김성환(계명대학교 통계학과) R 패키지 메타주성분분석(MetaPCA)

1. Genomic meta-analysis in
combining expression profiles
(Packages for MetaQC , MetaDE, MetaPCA for QC,
biomarker detection and visualization)

2. Outline
1. Introduction
2. Quality control (MetaQC)
3. Meta-analysis for detecting differentially
expressed genes (MetaDE)
4. Integrative visualization (MetaPCA)

3. 1. Introduction

4. Experimental design
Image analysis
Preprocessing
(Normalization, filtering,
MV imputation)
Data visualization
Identify differentially
expressed genes
Regulatory network
Clustering Classification
Statistical Issues in Microarray Analysis
Gene enrichment
analysis
Integrative analysis &
meta-analysis

5. Motivation
• Microarray has become a common tool in
biological investigation. Related high-
throughput technologies (SNP array, ChIP-
chip, next-generation sequencing) are also
getting popular.
• As many data sets are publicly available,
information integration of multiple studies
becomes important.

6. Meta-analysis and integrative analysis

7. Meta-analysis and integrative analysis
• Horizontal genomic meta-analysis:
– Combine multiple relevant studies (e.g. microarray
or GWAS) to (1) increase statistical power (2)
provide robust and accurate validation across
studies and (3) guide future experiments
• Vertical genomic integrative analysis:
– Integrate multiple studies that measure multiple
dimension of genetic information of the same
cohort (e.g. transcription, genotyping, copy
number variation, methylation, miRNA etc).

8. group)(diseased1
,group)(control1
,1,1
study,sample
,geneofintensityexpression:
kkk
k
gsk
nmsm
ms
KkGg
ks
gx
+≤≤+
≤≤
≤≤≤≤
study 1
genes
N … N T … T
statistic
1 t11
2 t21
3 t31
… …
G tG1
study K
genes
N … N T … T
statistic
1 t1K
2 tK
3 t3K
… …
G tGK
!
study 2
genes
N … N T … T
statistic
1 t12
2 t22
3 t32
… …
G tG2
Motivation and background
Data considered:

9. Summary of microarray meta-analysis

10. Summary of GWAS meta-analysis

11. Summary of GWAS meta-analysis

13. http://www.pitt.edu/~tsengweb/MetaOmicsHome.htm

14. 2. Quality control (MetaQC)

15. MetaQC
Quality control analysis to determine
inclusion/exclusion criteria for microarray meta-
analysis
• Dongwan D. Kang, Etienne Sibille, Naftali Kaminski, and
George C. Tseng*. (2012) MetaQC: Objective Quality Control
and Inclusion/Exclusion Criteria for Genomic Meta-Analysis.
Nucleic Acids Research. 40(2):e15.

16. Inclusion/exclusion criteria
Examples of inclusion/exclusion criteria in the literature:
• Collect whatever microarray data sets available to
combine
• Go to GEO to retrieve all relevant studies in Affymetrix
U133
• At least four samples in each class label
…
Problem: ad hoc criteria and “expert” opinion
Aim: Is it possible to develop a quantitative quality
assessment to perform inclusion/exclusion of the
microarray studies?

17. Six quality control (QC) measures
Each QC measure is defined as minus log-transformed p-
values from formal hypothesis testing.

18. Four example tested

19. install.packages("MetaQC")
library(MetaQC)
requireAll(c("proto", "foreach"))
#Toy Example
data(brain) #already hugely filtered
#Two default gmt files are automatically downloaded,
#otherwise it is required to locate it correctly.
#Refer to http://www.broadinstitute.org/gsea/downloads.jsp
#For parallel computation with only 2 cores
#R >= 2.11.0 in windows to use parallel computing
brainQC <- MetaQC(brain, "c2.cp.biocarta.v3.0.symbols.gmt",
filterGenes=FALSE, verbose=TRUE, isParallel=TRUE,
nCores=2)
#B is recommended to be >= 1e4 in real application
runQC(brainQC, B=1e2, fileForCQCp="c2.all.v3.0.symbols.gmt")
plot(brainQC)
R-codes to execute

20. Brain cancer example
Paugh and Yamanaka have
lower quality and will be
excluded from meta-analysis.
These two studies have small
sample sizes.

21. 3. Meta-analysis for detecting
differentially expressed genes
(MetaDE)

22. Prostate cancer data
• Each study contains small number of samples. Makes sense
to perform meta-analysis.

23. Goal of meta-analysis
Goal of meta-analysis:
• What kind of biomarkers is of interest:
– Biomarkers statistically significant and consistent
in all (or majority) of the studies.
– Biomarkers statistically significant in one or more
studies.

