Entropy sorting of single-cell RNA sequencing data reveals the inner cell mass in the human pre-implantation embryo
Summary: A major challenge in single-cell gene expression analysis is to discern meaningful cellular heterogeneity from technical or biological noise. To address this challenge, we present entropy sorting (ES), a mathematical framework that distinguishes genes indicative of cell identity. ES achieve...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Stem Cell Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213671122004568 |
| _version_ | 1797955436272418816 |
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| author | Arthur Radley Elena Corujo-Simon Jennifer Nichols Austin Smith Sara-Jane Dunn |
| author_facet | Arthur Radley Elena Corujo-Simon Jennifer Nichols Austin Smith Sara-Jane Dunn |
| author_sort | Arthur Radley |
| collection | DOAJ |
| description | Summary: A major challenge in single-cell gene expression analysis is to discern meaningful cellular heterogeneity from technical or biological noise. To address this challenge, we present entropy sorting (ES), a mathematical framework that distinguishes genes indicative of cell identity. ES achieves this in an unsupervised manner by quantifying if observed correlations between features are more likely to have occurred due to random chance versus a dependent relationship, without the need for any user-defined significance threshold. On synthetic data, we demonstrate the removal of noisy signals to reveal a higher resolution of gene expression patterns than commonly used feature selection methods. We then apply ES to human pre-implantation embryo single-cell RNA sequencing (scRNA-seq) data. Previous studies failed to unambiguously identify early inner cell mass (ICM), suggesting that the human embryo may diverge from the mouse paradigm. In contrast, ES resolves the ICM and reveals sequential lineage bifurcations as in the classical model. ES thus provides a powerful approach for maximizing information extraction from high-dimensional datasets such as scRNA-seq data. |
| first_indexed | 2024-04-10T23:33:10Z |
| format | Article |
| id | doaj.art-a91d520927f84cbb9228913be102c1a9 |
| institution | Directory Open Access Journal |
| issn | 2213-6711 |
| language | English |
| last_indexed | 2024-04-10T23:33:10Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Stem Cell Reports |
| spelling | doaj.art-a91d520927f84cbb9228913be102c1a92023-01-12T04:19:00ZengElsevierStem Cell Reports2213-67112023-01-011814763Entropy sorting of single-cell RNA sequencing data reveals the inner cell mass in the human pre-implantation embryoArthur Radley0Elena Corujo-Simon1Jennifer Nichols2Austin Smith3Sara-Jane Dunn4Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge Biomedical Campus, Cambridge CB2 0AW, UKMRC Human Genetics Unit, MRC Institute of Genetics and Cancer, The University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UKMRC Human Genetics Unit, MRC Institute of Genetics and Cancer, The University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UKLiving Systems Institute, University of Exeter, Stocker Road, Exeter EX4 4QD, UK; Corresponding authorMicrosoft Research, 21 Station Road, Cambridge CB1 2FB, UK; Corresponding authorSummary: A major challenge in single-cell gene expression analysis is to discern meaningful cellular heterogeneity from technical or biological noise. To address this challenge, we present entropy sorting (ES), a mathematical framework that distinguishes genes indicative of cell identity. ES achieves this in an unsupervised manner by quantifying if observed correlations between features are more likely to have occurred due to random chance versus a dependent relationship, without the need for any user-defined significance threshold. On synthetic data, we demonstrate the removal of noisy signals to reveal a higher resolution of gene expression patterns than commonly used feature selection methods. We then apply ES to human pre-implantation embryo single-cell RNA sequencing (scRNA-seq) data. Previous studies failed to unambiguously identify early inner cell mass (ICM), suggesting that the human embryo may diverge from the mouse paradigm. In contrast, ES resolves the ICM and reveals sequential lineage bifurcations as in the classical model. ES thus provides a powerful approach for maximizing information extraction from high-dimensional datasets such as scRNA-seq data.http://www.sciencedirect.com/science/article/pii/S2213671122004568single-cell RNA sequencingfeature selectionhuman embryo inner cell mass |
| spellingShingle | Arthur Radley Elena Corujo-Simon Jennifer Nichols Austin Smith Sara-Jane Dunn Entropy sorting of single-cell RNA sequencing data reveals the inner cell mass in the human pre-implantation embryo Stem Cell Reports single-cell RNA sequencing feature selection human embryo inner cell mass |
| title | Entropy sorting of single-cell RNA sequencing data reveals the inner cell mass in the human pre-implantation embryo |
| title_full | Entropy sorting of single-cell RNA sequencing data reveals the inner cell mass in the human pre-implantation embryo |
| title_fullStr | Entropy sorting of single-cell RNA sequencing data reveals the inner cell mass in the human pre-implantation embryo |
| title_full_unstemmed | Entropy sorting of single-cell RNA sequencing data reveals the inner cell mass in the human pre-implantation embryo |
| title_short | Entropy sorting of single-cell RNA sequencing data reveals the inner cell mass in the human pre-implantation embryo |
| title_sort | entropy sorting of single cell rna sequencing data reveals the inner cell mass in the human pre implantation embryo |
| topic | single-cell RNA sequencing feature selection human embryo inner cell mass |
| url | http://www.sciencedirect.com/science/article/pii/S2213671122004568 |
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