Abstract
Metabolic disorders such as obesity and diabetes are diseases which develop gradually over time through the perturbations of biological processes. These perturbed biological processes usually work in an interdependent way. Systematic experiments tracking disease progression at gene level are usually conducted through a temporal microarray data. There is a need for developing methods to analyze such highly complex data to capture disease progression at the molecular level. In the present study, we have considered temporal microarray data from an experiment conducted to study development of obesity and diabetes in mice. We first constructed a network between biological processes through common genes. We analyzed the data to obtain perturbed biological processes at each time point. Finally, we used the biological process network to find links between these perturbed biological processes. This enabled us to identify paths linking initial perturbed processes with final perturbed processes which capture disease progression. Using different datasets and statistical tests, we established that these paths are highly precise to the dataset from which these are obtained. We also established that the connecting genes present in these paths might contain some biological information and thus can be used for further mechanistic studies. The methods developed in our study are also applicable to a broad array of temporal data.
Highlights
High throughput data like Microarray [1, 2] or RNAseq [3] are used to study systematically a disease condition or how organism is responding to different conditions of the experiment [4]
In the gene set enrichment analysis, briefly, for each time point, the genes are first sorted by the absolute values of fold change in descending order to get a sorted gene list
To find how processes perturbed at the initial time point affects processes perturbed at later time point, we considered looking at connectivity between biological processes
Summary
High throughput data like Microarray [1, 2] or RNAseq [3] are used to study systematically a disease condition or how organism is responding to different conditions of the experiment [4]. To study a disease condition from such a high throughput data, instead of looking at expression levels of each gene one by one, it is more informative to look at biological processes perturbed at different experimental conditions [4]. One can find biological processes significantly enriched in each cluster using tools such as enrichr [7]. Other methods such as Gene Set Enrichment Analysis [8] finds.
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