24. • Biomarkers detected
by Fisher’s method
(EW) and ordered by
hierarchical
clustering.
• Genes are DE in one
or more studies but
no indication of which
ones.
Fisher’s method
Fisher vs AW

25. • Biomarkers detected by
AW method and
ordered by hierarchical
clustering.
• The optimal weights
provide natural
categorization and
interpretation of
biomarkers.
Adaptively weighted (AW)
Fisher vs AW

26. install.packages("MetaDE")
library(MetaDE)
#Meta analysis of DE genes between two classes
#Two pseudo datasets
label1<-rep(0:1,each=5)
label2<-rep(0:1,each=5)
exp1<-cbind(matrix(rnorm(5*20),20,5),matrix(rnorm(5*20,2),20,5))
exp2<-cbind(matrix(rnorm(5*20),20,5),matrix(rnorm(5*20,1.5),20,5))
x<-list(list(exp1,label1),list(exp2,label2))
# modt tests for individual study and used Fisher's method to
combine results
MetaDE.rawdata(x=x,ind.method=c('modt','modt'),meta.method='Fisher',
nperm=20)
R-codes to execute

27. The available statistical tests for argument, ind.method:
• "regt": Two-sample t-statistics (unequal variances).
• "modt": Two-sample t-statistics with the variance is modified by
adding a fudging parameter. In our algorithm, we choose the
penalized t-statistics used in Efron et al.(2001) and Tusher et al.
(2001). The fudge parameter s0 is chosen to be the median
variability estimator in the
genome.
• "pairedt": Paired t-statistics for the design of paired samples.
• "F":, the test is based on F-statistics. It is usually chosen
where there are 2 or more classes.
R-codes to execute

28. The options “meta.method”
• "maxP": the maximum of p value method.
• "maxP.OC": the maximum of p values with one-sided
correction.
• "minP": the minimum of p values from "test" across
studies.
• "minP.OC": the minimum of p values with one-sided
correction.
• "Fisher": Fisher’s method (Fisher, 1932),the summation
of -log(p-value) across studies.
• "Fisher.OC": Fisher’s method with one-sided correction
(Fisher, 1932),the summation of -log(p-value) across
studies.
• "AW": Adaptively-weighted method (Li and Tseng, 2011).
• "AW.OC": Adaptively-weighted method with one-sided
correction (Li and Tseng, 2011).
• "FEM": the Fixed-effect model method.
• "REM": the Random-effect model method.

29. 4. Integrative visualization
(MetaPCA)

30. • Microarray experiment (mRNA) for analyzing
mouse metabolisms
• Three class labels of samples: Three genotype
mice: wild-type (WT), LCAD knock-out (LCAD)
and VLCAD knock-out (VLCAD).
• Four microarray datasets (Brown fat, Skeletal,
Liver and Heart; 44 samples in total).
• Pre-processing: low-expressed features
(mean<0.7, SD <0.7) and gene matching leave
1,304 features in analysis.

31. Motivation examples of dimension reduction

32. Geometrical PC space comparison

35. • Spellman’s yeast cell cycle data (Spellman et al., 1998) forms
time-dependent gene expression profiles that are used to
monitor transcriptomic variation during yeast cell cycles.
• Yeast cells were arrested to the same cell cycle stage using four
different synchronizing methods: α arrest (alpha), arrest of cdc15
or cdc28 temperature-sensitive mutant (cdc15 and cdc28), and
elutriation (elu).
• A total of 18, 24, 17 and 14 time points were considered for
synchronization.
• We matched up features across all four studies and filtered out
features using standard deviation (i.e. SD ≤ 0.45, non-
informative features with smaller variation) that left 1,025
features.
Spellman’s Yeast dataset

37. Prostate cancer data with three class
labels (normal, primary, metastasis)
Library(MetaPCA)
# Data preparation
data(prostate)
#There are currently 4 meta-pca methods. Run either one of
following four.
MetaPCA(prostate, method="Angle", doPreprocess=FALSE)

38. #Plotting four data on the common PC space
coord <- foreach(dd=iter(metaPC$x), .combine=rbind) %do% dd$coord
PlotPC2D(coord[,1:2], drawEllipse=F, dataset.name="Prostate", .class.order=
c("Metastasis","Primary","Normal"), .class.color=c("red", "#838383", "blue"),
.annotation=T, newPlot=T,.class2=rep(names(metaPC$x),
times=sapply(metaPC$x,function(x)nrow(x$coord))),
.class2.order=names(metaPC$x), .points.size=1)

39. Spellman, 1998 Yeast cell cycle data set
#Consider each synchronization method as a separate data
# Calling packages
install.packages("MetaPCA")
library(MetaPCA)
# Data preparation
data(Spellman)
# Perform individual PCAs
pc <- list(alpha=prcomp(t(Spellman$alpha))$x, cdc15=prcomp(t(Spellman$cdc15))$x,
cdc28=prcomp(t(Spellman$cdc28))$x, elu=prcomp(t(Spellman$elu))$x)
#There are currently 4 meta-pca methods. Run either one of following four.
MetaPCA(Spellman, method="Eigen", doPreprocess=FALSE)