(prostate$x, prostate$y, K=3, eta=0.6, scale.x=FALSE))

    # Print out coefficients
    coef.f <- coef(f)
    coef.f[coef.f!=0, ]

Fit SPLS regression models

Description

Fit a SPLS regression model.

Usage

spls( x, y, K, eta, kappa=0.5, select="pls2", fit="simpls",
    scale.x=TRUE, scale.y=FALSE, eps=1e-4, maxstep=100, trace=FALSE)

Arguments

Details

The SPLS method is described in detail in Chun and Keles (2010). SPLS directly imposes sparsity on the dimension reduction step of PLS in order to achieve accurate prediction and variable selection simultaneously. The option select refers to the PLS algorithm for variable selection. The option fit refers to the PLS algorithm for model fitting and spls utilizes algorithms offered by the pls package for this purpose. See help files of the function plsr in the pls package for more details. The user should install the pls package before using spls functions. The choices for select and fit are independent.

Value

A spls object is returned. print, plot, predict, coef, ci.spls, coefplot.spls methods use this object.

Author(s)

Dongjun Chung, Hyonho Chun, and Sunduz Keles.

References

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3–25.

See Also

print.spls, plot.spls, predict.spls, coef.spls, ci.spls, and coefplot.spls.

Examples

    data(yeast)
    # SPLS with eta=0.7 & 8 hidden components
    (f <- spls(yeast$x, yeast$y, K=8, eta=0.7))

    # Print out coefficients
    coef.f <- coef(f)
    coef.f[,1]

    # Coefficient path plot
    plot(f, yvar=1)
    dev.new()

    # Coefficient plot of selected variables
    coefplot.spls(f, xvar=c(1:4))

Internal SPLS functions

Description

Internal SPLS functions.

Usage

heatmap.spls( mat, coln=16, as='n', ... )
spls.dv( Z, eta, kappa, eps, maxstep )
ust( b, eta )
correctp( x, y, eta, K, kappa, select, fit )
cv.split( y, fold )
wpls( x, y, V, K=ncol(x), type="pls1",
        center.x=TRUE, scale.x=FALSE )
sgpls.binary( x, y, K, eta, scale.x=TRUE,
        eps=1e-5, denom.eps=1e-20, zero.eps=1e-5, maxstep=100,
        br=TRUE, ftype='iden' )
sgpls.multi( x, y, K, eta, scale.x=TRUE,
        eps=1e-5, denom.eps=1e-20, zero.eps=1e-5, maxstep=100,
        br=TRUE, ftype='iden' )
cv.sgpls.binary( x, y, fold=10, K, eta, scale.x=TRUE, plot.it=TRUE,
    br=TRUE, ftype='iden', n.core=8 )
cv.sgpls.multi( x, y, fold=10, K, eta, scale.x=TRUE, plot.it=TRUE,
    br=TRUE, ftype='iden', n.core=8 )

Details

These are not to be called by the user.

Author(s)

Dongjun Chung, Hyonho Chun, and Sunduz Keles.

References

Chung D and Keles S (2010), "Sparse partial least squares classification for high dimensional data", Statistical Applications in Genetics and Molecular Biology, Vol. 9, Article 17.

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3–25.

Fit SPLSDA classification models

Description

Fit a SPLSDA classification model.

Usage

splsda( x, y, K, eta, kappa=0.5,
    classifier=c('lda','logistic'), scale.x=TRUE, ... )

Arguments

Details

The SPLSDA method is described in detail in Chung and Keles (2010). SPLSDA provides a two-stage approach for PLS-based classification with variable selection, by directly imposing sparsity on the dimension reduction step of PLS using sparse partial least squares (SPLS) proposed in Chun and Keles (2010). y is assumed to have numerical values, 0, 1, ..., G, where G is the number of classes subtracted by one. The option classifier refers to the classifier used in the second step of SPLSDA and splsda utilizes algorithms offered by MASS and nnet packages for this purpose. If classifier="logistic", then either logistic regression or multinomial regression is used. Linear discriminant analysis (LDA) is used if classifier="lda". splsda also utilizes algorithms offered by the pls package for fitting spls. The user should install pls, MASS and nnet packages before using splsda functions.

Value

A splsda object is returned. print, predict, coef methods use this object.

Author(s)

Dongjun Chung and Sunduz Keles.

References

Chung D and Keles S (2010), "Sparse partial least squares classification for high dimensional data", Statistical Applications in Genetics and Molecular Biology, Vol. 9, Article 17.

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3–25.

See Also

print.splsda, predict.splsda, and coef.splsda.

Examples

data(prostate)
# SPLSDA with eta=0.8 & 3 hidden components
f <- splsda( prostate$x, prostate$y, K=3, eta=0.8, scale.x=FALSE )
print(f)
# Print out coefficients
coef.f <- coef(f)
coef.f[ coef.f!=0, ]

Yeast Cell Cycle Dataset

Description

This is the Yeast Cell Cycle dataset used in Chun and Keles (2010).

Usage

 data(yeast) 

Format

A list with two components:

x

ChIP-chip data. A matrix with 542 rows and 106 columns.

y

Cell cycle gene expression data. A matrix with 542 rows and 18 columns.

Details

Matrix y is cell cycle gene expression data (Spellman et al., 1998) of 542 genes from an \alpha factor based experiment. Each column corresponds to mRNA levels measured at every 7 minutes during 119 minutes (a total of 18 measurements). Matrix x is the chromatin immunoprecipitation on chip (ChIP-chip) data of Lee et al. (2002) and it contains the binding information for 106 transcription factors. See Chun and Keles (2010) for more details.

Source

Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, Hannett NM, Harbison CT, Thomson CM, Simon I, Zeitlinger J, Jennings EG, Murray HL, Gordon DB, Ren B, Wyrick JJ, Tagne JB, Volkert TL, Fraenkel E, Gifford DK, and Young RA (2002), "Transcriptional regulatory networks in Saccharomyces cerevisiae", Science, Vol. 298, pp. 799–804.

Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D, and Futcher B (1998), "Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hydrization", Molecular Biology of the Cell, Vol. 9, pp. 3273–3279.

References

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3–25.

Examples

data(yeast)
yeast$x[1:5,1:5]
yeast$y[1:5,1:5]