Charting the Uncharted: Computational Exploration of Tissue Architecture by Spatially Resolved Transcriptomics | ||||
| International Journal of Intelligent Computing and Information Sciences | ||||
| Volume 23, Issue 3, September 2023, Page 1-8 PDF (368.54 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijicis.2023.186738.1247 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Mohamed Nossier1; Sherin Moussa   2; Nagwa Badr 3
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| 1Program of Bioinformatics, Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt | ||||
| 2Department of Information Systems, Faculty of Computer and Information Scences | ||||
| 3Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt | ||||
| Abstract | ||||
| Exploring and understanding the mechanisms of the complex cellular arrangements orchestration by gene activity in multicellular organisms has great impact on the advancement of life sciences research. Spatial transcriptomics has been enabled by novel technological breakthroughs in next-generation sequencing- and imaging-based techniques to systematically measure the gene expression levels throughout the tissue, and accordingly, increase our capabilities to draw better biological insights in developmental biology and neuroscience as well as to better understand the cellular composition and landscapes of many complex diseases such as cancer. Such large scale data made possible population wide genomic sequencing opens the door to answering many unanswered biological questions using exploratory data analysis. In this paper we deliver a review of the different exploratory data analysis aspects of spatial transcriptomic data in order to test different hypotheses using various experimental designs that utilize and compare different genetic or environmental conditions as well as different points in time. Finally, spatial transcriptomic can be integrated with multiple other omics data in order to provide much broader and deeper insights into the cellular composition and organization. | ||||
| Keywords | ||||
| scRNA-seq; spatial transcriptomics; data analysis; tissue architecture | ||||
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