Data Analysis of Microarrays Bioconductor.org

“affy” package

“limma” package

Open R and Load libraries

> load(affy)

Affymetrix Data files

CEL files contain information about the expression levels of the individual probes.

CDF file contains information about which probes belong to which probe set. The probe set information in the CEL file by itself is not particularly useful as there is no indication in the file as to which probe set a probe belongs. This information is stored in the CDF library file associated with a chip type. All the arrays belonging to a given type will share this same information.

Analysis

Sample Generation, experimental design, hybridization =) RAW DATA.

Data analysis. (a) Scanning and image analysis =) PROBE INTENSITIES. [low

level analysis] (b) Background correction, normalization and quantifying

expression measures =) NORMALIZED EXPRESSION MEASURES FOR EACH GENE (PROBE SET). [low level analysis]

(c) Ranking of genes, clustering/classification of genes =) INTERESTING GENE SET. [high level analysis]

Validation of the interesting genes: using additional biological data and performing targeted experiments.

Estrogen dataset

"The investigators in this experiment were interested in the effect of estrogen on the genes in ER+ breast cancer cells over time.

After serum starvation of all eight samples, they exposed four samples to estrogen, and then measured mRNA transcript abundance after 10 hours for two samples and 48 hours for the other two.

They left the remaining four samples untreated, and measured mRNA transcript abundance at 10 hours for two samples, and 48 hours for the other two. Since there are two factors in this experiment (estrogen and time), each at two levels (present or absent,10 hours or 48 hours), this experiment is said to have a 2x2 factorial design."

Experimental Design

Put .cel files in a directory

Estrogen.zip file should be extracted into a file named estrogen.

Working directory should be set to this file using the change dir command under the file menu.

Information on chips/samples

A text file that looks like the following should be prepared using a text editor and saved as filename.txt (e.g., estrogen.txt):

R: read the sample information

You have to load the file into a phenoData object

> pd <- read.phenoData("estrogen.txt", header = TRUE, row.names = 1)

> pData(pd)

phenoData objects

phenoData objects are where the Bioconductor Package stores information about samples treatment conditions in a cell line experiment or

clinical or histopathological characteristics of tissue biopsies.

The header option lets the read.phenoData function know that the first line in the file contains column headings, and the row.names option indicates that the first column of the file contains the row names.

phenoData objects

> pd <- read.phenoData("estrogen.txt", header = TRUE, row.names = 1)

> pData(pd)

estrogen time.h

low10-1.cel absent 10

low10-2.cel absent 10

high10-1.cel present 10

high10-2.cel present 10

low48-1.cel absent 48

low48-2.cel absent 48

high48-1.cel present 48

high48-2.cel present 48

Read .cel files and name the object as a

> a <- ReadAffy(filenames = rownames(pData(pd)), phenoData = pd,

+ verbose = TRUE)

Copy and paste the above statements onto the command line in R

Read .cel files and name the object as a

> a <- ReadAffy(filenames = rownames(pData(pd)), phenoData = pd,+ verbose = TRUE)1 reading low10-1.cel ...instanciating an AffyBatch (intensity a

409600x8 matrix)...done.Reading in : low10-1.celReading in : low10-2.celReading in : high10-1.celReading in : high10-2.celReading in : low48-1.celReading in : low48-2.celReading in : high48-1.celReading in : high48-2.cel

Type a

> aAffyBatch objectsize of arrays=640x640 features (25606 kb)cdf=HG_U95Av2 (12625 affyids)number of samples=8number of genes=12625annotation=hgu95av2

Visualize chips

>image(a[,1])

Visualize log2 transformed raw values using hist (histogram of distribution)

> hist(log2(intensity(a[, 4])), breaks = 100, col = "blue")

Boxplots

>boxplot(a, col = "red")

Normalize data using rma

> eset = rma(a)

>boxplot(data.frame(exprs(eset)), col = "blue")

MA Plots

Measurement of relative expression versus Average log intensity plot. Also known as the RI (Ratio versus Intensity).

