Affy¶
Mutations in DNA that is coding for proteins (genes) or regulatory elements are often the cause of most diseases. Affymatirx arrays allow quantification of specific sequences of DNA or RNA in biological samples such as blood, tissue, or tumors. These arrays give the relative levels of specific transcript (between two samples) so that these can be compared (between eg. healty vs diseased tissues or before vs after treatment).
To do so, DNA sequences unique to specific genes/mutations are printed as small spots on a glass surface (arrays), with each spot containing thousends of copies of a single sequence. The DNA from each sample is than tagged with different colors of flourecent molecules (red or green) and hybredized with the array. This way, DNA from sample can bind to sequences on the surface that are antisense of its own sequence (specific binding). The array undergoes several washing steps to remove the none-specifically bound DNA sequences, which bind loosly. The plate is than scanned and the flourecence intensity for each color at each spot is recorded. This intensity is an indicator of the ammount of DNA bound and probably the levels of that specific DNA present in the sample. The information regarding the specific sequence printed on each spot, control spots, and other array specific information is stored in a CDF format/file. By comparing the intensity of red to green at each spot you can know which sample contained more of that specific transcript.
This workflow is provided by ArrayAnalysis.org and its code has been merged into single R script. It performs array normalization and differential expression analysis, and plots the important Quality Control plots. Dataset used in this workflow is from a study by Ramsey JE et al 2013 in which the effects of presence/absence of ZXDC gene before and during differentiation of a white blood cell type is studied. The corresponding data can be downloaded from Gene Expression Omnibus repository using accession number GSE45417. For more information about this workflow please visit ArrayAnalysis.org.
![digraph AFFY_diagram {
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node [fontname="serif",
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subgraph "cluster0" {
label="Raw data QC graphs";
edge [comment="Wildcard node added automatic in EG."];
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"c0_rawData" [shape="invhouse",
label="rawData",
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"c0_plot" [shape="box",
label="plot"];
"c0_rawData" -> "samplePrepPlot()";
"samplePrepPlot()" -> "c0_plot";
"c0_rawData" -> "ratioPlot()";
"ratioPlot()" -> "c0_plot";
"c0_rawData" -> "RNAdegPlot()";
"RNAdegPlot()" -> "c0_plot";
}
subgraph "cluster1" {
label="Spike-in Controls";
edge [comment="Wildcard node added automatic in EG."];
node [comment="Wildcard node added automatic in EG."];
"c0_plot" -> "c1_rawData" [style="bold",
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"c1_rawData" [shape="invhouse",
label="rawData",
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"c1_plot" [shape="box",
label="plot"];
"c1_wt" [label="write.table()"];
"c1_pmacall" [label="deduceSpecies() \n computePMAtable()"];
"c1_rawData" -> "hybridPlot()";
"hybridPlot()" -> "c1_plot";
"c1_rawData" -> "backgroundPlot()";
"backgroundPlot()" -> "c1_plot";
"c1_rawData" -> "percPresPlot()";
"percPresPlot()" -> "c1_plot";
"c1_rawData" -> "PNdistrPlot()";
"PNdistrPlot()" -> "c1_plot";
"c1_rawData" -> "controlPlots()";
"controlPlots()" -> "c1_plot";
"c1_rawData" -> "c1_pmacall";
"c1_pmacall" -> "c1_plot";
"c1_pmacall" -> "c1_wt";
}
subgraph "cluster2" {
label="Scale factor";
edge [comment="Wildcard node added automatic in EG."];
node [comment="Wildcard node added automatic in EG."];
"c1_plot" -> "c2_rawData" [ltail="cluster1",
penwidth="5.0",
lhead="cluster2"];
"c2_rawData" [shape="invhouse",
label="rawData",
colorscheme="ylorrd3"];
"c2_plot" [shape="box",
label="plot"];
"c2_rawData.pset" [label="rawData.pset"];
"c2_rawData" -> "scaleFactPlot()";
"scaleFactPlot()" -> "c2_plot";
