Feature Selection for Topological Proximity Prediction of Single-Cell Transcriptomic Profiles in Drosophila Embryo Using Genetic Algorithm
| Autor: | Shandar Ahmad, Shruti Gupta, Ajay Kumar Verma |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Reverse engineering lcsh:QH426-470 Computer science Feature selection computer.software_genre Topology single-cell RNA sequencing Set (abstract data type) 03 medical and health sciences 0302 clinical medicine Drosophila embryo Genetic algorithm Genetics genetic algorithm Relevance (information retrieval) Spatial analysis Genetics (clinical) Selection (genetic algorithm) spatial organization gene expression pattern lcsh:Genetics 030104 developmental biology Ranking DREAM challenge computer 030217 neurology & neurosurgery |
| Zdroj: | Genes Volume 12 Issue 1 Genes, Vol 12, Iss 28, p 28 (2021) |
| ISSN: | 2073-4425 |
| DOI: | 10.3390/genes12010028 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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