MA plots can show the intensity-dependent ratio of raw microarray data. Let R represent raw intensity for one chip and G represent raw intensity for another chip. Define

M = log2(R/G) A = log2(RG)1/2.

MA plots after normalization

>mva.pairs(exprs(eset)[,c(1,3,5,7)],log.it=FALSE)

MA plots before normalization

> mva.pairs(pm(a)[,c(1,3,5)])

Memory Problems

If you experience memory errors, please restart your R session before trying a less memory-expensive alternative.

The following function justRMA() should read in all 8 arrays, normalize them, and create an object of class exprSet with as little as 128 Megabytes of RAM:

> library(affy)> pd <- read.phenoData("EstrogenTargets.txt",header=TRUE,row.names=1,as.is=TRUE)> eset <- justRMA(filenames=pData(pd)$FileName,phenoData=pd)> eset

Or to read in only the first four arrays, you can use:

>eset <- justRMA(filenames=pData(pd)$FileName[1:4],phenoData=pd)>eset

GlossaryTerm Definition

AffyID An identifier for a probe-pair set.

Affymetrix The largest company which manufactures (single-channel)

high-density oligonucleotide microarray chips.

CDF file

Chip Description File. Describes which probes go in which probe sets and the location of probe-pair sets (genes, gene fragments, ESTs). See http://www.bioconductor.org/data/metaData.html

CEL file Cell intensity file, including probe level PM and MM values.

Cell lineCells grown in tissue culture, representing generations of a

primary culture.

DAT fileImage file, approximately 10^7 pixels, approximately 50

MB.

Glossary

GenotypeThe type of RNA in a biological sample as described by the DNA sequence, e.g. using Single Nucleotide Polymorphisms (SNPs) or microsatellite markers.

MAS 5.0The main microarray analysis software from the Affymetrix company: MicroArraySuite-MAS, now version 5.

Mismatch(MM)

The same as PM but with a single homomericbase change for the middle (13th) base (transversionpurine<-> pyrimidine, G <->C, A <->T). The purpose of the MM probe design is to measure non-specific binding and background.

Perfect match(PM)

A 25-mer complementary to a reference sequence of interest (e.g., part of a gene).

Glossary

Phenotype

The type of RNA in a biological sample as described by physical characteristics of the biological sample, e.g. time or estrogen presence/absence or observed susceptibility to disease.

Probe An oligonucleotideof 25 base-pairs, i.e., a 25-mer.

Probe-pair A (PM,MM) pair.

Probe-pair setA collection of probe-pairs (16 to 20) related to a common gene or fraction of a gene.

RMA Robust Multichip Averaging (Irizarry et al. [6]).

TargetA type of RNA under a particular condition (e.g. Estrogen present) which is hybridized to a microarray chip.

Gene Filtering (Genefilter package)

> library(genefilter)Loading required package: survivalLoading required package: splines> f1 <- pOverA(0.25, log2(100))> f2 <- function(x) (IQR(x) > 0.5)> ff <- filterfun(f1, f2)> selected <- genefilter(eset, ff)> sum(selected)[1] 1664> esetSub <- eset[selected, ]

See some data

> esetSub[1:3, ]Expression Set (exprSet) with 3 genes 8 samples phenoData object with 2 variables and 8 cases varLabels estrogen: read from file time.h: read from file> exprs(esetSub[1:3, ]) low10-1.cel low10-2.cel high10-1.cel high10-2.cel low48-1.cel1005_at 9.206734 8.993805 8.237886 8.338003 9.1731921008_f_at 10.119335 10.986661 10.830301 10.025106 11.0446711009_at 10.553034 10.500375 11.159770 11.048265 10.211211 low48-2.cel high48-1.cel high48-2.cel1005_at 9.040490 7.926101 8.069681008_f_at 11.138321 10.705763 11.369521009_at 9.565864 11.363234 10.76293

Clustering Algorithms in R

Two main categories Agglomerative (e.g., hierarchical) Partitioning (e.g., kmeans)

Clustering Algorithms in R

First, dist is used to compute distances. This function takes a matrix as its first argument and computes the distances between the rows of the matrix.

