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<h1>GU- Blood Brain Analysis</h1>
<p>Array analysis for Ben Andreone (<a href="mailto:[email protected]">[email protected]</a>) at Neurobiology at HMS. Contact Meeta Mistry (<a href="mailto:[email protected]">[email protected]</a>) for additional details. Request from client was:</p>
<p>Revisit array data comparing gene expression in endothelial cells purified from brain and lung, looking for genes that may be important in the development of the blood brain barrier.</p>
<p>Also, compare the gene expression levels to those from other endothelial cell microarray data sets published in GEO that use the same platform (Affymetrix 430 2.0)</p>
<h1>Bioconductor and R libraries used</h1>
<pre><code class="r">
library(affy)
library(arrayQualityMetrics)
library(RColorBrewer)
library(simpleaffy)
library(limma)
library(mouse430a2.db)
library(pheatmap)
library(ape)
library(statmod)
library(xtable)
library(plyr)
library(ggplot2)
library(gplots)
</code></pre>
<h3>Get variables</h3>
<ul>
<li>get base directory for analyses</li>
<li>specify data and results directories</li>
<li>specify column headers used in metadata file</li>
</ul>
<pre><code class="r">baseDir = getwd()
dataDir = paste(baseDir, "/data", sep = "")
resultsDir = paste(baseDir, "/results", sep = "")
groupid_column_header1 = "Tissue"
groupid_column_header2 = "SampleNumber"
</code></pre>
<p>Phenotype data was cleaned up with Google Refine (the Refine project can be downloaded at <a href="./results/meta/Gu-dChip_array_summary.google-refine.tar.gz">this link</a> including a history of all data transformation steps). One row was deleted after the use of Refine</p>
<h2>Load in phenotype data and CEL files</h2>
<pre><code class="r">mic.data <- read.affy("covars.desc", path = dataDir, verbose = T)
</code></pre>
<pre><code>## 1 reading /home/mistrm82/R/Gu_bloodbrain/data/CG2011101819.CEL ...instantiating an AffyBatch (intensity a 1004004x8 matrix)...done.
## Reading in : /home/mistrm82/R/Gu_bloodbrain/data/CG2011101819.CEL
## Reading in : /home/mistrm82/R/Gu_bloodbrain/data/CG2011101820.CEL
## Reading in : /home/mistrm82/R/Gu_bloodbrain/data/CG2011110401.CEL
## Reading in : /home/mistrm82/R/Gu_bloodbrain/data/CG2011110402.CEL
## Reading in : /home/mistrm82/R/Gu_bloodbrain/data/CG2011110403.CEL
## Reading in : /home/mistrm82/R/Gu_bloodbrain/data/CG2011110404.CEL
## Reading in : /home/mistrm82/R/Gu_bloodbrain/data/CG2011110405.CEL
## Reading in : /home/mistrm82/R/Gu_bloodbrain/data/CG2011110406.CEL
</code></pre>
<h2>QC report on raw data</h2>
<p><a href="./results/report_raw/index.html">Raw QC</a></p>
<h2>Background Correct and Normalize using RMA normalization</h2>
<pre><code class="r">mic.edata <- call.exprs(mic.data, "rma")
</code></pre>
<pre><code>## Background correcting
## Normalizing
## Calculating Expression
</code></pre>
<pre><code class="r">
# Extract expression data and factor pheno data
allData <- exprs(mic.edata)
colnames(allData) <- rownames(pData(mic.edata))
pData(mic.edata)[, groupid_column_header1] <- factor(pData(mic.edata)[, groupid_column_header1])
pData(mic.edata)[, groupid_column_header2] <- factor(pData(mic.edata)[, groupid_column_header2])
</code></pre>
<h2>QC Analysis</h2>
<p>Using different methods to identfy outliers - each module can take the raw data or the normalized data.</p>
<pre><code class="r">
# Prepare data for AQM
preparedData = prepdata(expressionset = mic.edata, intgroup = c(groupid_column_header1,
groupid_column_header2), do.logtransform = F)
