Consensus Nature Inspired Clustering of Single-Cell RNA-Sequencing Data
Single-cell RNA sequencing (scRNA-seq) enables quantification of mRNA expression at the level of individual cells. scRNA-seq uncovers the disparity of cellular heterogeneity giving insights about the expression profiles of distinct cells revealing cellular differentiation. The rapid advancements in...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9868780/ |
| _version_ | 1798028196433625088 |
|---|---|
| author | Amany H. Abou El-Naga Sabah Sayed Akram Salah Heba Mohsen |
| author_facet | Amany H. Abou El-Naga Sabah Sayed Akram Salah Heba Mohsen |
| author_sort | Amany H. Abou El-Naga |
| collection | DOAJ |
| description | Single-cell RNA sequencing (scRNA-seq) enables quantification of mRNA expression at the level of individual cells. scRNA-seq uncovers the disparity of cellular heterogeneity giving insights about the expression profiles of distinct cells revealing cellular differentiation. The rapid advancements in scRNA-seq technologies enable researchers to exploit questions regarding cancer heterogeneity and tumor microenvironment. The process of analyzing mainly clustering scRNA-seq data is computationally challenging due to its noisy high dimensionality nature. In this paper, a computational clustering approach is proposed to cluster scRNA-seq data based on consensus clustering using swarm intelligent optimization algorithms to accurately recognize cell subtypes. The proposed approach uses variational auto-encoders to handle the curse of dimensionality, as it operates to create a latent biologically relevant feature space representing the original data. The new latent space is then clustered using Particle Swarm Optimization Algorithm, Multi-Verse Optimization Algorithm and Grey Wolf Optimization Algorithm. A consensus solution is found using solutions returned by the swarm intelligent algorithms. The proposed approach automatically derives the number of clusters without any prior knowledge. To evaluate the performance of the proposed approach a total of four datasets have been used then a comparison against the existing methods in literature has been performed. Experimental results show that the proposed approach performs better than widely most used tools, achieving an adjusted rand index of.95,.75,.88,.9 for Biase, Goolam, Melanoma cancer and Lung cancer datasets respectively. |
| first_indexed | 2024-04-11T19:03:54Z |
| format | Article |
| id | doaj.art-485c5ea17339418490afa6072d69bc87 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T19:03:54Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-485c5ea17339418490afa6072d69bc872022-12-22T04:07:50ZengIEEEIEEE Access2169-35362022-01-0110980799809410.1109/ACCESS.2022.32021879868780Consensus Nature Inspired Clustering of Single-Cell RNA-Sequencing DataAmany H. Abou El-Naga0https://orcid.org/0000-0002-6722-8718Sabah Sayed1https://orcid.org/0000-0002-8344-1535Akram Salah2Heba Mohsen3Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, Cairo, EgyptDepartment of Computer Science, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, EgyptDepartment of Computer Science, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, EgyptDepartment of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, Cairo, EgyptSingle-cell RNA sequencing (scRNA-seq) enables quantification of mRNA expression at the level of individual cells. scRNA-seq uncovers the disparity of cellular heterogeneity giving insights about the expression profiles of distinct cells revealing cellular differentiation. The rapid advancements in scRNA-seq technologies enable researchers to exploit questions regarding cancer heterogeneity and tumor microenvironment. The process of analyzing mainly clustering scRNA-seq data is computationally challenging due to its noisy high dimensionality nature. In this paper, a computational clustering approach is proposed to cluster scRNA-seq data based on consensus clustering using swarm intelligent optimization algorithms to accurately recognize cell subtypes. The proposed approach uses variational auto-encoders to handle the curse of dimensionality, as it operates to create a latent biologically relevant feature space representing the original data. The new latent space is then clustered using Particle Swarm Optimization Algorithm, Multi-Verse Optimization Algorithm and Grey Wolf Optimization Algorithm. A consensus solution is found using solutions returned by the swarm intelligent algorithms. The proposed approach automatically derives the number of clusters without any prior knowledge. To evaluate the performance of the proposed approach a total of four datasets have been used then a comparison against the existing methods in literature has been performed. Experimental results show that the proposed approach performs better than widely most used tools, achieving an adjusted rand index of.95,.75,.88,.9 for Biase, Goolam, Melanoma cancer and Lung cancer datasets respectively.https://ieeexplore.ieee.org/document/9868780/Single-cell RNA-seqautomatic clusteringunsupervised learningswarm intelligencemetaheuristic algorithmsconsensus clustering |
| spellingShingle | Amany H. Abou El-Naga Sabah Sayed Akram Salah Heba Mohsen Consensus Nature Inspired Clustering of Single-Cell RNA-Sequencing Data IEEE Access Single-cell RNA-seq automatic clustering unsupervised learning swarm intelligence metaheuristic algorithms consensus clustering |
| title | Consensus Nature Inspired Clustering of Single-Cell RNA-Sequencing Data |
| title_full | Consensus Nature Inspired Clustering of Single-Cell RNA-Sequencing Data |
| title_fullStr | Consensus Nature Inspired Clustering of Single-Cell RNA-Sequencing Data |
| title_full_unstemmed | Consensus Nature Inspired Clustering of Single-Cell RNA-Sequencing Data |
| title_short | Consensus Nature Inspired Clustering of Single-Cell RNA-Sequencing Data |
| title_sort | consensus nature inspired clustering of single cell rna sequencing data |
| topic | Single-cell RNA-seq automatic clustering unsupervised learning swarm intelligence metaheuristic algorithms consensus clustering |
| url | https://ieeexplore.ieee.org/document/9868780/ |
| work_keys_str_mv | AT amanyhabouelnaga consensusnatureinspiredclusteringofsinglecellrnasequencingdata AT sabahsayed consensusnatureinspiredclusteringofsinglecellrnasequencingdata AT akramsalah consensusnatureinspiredclusteringofsinglecellrnasequencingdata AT hebamohsen consensusnatureinspiredclusteringofsinglecellrnasequencingdata |