We will use the expression data matrix from estrogen for this purpose

The commands below are used to carry out hierarchical clustering using the Manhattan distance metric and to plot the corresponding dendrogram with nodes labeled according to file names.

Cluster data (hierarchical)

> dgTr <- dist(t(exprs(esetSub)), method = "manhattan")

> hcgTr <- hclust(dgTr, method = "average")

Cluster data (hierarchical)

> kmgTr <- kmeans(t(exprs(esetSub)), centers = 2)> kmgTr$cluster low10-1.cel low10-2.cel high10-1.cel high10-2.cel low48-1.cel low48-2.cel 1 1 2 2 1 1 high48-1.cel high48-2.cel 2 2

T-test on a subset of estrogen data

>subEST=exprs(eset[,1:4]);> group1=c(1,1,2,2)> group1[1] 1 1 2 2> subESTExpression Set (exprSet) with 12625 genes 4 samples phenoData object with 2 variables and 4 cases varLabels estrogen: read from file time.h: read from file> tf1 <- ttest(group1, 0.1)> ff2 <- filterfun(tf1)> wh2 <- genefilter(exprs(subEST), ff2)> sum(wh2)[1] 1711>

ApoAI data

The data is from a study of lipid metabolism by Callow et al (2000). The apolipoprotein AI (ApoAI) gene is known to play a pivotal role in high density lipoprotein (HDL) metabolism. Mice which have the ApoAI gene knocked out have very low HDL cholesterol levels. The purpose of this experiment is to determine how ApoAI deficiency affects the action of other genes in the liver, with the idea that this will help determine the molecular pathways through which ApoAI operates.

Hybridizations

The experiment compared 8 ApoAI knockout mice with 8 wild type (normal) C57BL/6 ("black six") mice, the control mice. For each of these 16 mice, target mRNA was obtained from liver tissue and labelled using a Cy5 dye. The RNA from each mouse was hybridized to a separate microarray. Common reference RNA was labelled with Cy3 dye and used for all the arrays. The reference RNA was obtained by pooling RNA extracted from the 8 control mice.

Experimental Design

Load data

> library(limma)> load("ApoAI.RData")> objects()[1] "last.warning" "mart" "RG" > names(RG)[1] "R" "G" "Rb" "Gb" "printer"

"genes" "targets"

What did you download?> RG$targets FileName Cy3 Cy5c1 a1koc1.spot Pool C57BL/6c2 a1koc2.spot Pool C57BL/6c3 a1koc3.spot Pool C57BL/6c4 a1koc4.spot Pool C57BL/6c5 a1koc5.spot Pool C57BL/6c6 a1koc6.spot Pool C57BL/6c7 a1koc7.spot Pool C57BL/6c8 a1koc8.spot Pool C57BL/6k1 a1kok1.spot Pool ApoAI-/-k2 a1kok2.spot Pool ApoAI-/-k3 a1kok3.spot Pool ApoAI-/-k4 a1kok4.spot Pool ApoAI-/-k5 a1kok5.spot Pool ApoAI-/-k6 a1kok6.spot Pool ApoAI-/-k7 a1kok7.spot Pool ApoAI-/-k8 a1kok8.spot Pool ApoAI-/-

