Feature Selection for Topological Proximity Prediction of Single-Cell Transcriptomic Profiles in Drosophila Embryo Using Genetic Algorithm

Gupta, Shruti; Verma, Ajay Kumar; Ahmad, Shandar

doi:10.3390/genes12010028

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Feature Selection for Topological Proximity Prediction of Single-Cell Transcriptomic Profiles in Drosophila Embryo Using Genetic Algorithm

Shruti Gupta

Ajay Kumar Verma

and

Shandar Ahmad

School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Mehrauli Road, New Delhi 110067, India

Author to whom correspondence should be addressed.

Genes 2021, 12(1), 28; https://doi.org/10.3390/genes12010028

Received: 30 September 2020 / Revised: 16 December 2020 / Accepted: 22 December 2020 / Published: 28 December 2020

(This article belongs to the Special Issue Selected Papers from the 19th International Conference on Bioinformatics (InCoB 2020))

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Abstract

Single-cell transcriptomics data, when combined with in situ hybridization patterns of specific genes, can help in recovering the spatial information lost during cell isolation. Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium conducted a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC) to predict the masked locations of single cells from a set of 60, 40 and 20 genes out of 84 in situ gene patterns known in Drosophila embryo. We applied a genetic algorithm (GA) to predict the most important genes that carry positional and proximity information of the single-cell origins, in combination with the base distance mapping algorithm DistMap. Resulting gene selection was found to perform well and was ranked among top 10 in two of the three sub-challenges. However, the details of the method did not make it to the main challenge publication, due to an intricate aggregation ranking. In this work, we discuss the detailed implementation of GA and its post-challenge parameterization, with a view to identify potential areas where GA-based approaches of gene-set selection for topological association prediction may be improved, to be more effective. We believe this work provides additional insights into the feature-selection strategies and their relevance to single-cell similarity prediction and will form a strong addendum to the recently published work from the consortium. View Full-Text

Keywords: spatial organization; single-cell RNA sequencing; gene expression pattern; Drosophila embryo; DREAM challenge; genetic algorithm

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Gupta, S.; Verma, A.K.; Ahmad, S. Feature Selection for Topological Proximity Prediction of Single-Cell Transcriptomic Profiles in Drosophila Embryo Using Genetic Algorithm. Genes 2021, 12, 28. https://doi.org/10.3390/genes12010028

AMA Style

Gupta S, Verma AK, Ahmad S. Feature Selection for Topological Proximity Prediction of Single-Cell Transcriptomic Profiles in Drosophila Embryo Using Genetic Algorithm. Genes. 2021; 12(1):28. https://doi.org/10.3390/genes12010028

Chicago/Turabian Style

Gupta, Shruti, Ajay K. Verma, and Shandar Ahmad. 2021. "Feature Selection for Topological Proximity Prediction of Single-Cell Transcriptomic Profiles in Drosophila Embryo Using Genetic Algorithm" Genes 12, no. 1: 28. https://doi.org/10.3390/genes12010028

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Feature Selection for Topological Proximity Prediction of Single-Cell Transcriptomic Profiles in Drosophila Embryo Using Genetic Algorithm

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