👤 David J Pocalyko

Transcriptomics technologies such as next-generation sequencing and microarray platforms provide exciting opportunities for improving diagnosis and treatment of complex diseases. Transcriptomics studies often share similar hypotheses, but are carried out on different platforms, in different conditions, and with different analysis approaches. These factors, in addition to small sample sizes, can result in a lack of reproducibility. A clear understanding and unified picture of many complex diseases are still elusive, highlighting an urgent need to effectively integrate multiple transcriptomic studies for disease signatures. We have integrated more than 3,000 high-quality transcriptomic datasets in oncology, immunology, neuroscience, cardiovascular and metabolic disease, and from both public and internal sources (DiseaseLand database). We established a systematic data integration and meta-analysis approach, which can be applied in multiple disease areas to create a unified picture of the disease signature and prioritize drug targets, pathways, and compounds. In this bipolar case study, we provided an illustrative example using our approach to combine a total of 30 genome-wide gene expression studies using postmortem human brain samples. First, the studies were integrated by extracting raw FASTQ or CEL files, then undergoing the same procedures for preprocessing, normalization, and statistical inference. Second, both Show less