Deep Learning model to Automate the process of mapping Cancer Cells to Cell Lines & Cancer Types from Single Cell RNA-Seq Data

Authors

  • Vatsal Patel  BS Mathematics, St. Xavier's College, Mumbai, India

DOI:

https://doi.org/10.32628/CSEIT21741

Keywords:

Single Cell RNA-Seq, Deep Learning, Cancer Biology, Cancer Cell Lines, Automation, Neural Network, Gene Expression, Transfer Learning

Abstract

Single Cell RNA Sequencing has given us a broad domain to study heterogeneity & expression profiles of cells. Downstream analysis of such data has led us to important observation and classification of cell types. However, these approaches demand great exertion and effort added that it seems the only way to proceed ahead for the first time. Results of such verified analysis have led us to create labels from our dataset. We can use the same labeled data as an input to a neural network and this way we would be able to automate the tedious & time-consuming process of downstream analysis. In this paper, we have automated the process of mapping cancer cells to cancer cell lines & cancer types. For the same, we have used pan-cancer single cell sequencing data of 53513 cells from 198 cell lines reflecting 22 cancer types.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-08-30

Issue

Volume 7, Issue 4, July-August-2021

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Vatsal Patel, " Deep Learning model to Automate the process of mapping Cancer Cells to Cell Lines & Cancer Types from Single Cell RNA-Seq Data" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 4, pp.17-26, July-August-2021. Available at doi : https://doi.org/10.32628/CSEIT21741