preparedAffy = prepaffy(expressionset = mic.data, preparedData)
</code></pre>
<p>We can compute the number of outliers found from the different methods outlined in the QC report link above.</p>
<pre><code class="r">
# Relative Log Expression plots
rle <- aqm.rle(preparedAffy)
rle.out <- rle@outliers@which
if (length(rle.out) > 0) rleoutliers <- names(rle.out)
# Normalized Unscaled Standard Error plots
nuse <- aqm.nuse(preparedAffy)
nuse.out <- nuse@outliers@which
if (length(nuse.out) > 0) nuseoutliers <- names(nuse.out)
</code></pre>
<p>Above, we have RLE and NUSE plots. These plots.. Based on these QC methods we identify a total of 0 outliers. Next, we use other QC methods for which the input can be either raw data or normalized data, including boxplots, MA plots and a distance heatmap.</p>
<pre><code class="r">
# Distance between arrays using heatmap
hm <- aqm.heatmap(preparedAffy)
hm.out <- hm@outliers@which
if (length(hm.out) > 0) hmoutliers <- names(hm.out) #sample names of outliers
# Boxplots
bo <- aqm.boxplot(preparedAffy, subsample = 10000, outlierMethod = "KS")
bo.out <- bo@outliers@which
if (length(bo.out) > 0) boutliers <- names(bo.out)
# MA plots
ma <- aqm.maplot(preparedAffy)
ma.out <- ma@outliers@which
if (length(ma.out) > 0) moutliers <- names(ma.out)
</code></pre>
<p>From these methods we find the data is generally good with 0 ouliers found. The heatmap below illustrates this.The color scale is chosen to cover the range of distances encountered in the dataset. Patterns in this plot indicate that the arrays cluster better by tissue type rather than by sample number.</p>
<p><img src="data:image/png;base64,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" title="plot of chunk plot heatmap" alt="plot of chunk plot heatmap" width="800px" style="display: block; margin: auto;" /></p>
<h2>PCA</h2>
<p>Plot all pairwise combinations of the first 5 principal components for the samples
The more similar the samples are, the closer they will cluster in these plots. In the frst one below tissue type is coded by color.</p>
<p><img 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" title="plot of chunk calculate_PCA" alt="plot of chunk calculate_PCA" width="800px" style="display: block; margin: auto;" /></p>
<p>In the second plot the color coding represents the sample number. For each mouse tissue from the cortex and lung was assayed.</p>
<p><img 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" title="plot of chunk calculate_PCA2" alt="plot of chunk calculate_PCA2" width="800px" style="display: block; margin: auto;" /></p>
<p>In this last plot we investigate whether samples are clustering based on average intensity. Intensity is illustrated using a color scale from red (high intensity) to light pink (low intensity). </p>
<p><img 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" title="plot of chunk calculate_PCA3" alt="plot of chunk calculate_PCA3" width="800px" style="display: block; margin: auto;" /></p>
<p>From the PCA plots we see that the first two principal components (along which we see the largest variation in the data) represent tissue differences between samples.We also see PC3 somewhat explained by difference in Sample Number.Its best to use a paired design in limma. </p>
<h2>Differential Expression Analysis</h2>
<pre><code class="r">
pData(mic.edata) <- pData(mic.edata)[with(pData(mic.edata), order(Tissue, SampleNumber)),
]
newOrder <- match(rownames(pData(mic.edata)), colnames(exprs(mic.edata)))
exprs(mic.edata) <- exprs(mic.edata)[, as.vector(newOrder)]
</code></pre>
