Sparse PLS on R

Reference

• An Introduction to the ‘spls’ Package, Version 1.0
• Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection

library

install.packages("spls")

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library(spls)

Sparse Partial Least Squares (SPLS) Regression and
Classification (version 2.2-3)

library(ggplot2)

data

data(yeast)

xはクロマチンの免疫沈降、yは発現サイクル？

help(yeast)

yeast {spls}

R Documentation

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 α 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]

---

[Package spls version 2.2-3 ]

yeast$x[1:5,1:5]

A matrix: 5 × 5 of type dbl

	ABF1_YPD	ACE2_YPD	ADR1_YPD	ARG80_YPD	ARG81_YPD
21	-0.2722730	0.21932294	0.9238359567	-0.4755756	-0.10389318
41	0.1691280	0.53831198	0.0097604993	-0.3219534	-0.19750606
71	-0.1388962	0.02636382	0.0877516229	-0.2234093	0.10307741
78	-0.2865169	-0.31409427	-0.0454998435	0.3262217	0.27757502
102	-0.4950561	-0.14827419	0.0002987512	-0.2179458	-0.02539585

plot(yeast$x[,1])

yeast$y[1:5,1:5]

A matrix: 5 × 5 of type dbl

	alpha0	alpha7	alpha14	alpha21	alpha28
1	-0.36	-0.42	0.29	-0.14	-0.19
2	1.04	0.19	0.47	-1.03	-0.63
5	-0.30	-0.45	0.75	0.37	0.27
8	-0.46	0.12	-0.06	-0.76	-0.70
9	-1.35	-0.86	-0.22	-0.38	-0.65

plot(yeast$y[,1])

↓ 変数が542個もある・・

str(yeast)

List of 2
 $ x: num [1:542, 1:106] -0.272 0.169 -0.139 -0.287 -0.495 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:542] "21" "41" "71" "78" ...
  .. ..$ : chr [1:106] "ABF1_YPD" "ACE2_YPD" "ADR1_YPD" "ARG80_YPD" ...
 $ y: num [1:542, 1:18] -0.36 1.04 -0.3 -0.46 -1.35 -2.06 -1.61 -0.07 0.11 0.15 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:542] "1" "2" "5" "8" ...
  .. ..$ : chr [1:18] "alpha0" "alpha7" "alpha14" "alpha21" ...

SPLS

set.seed(1)
cv <- cv.spls(yeast$x, yeast$y, eta = seq(0.1,0.9,0.1), K = c(5:10))

eta = 0.1 
eta = 0.2 
eta = 0.3 
eta = 0.4 
eta = 0.5 
eta = 0.6 
eta = 0.7 
eta = 0.8 
eta = 0.9 

Optimal parameters: eta = 0.6, K = 8

f <- spls(yeast$x, yeast$y, eta = cv$eta.opt, K = cv$K.opt)

print(f)

Sparse Partial Least Squares for multivariate responses
----
Parameters: eta = 0.6, K = 8, kappa = 0.5
PLS algorithm:
pls2 for variable selection, simpls for model fitting

SPLS chose 56 variables among 106 variables

Selected variables: 
ACE2_YPD	ARG80_YPD	ARG81_YPD	ASH1_YPD	AZF1_YPD	
BAS1_YPD	CBF1_YPD	CHA4_YPD	CRZ1_YPD	FHL1_YPD	
FKH1_YPD	FKH2_YPD	FZF1_YPD	GAT1_YPD	GAT3_YPD	
GCN4_YPD	GCR2_YPD	GLN3_YPD	HAA1_YPD	HAP2_YPD	
HAP5_YPD	HIR1_YPD	HIR2_YPD	IME4_YPD	INO4_YPD	
A1..MATA1._YPD	MBP1_YPD	MCM1_YPD	MET4_YPD	MSN2_YPD	
NDD1_YPD	NRG1_YPD	PHD1_YPD	PHO2_YPD	PUT3_YPD	
RCS1_YPD	REB1_YPD	RFX1_YPD	RIM101_YPD	RME1_YPD	
RTG1_YPD	RTG3_YPD	SIP4_YPD	SOK2_YPD	STB1_YPD	
STE12_YPD	STP2_YPD	SWI4_YPD	SWI5_YPD	SWI6_YPD	
THI2_YPD	YAP1_YPD	YAP6_YPD	YAP7_YPD	YFL044C_YPD	
YJL206C_YPD	

coef.f <- coef(f)

coef.f[1:5,1:5]

A matrix: 5 × 5 of type dbl

	alpha0	alpha7	alpha14	alpha21	alpha28
ABF1_YPD	0.0000000	0.000000000	0.00000000	0.0000000000	0.000000000
ACE2_YPD	0.0874325	0.068452293	0.01374781	-0.0002541969	-0.033302624
ADR1_YPD	0.0000000	0.000000000	0.00000000	0.0000000000	0.000000000
ARG80_YPD	-0.0486881	-0.019092797	0.02063442	0.0300421634	0.007925553
ARG81_YPD	-0.0168849	0.009465868	0.06353825	0.0541704059	0.006978985

plot.spls(f, yvar=1 )

coefplot.spls( f, nwin=c(2,2), xvar=c(1:4) )