What does RG contain

Type in RG at the command line:>RG$R a1koc1 a1koc2 a1koc3 a1koc4 a1koc5 a1koc6 a1koc7 a1koc8 a1kok1[1,] 4184.08 3300.22 2432.54 2069.81 2809.83 2029.05 1720.56 1795.58 1845.97[2,] 4148.48 3774.18 2579.92 2566.33 2286.43 2244.33 1987.83 1908.33 2924.68[3,] 2452.32 3028.47 3574.45 2348.09 4462.67 2703.20 1543.10 2123.91 1702.91[4,] 1577.31 1999.92 1296.81 1926.69 1010.74 1764.58 1161.71 1495.13 1778.82[5,] 1525.48 1967.44 1210.10 1929.64 950.80 1638.89 936.42 1540.65 1554.96 a1kok2 a1kok3 a1kok4 a1kok5 a1kok6 a1kok7 a1kok8[1,] 2330.53 2630.76 2234.17 2811.72 2721.10 2361.67 2585.89[2,] 3027.00 2411.81 2023.39 4148.58 3384.13 2763.00 2775.44[3,] 2235.84 2343.61 4196.89 2079.53 4344.00 3051.24 3277.82[4,] 1313.93 1551.66 1359.58 1305.88 1512.14 1617.78 1584.25[5,] 1288.93 1378.91 1216.87 1218.35 1205.67 1412.08 1666.326379 more rows ...

What does RG contain

$G a1koc1 a1koc2 a1koc3 a1koc4 a1koc5 a1koc6 a1koc7 a1koc8 a1kok1[1,] 6256.08 5269.89 4080.85 3229.38 3763.22 2773.58 2800.44 1923.58 804.80[2,] 5389.81 3608.12 2614.42 2107.56 1878.30 1926.00 1711.17 1205.20 1385.96[3,] 2653.29 4168.16 8091.95 3134.39 3046.00 3612.40 2002.62 1651.41 915.24[4,] 1071.26 1162.71 819.13 1295.82 428.95 900.78 838.19 689.00 763.64[5,] 1321.75 1148.64 642.94 1289.74 500.49 684.00 571.92 736.41 685.88 a1kok2 a1kok3 a1kok4 a1kok5 a1kok6 a1kok7 a1kok8[1,] 2558.68 1724.41 2309.08 2758.44 2632.43 1955.33 3191.78[2,] 1943.40 1100.10 1233.33 2337.75 1630.91 1422.45 1961.50[3,] 2404.77 3011.42 3708.85 2830.40 3030.90 2809.82 2482.39[4,] 583.08 873.06 737.57 834.58 887.97 827.38 890.58[5,] 536.43 656.76 593.17 679.73 756.44 572.64 1233.456379 more rows ...

What does RG contain

$Rb a1koc1 a1koc2 a1koc3 a1koc4 a1koc5 a1koc6 a1koc7 a1koc8 a1kok1 a1kok2[1,] 1418.50 1532.00 992.00 1306.75 781.89 1165 761.88 1151.00 1098.86 941.74[2,] 1280.05 1497.00 980.00 1328.00 773.00 1165 759.17 1151.00 994.43 934.00[3,] 1216.00 1481.63 935.00 1348.61 773.00 1198 758.00 1129.05 949.39 935.84[4,] 1193.69 1467.42 973.26 1341.55 760.00 1198 752.53 1077.34 949.00 911.09[5,] 1148.12 1442.00 929.43 1376.21 760.00 1177 710.79 1075.00 920.16 898.00 a1kok3 a1kok4 a1kok5 a1kok6 a1kok7 a1kok8[1,] 1042.00 954.00 930.00 987.57 1190.83 1073.44[2,] 1042.00 952.22 930.00 933.09 1158.00 1074.62[3,] 1042.00 904.63 930.30 919.70 1150.18 1077.00[4,] 1037.75 899.89 914.79 911.14 1179.75 1077.00[5,] 1023.15 898.00 892.76 882.02 1069.02 1064.046379 more rows ...

