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The BioConnector Wiki is a standards compliant, simple to use wiki, mainly aimed at creating documentation. It's simple but powerful [[https:// | The BioConnector Wiki is a standards compliant, simple to use wiki, mainly aimed at creating documentation. It's simple but powerful [[https:// | ||
- | Interested in documenting your group' | + | Bioinformatics core uses this Wiki page to create a project-specific page and communicate with clients. |
+ | |||
+ | Interested in documenting your group' | ||
- | ====== Example Entry ====== | ||
- | The example entry below was originally part of a [[http:// | ||
- | |||
- | ===== Pathway Analysis: Gene expression and colon cancer susceptibility ===== | ||
- | |||
- | Data and background from: Hong Y, Ho KS, Eu KW, Cheah PY. A susceptibility gene set for early onset colorectal cancer that integrates diverse signaling pathways: implication for tumorigenesis. //Clin Cancer Res// 2007 Feb 15; | ||
- | |||
- | ==== Background ==== | ||
- | |||
- | Causative genes for autosomal dominantly inherited familial adenomatous polyposis (FAP) and hereditary non-polyposis colorectal cancer (HNPCC) have been well characterized. There is, however, another 10-15 % early onset colorectal cancer (CRC) whose genetic components are currently unknown. In this study, we used DNA chip technology to systematically search for genes differentially expressed in early onset CRC. | ||
- | |||
- | ==== Methods ==== | ||
- | |||
- | === Overall design === | ||
- | |||
- | RNA extracted from colonic mucosa of healthy controls(10samples) and patients(12samples) were analyzed using GeneChip U133-Plus 2.0 Array. Patients and controls were age- (50 or less), ethnicity- (Chinese) and tissue-matched. T-test, hierarchical clustering, mean fold-change and principal component analysis were used to identify genes that differentiate between patients and controls. These were subsequently verified by real-time polymerase chain reaction (PCR) technology. Signaling Pathway Impact Analysis was used to perform a systems biology pathway analysis. | ||
- | |||
- | === R code === | ||
- | |||
- | <code rsplus examplecode.R> | ||
- | # These are bioconductor packages. See http:// | ||
- | library(Biobase) | ||
- | library(GEOquery) | ||
- | library(limma) | ||
- | library(SPIA) | ||
- | library(hgu133plus2.db) | ||
- | |||
- | # load series and platform data from GEO: | ||
- | # http:// | ||
- | eset <- getGEO(" | ||
- | |||
- | # log transform | ||
- | exprs(eset) <- log2(exprs(eset)) | ||
- | |||
- | # set up a design matrix and contrast matrix | ||
- | design <- model.matrix(~0+as.factor(c(rep(1, | ||
- | colnames(design) <- c(" | ||
- | contrast.matrix <- makeContrasts(cancer_v_normal=cancer-normal, | ||
- | |||
- | # run the analysis with empirical Bayes moderated standard errors | ||
- | fit <- lmFit(eset, | ||
- | fit2 <- contrasts.fit(fit, | ||
- | fit2 <- eBayes(fit2) | ||
- | |||
- | |||
- | # get useful information for the top 25 genes | ||
- | top <- topTable(fit2, | ||
- | top <- na.omit(subset(top, | ||
- | top$ID <- as.character(top$ID) | ||
- | |||
- | # annotate with entrez info | ||
- | top$ENTREZ< | ||
- | top< | ||
- | top< | ||
- | top$SYMBOL< | ||
- | top< | ||
- | top< | ||
- | |||
- | # significant genes is a vector of fold changes where the names | ||
- | # are ENTREZ gene IDs. The background set is a vector of all the | ||
- | # genes represented on the platform. | ||
- | sig_genes <- subset(top, adj.P.Val< | ||
- | names(sig_genes) <- subset(top, adj.P.Val< | ||
- | all_genes <- top$ENTREZ | ||
- | |||
- | # run SPIA. | ||
- | spia_result <- spia(de=sig_genes, | ||
- | |||
- | # Once you start running SPIA you'll see it go through all the KEGG pathways | ||
- | # for your organism. This will take a few minutes! Be patient. | ||
- | # Done pathway 1 : RNA transport.. | ||
- | # Done pathway 2 : RNA degradation.. | ||
- | # Done pathway 3 : PPAR signaling pathway.. | ||
- | # Done pathway 4 : Fanconi anemia pathway.. | ||
- | # Done pathway 5 : MAPK signaling pathway.. | ||
- | # Done pathway 6 : ErbB signaling pathway.. | ||
- | # Done pathway 7 : Calcium signaling pathway.. | ||
- | # Done pathway 8 : Cytokine-cytokine receptor int.. | ||
- | # Done pathway 9 : Chemokine signaling pathway.. | ||
- | # Done pathway 10 : Neuroactive ligand-receptor in.. | ||
- | |||
- | head(spia_result) | ||
- | plotP(spia_result, | ||
- | </ | ||
- | |||
- | ==== Results ==== | ||
- | |||
- | The output from Signaling Pathway Impact Analysis is a list of pathways, whether they' | ||
- | |||
- | Limitations: |
start.txt · Last modified: 2020/10/12 15:01 by pk7z