<pre><code class="r">
samples <- cbind(c(1, rep(0, 3), 1, rep(0, 3)), c(0, 1, rep(0, 2), 0, 1, rep(0,
2)), c(rep(0, 2), 1, 0, rep(0, 2), 1, 0), c(rep(0, 3), 1, rep(0, 3), 1))
tissue <- cbind(c(rep(1, 4), rep(0, 4)), c(rep(0, 4), rep(1, 4)))
contrast.matrix <- data.frame(cbind(tissue, samples))
colnames(contrast.matrix) <- c("Cortex", "Lung", "Sample18", "Sample19", "Sample22",
"Sample72")
mod <- model.matrix(~-1 + Tissue + SampleNumber, pData(mic.edata))
# Fit a linear model
fit <- lmFit(mic.edata, mod)
# Compute estimated coefficients and standard errors for contrasts
contrasts <- makeContrasts(TissueCortex - TissueLung, levels = mod)
fit2 <- contrasts.fit(fit, contrasts)
fit2 <- eBayes(fit2)
</code></pre>
<p>From this fit, we find 277 genes to be differentially expressed between Cortex and Lung. Threshold applied is fold change greater than 2 AND adjusted p-value < 0.001.</p>
<pre><code class="r">gene_list <- topTable(fit2, coef = 1, number = nrow(exprs(mic.edata)), sort.by = "logFC")
# P-value Distribution
hist(gene_list$P.Value, col = "darkgrey", border = F, xlab = "P-value", main = "P-value Distribution")
</code></pre>
<p><img 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title="plot of chunk histplot" alt="plot of chunk histplot" width="800px" style="display: block; margin: auto;" /></p>
<pre><code class="r"># Highlight genes Bonferroni
gene_list$threshold.B = as.factor(abs(gene_list$logFC) > 2 & gene_list$P.Value <
0.05/nrow(gene_list))
# FDR
gene_list$threshold.FDR = as.factor(abs(gene_list$logFC) > 2 & gene_list$adj.P.Val <
0.001)
## Construct the plot object
ggplot(data = gene_list, aes(x = logFC, y = -log10(P.Value), colour = threshold.FDR)) +
geom_point(alpha = 0.4, size = 1.75) + opts(legend.position = "none") +
xlim(c(-10, 10)) + ylim(c(0, 15)) + xlab("log2 fold change") + ylab("-log10 p-value")
</code></pre>
<p><img 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" title="plot of chunk volcanoplot" alt="plot of chunk volcanoplot" width="800px" style="display: block; margin: auto;" /></p>
<pre><code class="r">
top <- gene_list[which(as.logical(gene_list$threshold.FDR)), ]
</code></pre>
<p>Plot a heat map of the top 277 significant genes to see changes in gene expression.</p>
<pre><code class="r">top.edata <- exprs(mic.edata)[rownames(top), ]
heatmap.2(top.edata, dendrogram = "none", trace = "none", col = greenred(10),
labRow = "", labCol = pData(mic.edata)$Tissue)
</code></pre>
<p><img 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title="plot of chunk heatmap" alt="plot of chunk heatmap" width="800px" style="display: block; margin: auto;" /></p>
<p>The Gu lab previously identfied a top list of 38 probes. None of these probes appear in our top list, although if we reduce the stringency by increasing p-value cutoff to padj < 0.01 we get 15 of their probes. A heatmap of the 38 probes depicted below. </p>
<pre><code class="r">gu.genes <- read.delim("Gu_orginal_list.csv", header = T, sep = "\t", row.names = 1,
as.is = T)
length(which(rownames(gu.genes) %in% row.names(top)))
</code></pre>
<pre><code>## [1] 0
</code></pre>
<pre><code class="r">
gu.edata <- exprs(mic.edata)[rownames(gu.genes), ]
heatmap.2(gu.edata, dendrogram = "none", trace = "none", col = greenred(10),
labRow = "", labCol = pData(mic.edata)$Tissue)
</code></pre>
<p><img 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title="plot of chunk compare" alt="plot of chunk compare" width="800px" style="display: block; margin: auto;" /></p>
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