What does RG contain

$Gb a1koc1 a1koc2 a1koc3 a1koc4 a1koc5 a1koc6 a1koc7 a1koc8 a1kok1 a1kok2[1,] 663.50 520.00 371.54 563.25 228.00 274.05 354.50 292.00 224.00 239.95[2,] 643.43 520.00 355.00 589.44 231.00 268.00 324.61 286.93 206.86 239.00[3,] 544.81 498.63 317.55 619.58 231.00 267.00 281.66 264.00 181.00 239.00[4,] 522.80 454.55 356.46 615.76 218.37 267.00 289.30 264.00 181.00 248.18[5,] 465.27 433.00 295.67 567.81 203.00 267.00 238.42 257.82 174.57 229.10 a1kok3 a1kok4 a1kok5 a1kok6 a1kok7 a1kok8[1,] 282.53 254.00 321.28 363.00 285.00 309.00[2,] 264.24 254.00 309.00 336.00 285.00 309.00[3,] 250.06 254.00 305.00 336.00 285.00 309.86[4,] 233.66 244.56 297.75 335.46 286.78 351.78[5,] 231.00 231.05 298.74 334.22 274.44 358.926379 more rows ...

What does RG contain

$genes GridROW GridCOL ROW COL NAME TYPE CLID ACC1 1 1 1 1 Cy3RT Control BLANK BLANK2 1 1 1 2 Cy5RT Control BLANK BLANK3 1 1 1 3 mSRB1 cDNA mSRB1 mSRB14 1 1 1 4 BLANK BLANK BLANK BLANK5 1 1 1 5 BLANK BLANK BLANK BLANK6379 more rows ...

$targets FileName Cy3 Cy5c1 a1koc1.spot Pool C57BL/6c2 a1koc2.spot Pool C57BL/6c3 a1koc3.spot Pool C57BL/6c4 a1koc4.spot Pool C57BL/6c5 a1koc5.spot Pool C57BL/611 more rows ...

Exercise

> dim(RG)[1] 6384 16> ncol(RG)[1] 16> colnames(RG) [1] "a1koc1" "a1koc2" "a1koc3"

"a1koc4""a1koc5" "a1koc6" "a1koc7" "a1koc8"

[9] "a1kok1" "a1kok2" "a1kok3" "a1kok4""a1kok5" "a1kok6" "a1kok7" "a1kok8"

subsets

> RG[1:2,]An object of class "RGList"$R a1koc1 a1koc2 a1koc3 a1koc4 a1koc5 a1koc6 a1koc7 a1koc8 a1kok1[1,] 4184.08 3300.22 2432.54 2069.81 2809.83 2029.05 1720.56 1795.58 1845.97[2,] 4148.48 3774.18 2579.92 2566.33 2286.43 2244.33 1987.83 1908.33 2924.68 a1kok2 a1kok3 a1kok4 a1kok5 a1kok6 a1kok7 a1kok8[1,] 2330.53 2630.76 2234.17 2811.72 2721.10 2361.67 2585.89[2,] 3027.00 2411.81 2023.39 4148.58 3384.13 2763.00 2775.44

$G a1koc1 a1koc2 a1koc3 a1koc4 a1koc5 a1koc6 a1koc7 a1koc8 a1kok1[1,] 6256.08 5269.89 4080.85 3229.38 3763.22 2773.58 2800.44 1923.58 804.80[2,] 5389.81 3608.12 2614.42 2107.56 1878.30 1926.00 1711.17 1205.20 1385.96 a1kok2 a1kok3 a1kok4 a1kok5 a1kok6 a1kok7 a1kok8[1,] 2558.68 1724.41 2309.08 2758.44 2632.43 1955.33 3191.78[2,] 1943.40 1100.10 1233.33 2337.75 1630.91 1422.45 1961.50

Background correction

RG.b <- backgroundCorrect(RG,method="minimum")

Loess normalization >plotDensities(MA.p)

Quantile Normalization

> MA.pAq <- normalizeBetweenArrays(MA.p, method="Aquantile")

> plotDensities(MA.pAq)

Print-tip loess normalization

> MA <- normalizeWithinArrays(RG)

