Linked Life Data

17906332

Source:http://linkedlifedata.com/resource/pubmed/id/17906332

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2007-11-26
pubmed:abstractText
This article describes specific procedures for conducting quality assessment of Affymetrix GeneChip(R) soybean genome data and for performing analyses to determine differential gene expression using the open-source R programming environment in conjunction with the open-source Bioconductor software. We describe procedures for extracting those Affymetrix probe set IDs related specifically to the soybean genome on the Affymetrix soybean chip and demonstrate the use of exploratory plots including images of raw probe-level data, boxplots, density plots and M versus A plots. RNA degradation and recommended procedures from Affymetrix for quality control are discussed. An appropriate probe-level model provides an excellent quality assessment tool. To demonstrate this, we discuss and display chip pseudo-images of weights, residuals and signed residuals and additional probe-level modeling plots that may be used to identify aberrant chips. The Robust Multichip Averaging (RMA) procedure was used for background correction, normalization and summarization of the AffyBatch probe-level data to obtain expression level data and to discover differentially expressed genes. Examples of boxplots and MA plots are presented for the expression level data. Volcano plots and heatmaps are used to demonstrate the use of (log) fold changes in conjunction with ordinary and moderated t-statistics for determining interesting genes. We show, with real data, how implementation of functions in R and Bioconductor successfully identified differentially expressed genes that may play a role in soybean resistance to a fungal pathogen, Phakopsora pachyrhizi. Complete source code for performing all quality assessment and statistical procedures may be downloaded from our web source: http://css.ncifcrf.gov/services/download/MicroarraySoybean.zip.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1477-4054
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
8
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
415-31
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
A microarray analysis for differential gene expression in the soybean genome using Bioconductor and R.
pubmed:affiliation
Statistical Consulting Services, Data Management Services, Inc. (DMS), National Cancer Institute at Frederick (NCI-Frederick), PO Box B, Frederick, MD 21702-1201, USA. gwa@css.ncifcrf.gov
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural