Dec 18, 2014
Thu, Dec 18, 2014 at 5:37 pm
Customer: We have a microarray study with longitudinal blood samples from each of 10 children who underwent heart transplantation. Each patient had gene expression profile before transplantation and then with each myocardial biopsy. Grade of rejection was obtained from myocardial biopsy (0, 1, 2). The purpose of the project to see, if gene expression profile could be correlated with the grade of rejection.
Fri, Dec 19, 2014 at 10:59 AM
AccuraScience LB: Do I understand it correctly that myocardial biopsy - and therefore the rejection grading evaluation - was performed exactly once for each child after the transplantation?
Generally, the analysis work could include the following components: (1) Data processing: the data from all 20 (or more) microarrays will be processed together and normalized (so that meaningful comparison can be made across them). Genes whose expression differs between at least 2 of the samples will be retained, and other genes eliminated for further analysis. (2) Principal component analysis (PCA) will be performed for the 20 (or more) samples. This analysis projects the expression profile of each sample to a 2-dimensional PC space. We will examine (a) before transplantation, whether some of the 10 children's gene expression profiles are grouped together (that is, their expression profiles are similar to each other than from that of other children), (b) Similarly, whether some of the samples are grouped together after transplantation, and (c) whether there are recognizable patterns of in the trajectories of changes from the pre-transplantation to the post-transplantation state. Because there are only 10 individuals, it would be easy to see the correspondence between the rejection grade (0, 1, and 2) and the grouping (both pre- and post-transplantation), and between rejection grade and the trajectory patterns (if any). This information will be analyzed together with the loading of the PCs, and dissect the genes contributing to the different patterns associated with the rejection grades. Optionally, resampling (or permutation)-based procedures can be carried out to evaluate the statistical significance of each conclusion. (3) Multiple clustering analysis techniques - including hierarchical clustering, K-means clustering and self-organization maps - could be attempted and the results compared with the PCA results, to evaluate the robustness of the PCA grouping results. (4) After the genes suspected to contribute to different rejection grades are identified (in (2) and perhaps (3)), pathway analysis will be performed to identify over-represented biological pathways (e.g., signaling pathways)) among the significant genes.
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Note: LB stands for Lead Bioinformatician. An AccuraScience LB is a senior bioinformatics expert and leader of an AccuraScience data analysis team.
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