Generate factors

> design <- cbind("WT-Ref"=1,"KO-WT"=rep(0:1,c(8,8)))> design WT-Ref KO-WT [1,] 1 0 [2,] 1 0 [3,] 1 0 [4,] 1 0 [5,] 1 0 [6,] 1 0 [7,] 1 0 [8,] 1 0 [9,] 1 1[10,] 1 1[11,] 1 1[12,] 1 1[13,] 1 1[14,] 1 1[15,] 1 1[16,] 1 1

Fit a linear model

> fit <- lmFit(MA,design=design)> fitAn object of class "MArrayLM"$coefficients WT-Ref KO-WT[1,] -0.65953808 0.63931974[2,] 0.22938847 0.65516034[3,] -0.25176365 0.33421048[4,] -0.05169672 0.04049065[5,] -0.25006850 0.223021296379 more rows ...

Ebayes fit

fit <- eBayes(fit)

Significance

$p.value WT-Ref KO-WT [1,] 0.0005273336 0.009821223 [2,] 0.0840905898 0.001674820 [3,] 0.2514756923 0.280677844 [4,] 0.5306727045 0.727393168 [5,] 0.0140994442 0.103570036 6379 more rows ...

$lods WT-Ref KO-WT [1,] -0.1466897 -2.581616 [2,] -4.8450256 -1.065213 [3,] -5.7239418 -5.260559 [4,] -6.2106950 -5.777607 [5,] -3.2510578 -4.528469 6379 more rows ...

$F [1] 8.8907738 26.5502551 0.7775771 0.2140163 3.7423046 6379 more elements ...

$F.p.value [1] 2.085929e-03 4.429307e-06 4.744446e-01 8.093730e-01 4.388436e-02 6379 more elements ...

Top table> topTable(fit,coef="KO-WT",adjust="fdr") GridROW GridCOL ROW COL NAME TYPE CLID ACC2149 2 2 8 7 ApoAI,lipid-Img cDNA 1077520 540 1 2 7 15 EST,HighlysimilartoA cDNA 439353 5356 4 2 9 1 CATECHOLO-METHYLTRAN cDNA 1350232 4139 3 3 8 2 EST,WeaklysimilartoC cDNA 374370 1739 2 1 7 17 ApoCIII,lipid-Img cDNA 483614 2537 2 3 7 17 ESTs,Highlysimilarto cDNA 483614 1496 1 4 15 5 est cDNA 484183 genome.wustl4941 4 1 8 6 similartoyeaststerol cDNA 737183 947 1 3 8 2 EST,WeaklysimilartoF cDNA 353292 5604 4 3 1 18 cDNA 317638 M A t P.Value B2149 -3.1661645 12.46803 -23.980007 3.045649e-11 14.9266311540 -3.0485504 12.28064 -12.962085 5.020967e-07 10.81334215356 -1.8481659 12.92660 -12.440765 6.505619e-07 10.44793754139 -1.0269537 12.60572 -11.756162 1.209061e-06 9.92852801739 -0.9325824 13.73744 -9.835355 1.563760e-05 8.19240992537 -1.0098117 13.63012 -9.015243 4.222433e-05 7.30489941496 -0.9774236 12.22891 -9.002324 4.222433e-05 7.29013364941 -0.9549693 13.28750 -7.441333 5.617334e-04 5.3106653947 -0.5705900 10.54291 -4.554709 1.768067e-01 0.56291495604 -0.3663573 12.71267 -3.962815 5.290409e-01 -0.5532768

MA plots

> plotMA(fit, 2)

Venn diagram

> results <- decideTests(fit)

> vennDiagram(results)

Switch function

center = function(x, type) {

switch(type,

mean = mean(x),

median = median(x),

trimmed = mean(x, trim = .1))

}

Switch function

The switch function uses the value of its first argument to determine which of its remaining arguments to evaluate and return. The first argument can be either an integer index, or a character string to be used in matching one of the following arguments.

Apply function

For example,

> apply(x, 2, mean) computes the column means of a

matrix x, while

> apply(x, 1, median) computes the row medians.