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Affymetrix GeneChip technology is used to detect gene expression levels in samples of cells under different conditions. In this project, we analyzed the gene expression profiling data for mouse induced pluripotent stem cell (iPSCs) (Takahashi, 2006) on Affymetrix Mouse 430 2.0 GeneChip. Three biological conditions were present: p53KO, microRNA mir34aKO, and wild type, each with three biological replicates. The first part was devoted to identifying differentially expressed genes from around 45,000 of them, and looking into their biological meanings by pathway analysis. The second part dealt with repetitive elements represented in the pool of mRNAs. We identified repetitive elements that show a significant difference between two biological conditions. Both the comparison of p53KO versus WT and mir34aKO versus WT were done. However, the emphasis was on the former. Laboratory validation with qPCR confirmed our findings. This work was done under the Overseas Research Fellowship (ORF) Scheme 2012 for Science Students by the Faculty of Science, The University of Hong Kong. Many thanks are due to the University for the fellowship, and to Professors Terry Speed and Lin He and Drs Chao-po Lin and Anne Biton of the University of California at Berkeley for their supervision and generous support.
Stratifying cancer patients based on their gene expression levels allows improving diagnosis, survival analysis and treatment planning. However, such data is extremely highly dimensional as it contains expression values for over 20000 genes per patie
Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequentl
We analyze publicly available data on Affymetrix microarrays spike-in experiments on the human HGU133 chipset in which sequences are added in solution at known concentrations. The spike-in set contains sequences of bacterial, human and artificial ori
Linear discrimination, from the point of view of numerical linear algebra, can be treated as solving an ill-posed system of linear equations. In order to generate a solution that is robust in the presence of noise, these problems require regularizati
We train a neural network to predict chemical toxicity based on gene expression data. The input to the network is a full expression profile collected either in vitro from cultured cells or in vivo from live animals. The output is a set of fine graine