"c2_rawData" -> "boxplotFun()";
"boxplotFun()" -> "c2_plot";
"c2_rawData" -> "densityFun()";
"densityFun()" -> "c2_plot";
"c2_rawData" -> "maFun()";
"maFun()" -> "c2_plot";
"c2_rawData" -> "plotArrayLayout()";
"plotArrayLayout()" -> "c2_plot";
"c2_rawData" -> "PNposPlot()";
"PNposPlot()" -> "c2_plot";
"c2_rawData" -> "fitPLM()";
"fitPLM()" -> "c2_rawData.pset";
"c2_rawData.pset" -> "spatialImages()";
"c2_rawData" -> "spatialImages()";
"spatialImages()" -> "c2_plot";
"c2_rawData" -> "array.image()";
"array.image()" -> "c2_plot";
"c2_rawData" -> "nuseFun()";
"nuseFun()" -> "c2_plot";
"c2_rawData" -> "rleFun()";
"rleFun()" -> "c2_plot";
}
subgraph "cluster3" {
label="Correlation Plot";
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"c2_plot" -> "c3_rawData" [ltail="cluster2",
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"c3_rawData" [shape="invhouse",
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"c3_plot" [shape="box",
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"c3_rawData" -> "c3_correlFun()";
"c3_correlFun()" -> "c3_plot";
"c3_pcaFun()" [label="pcaFun()"];
"c3_rawData" -> "c3_pcaFun()";
"c3_pcaFun()" -> "c3_plot";
"c3_clusterFun()" [label="clusterFun()"];
"c3_rawData" -> "c3_clusterFun()";
"c3_clusterFun()" -> "c3_plot";
}
subgraph "cluster4" {
label="Preprocessing";
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"c3_plot" -> "c4_rawData" [ltail="cluster3",
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lhead="cluster4"];
"c4_rawData" [shape="invhouse",
label="rawData",
colorscheme="ylorrd3"];
"c4_plot" [shape="box",
label="plot"];
"c4_png" [label="png"];
"c4_rawData" -> "deduceSpecies()";
"deduceSpecies()" -> "c4_plot";
"c4_rawData" -> "normalizeData()";
"normalizeData()" -> "c4_plot";
}
subgraph "cluster5" {
label="Preprocessing Evaluation";
edge [comment="Wildcard node added automatic in EG."];
node [comment="Wildcard node added automatic in EG."];
"c4_plot" -> "c5_rawData" [ltail="cluster4",
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lhead="cluster5"];
"c5_rawData" [shape="invhouse",
label="rawData",
colorscheme="ylorrd3"];
"c5_plot" [shape="box",
label="plot"];
"c5_boxplotFun()" [label="boxplotFun()"];
"c5_rawData" -> "c5_boxplotFun()";
"c5_boxplotFun()" -> "c5_plot";
"c5_densityFun()" [label="densityFun()"];
"c5_rawData" -> "c5_densityFun()";
"c5_densityFun()" -> "c5_plot";
"c5_maFun()" [label="maFun()"];
"c5_rawData" -> "c5_maFun()";
"c5_maFun()" -> "c5_plot";
"c5_rawData" -> "correlFun()";
"correlFun()" -> "c5_plot";
"c5_rawData" -> "pcaFun()";
"pcaFun()" -> "c5_plot";
"c5_rawData" -> "clusterFun()";
"clusterFun()" -> "c5_plot";
}
subgraph "cluster6" {
label="Prepare output table";
edge [comment="Wildcard node added automatic in EG."];
node [comment="Wildcard node added automatic in EG."];
"c5_plot" -> "c6_rawData" [ltail="cluster5",
penwidth="5.0",
lhead="cluster6"];
"c6_rawData" [shape="invhouse",
label="rawData",
colorscheme="ylorrd3"];
"c6_wt" [label="write.table()"];
"c6_rawData" -> "createNormDataTable()";
"createNormDataTable()" -> "c6_wt";
}
}](../../_images/graphviz-bbef3973f1665edbc9ae10a506b8b6735983173c.png)
Diagram of affy analysis workflow.
Packages and Dependencies¶
There are 12 packages (mainly affymatrix array analysis related) used in this workflow, which depend on 28 additional packages from CRAN and Bioconductor (dependencies)
Used packages:
- Bioconductor: ArrayTools, affy, affycomp, affyPLM, affypdnn, bioDist, simpleaffy, affyQCReport, plier, yaqcaffy
- CRAN: gdata, gplots
Package dependencies:
- Bioconductor: limma, Biobase, BiocInstaller, BiocGenerics, zlibbioc, preprocessCore, affyio, gcrma, Biostrings, XVector, S4Vectors, IRanges, genefilter, AnnotationDbi, annotate, GenomeInfoDb, affyPLM
- CRAN: xtable, KernSmooth, DBI, XML, lattice, RSQLite, RColorBrewer, caTools, bitops, gtools, survival
License¶
- Copyright (c) 2015 Arrayanalysis.org based on code from http://www.arrayanalysis.org/affyQC/doc_affyQC_R.php
- Copyright (c) 2015-2016 BeDataDriven B.V. License: Apache License version 2.0 or higher