Interpretable Deep Embeddings for Single-cell RNA-seq Clustering Analysis via Gene Attention
This work presents a ZINB model-based denoising autoencoder that offers interpretable deep embeddings through a gene attention mechanism for single-cell RNA-seq clustering. Our method performs a dimensionality reduction into a latent space that embeds semantic information from gene expression inputs...
- Autores:
-
Forigua Díaz, Cristhian David
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2022
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/63770
- Acceso en línea:
- http://hdl.handle.net/1992/63770
- Palabra clave:
- scRNA-seq
Autoencoder
Gene attention
Interpretability
Clustering
Biología
Ingeniería
- Rights
- openAccess
- License
- Atribución-CompartirIgual 4.0 Internacional
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Interpretable Deep Embeddings for Single-cell RNA-seq Clustering Analysis via Gene Attention |
| title |
Interpretable Deep Embeddings for Single-cell RNA-seq Clustering Analysis via Gene Attention |
| spellingShingle |
Interpretable Deep Embeddings for Single-cell RNA-seq Clustering Analysis via Gene Attention scRNA-seq Autoencoder Gene attention Interpretability Clustering Biología Ingeniería |
| title_short |
Interpretable Deep Embeddings for Single-cell RNA-seq Clustering Analysis via Gene Attention |
| title_full |
Interpretable Deep Embeddings for Single-cell RNA-seq Clustering Analysis via Gene Attention |
| title_fullStr |
Interpretable Deep Embeddings for Single-cell RNA-seq Clustering Analysis via Gene Attention |
| title_full_unstemmed |
Interpretable Deep Embeddings for Single-cell RNA-seq Clustering Analysis via Gene Attention |
| title_sort |
Interpretable Deep Embeddings for Single-cell RNA-seq Clustering Analysis via Gene Attention |
| dc.creator.fl_str_mv |
Forigua Díaz, Cristhian David |
| dc.contributor.advisor.none.fl_str_mv |
Duitama Castellanos, Jorge Alexander |
| dc.contributor.author.none.fl_str_mv |
Forigua Díaz, Cristhian David |
| dc.subject.keyword.none.fl_str_mv |
scRNA-seq Autoencoder Gene attention Interpretability Clustering |
| topic |
scRNA-seq Autoencoder Gene attention Interpretability Clustering Biología Ingeniería |
| dc.subject.themes.es_CO.fl_str_mv |
Biología Ingeniería |
| description |
This work presents a ZINB model-based denoising autoencoder that offers interpretable deep embeddings through a gene attention mechanism for single-cell RNA-seq clustering. Our method performs a dimensionality reduction into a latent space that embeds semantic information from gene expression inputs and uses the latent representations for further clustering into cell groups. Our gene attention mechanism offers a sense of interpretability to how the autoencoder is embedding the gene expression data and offers the possibility to perform gene analysis for clustering. We perform extensive ablation experiments on the configuration of the autoencoder configuration and the attention mechanism. We test our method on six scRNA-seq datasets with different cell types. The results indicate that our method is competitive compared to previous approaches. In particular, it outperforms previous methods on the 10XPBMC and Worm Neuron Cells datasets. Functional enrichment analysis of genes highlighted by attention vectors offers interpretability on how the network processes the gene expression data. The gene analysis shows a correspondence between what the network learns and the cell types in the datasets. |
| publishDate |
2022 |
| dc.date.issued.none.fl_str_mv |
2022-12-16 |
| dc.date.accessioned.none.fl_str_mv |
2023-01-13T19:21:28Z |
| dc.date.available.none.fl_str_mv |
2023-01-13T19:21:28Z |
| dc.type.es_CO.fl_str_mv |
Trabajo de grado - Pregrado |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_7a1f |
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Text |
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http://purl.org/redcol/resource_type/TP |
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http://purl.org/coar/resource_type/c_7a1f |
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acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/1992/63770 |
| dc.identifier.instname.es_CO.fl_str_mv |
instname:Universidad de los Andes |
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reponame:Repositorio Institucional Séneca |
| dc.identifier.repourl.es_CO.fl_str_mv |
repourl:https://repositorio.uniandes.edu.co/ |
| url |
http://hdl.handle.net/1992/63770 |
| identifier_str_mv |
instname:Universidad de los Andes reponame:Repositorio Institucional Séneca repourl:https://repositorio.uniandes.edu.co/ |
| dc.language.iso.es_CO.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.none.fl_str_mv |
[1] ANDOR, N., SIMONDS, E. F., CZERWINSKI, D. K., CHEN, J., GRIMES, S. M., WOODBOUWENS, C., ZHENG, G. X., KUBIT, M. A., GREER, S., WEISS, W. A., ET AL. Singlecell rna-seq of follicular lymphoma reveals malignant b-cell types and coexpression of t-cell immune checkpoints. Blood, The Journal of the American Society of Hematology 133, 10 (2019), 1119-1129. [2] BAGNOLI, J. W., ZIEGENHAIN, C., JANJIC, A., WANGE, L. E., VIETH, B., PAREKH, S., GEUDER, J., HELLMANN, I., AND ENARD, W. Sensitive and powerful single-cell rna sequencing using mcscrb-seq. Nature communications 9, 1 (2018), 1-8. [3] BUTLER, A., HOFFMAN, P., SMIBERT, P., PAPALEXI, E., AND SATIJA, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature biotechnology 36, 5 (2018), 411-420. [4] CAO, J., PACKER, J. S., RAMANI, V., CUSANOVICH, D. A., HUYNH, C., DAZA, R., QIU, X., LEE, C., FURLAN, S. N., STEEMERS, F. J., ET AL. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 6352 (2017), 661-667. [5] CHAUDHRY, F., ISHERWOOD, J., BAWA, T., PATEL, D., GURDZIEL, K., LANFEAR, D. E., RUDEN, D. M., AND LEVY, P. D. Single-cell rna sequencing of the cardiovascular system: New looks for old diseases. Frontiers in Cardiovascular Medicine 6 (2019). 10.3389/fcvm.2019.00173. [6] CHEN, C., LIAO, Y., AND PENG, G. Connecting past and present: Single-cell lineage tracing. Protein amp; Cell 13, 11 (2022), 790-807. 10.1007/s13238-022-00913-7. [7] CHEN, G., NING, B., AND SHI, T. Single-cell rna-seq technologies and related computational data analysis. Frontiers in Genetics 10 (2019). 10.3389/fgene.2019.00317. [8] CIORTAN, M., AND DEFRANCE, M. GNN-based embedding for clustering scRNA-seq data. Bioinformatics 38, 4 (11 2021), 1037¿1044. 10.1093/bioinformatics/btab787. [9] CORNWELL, J. A., HALLETT, R. M., DER MAUER, S. A., MOTAZEDIAN, A., SCHROEDER, T., DRAPER, J. S., HARVEY, R. P., AND NORDON, R. E. Quantifying intrinsic and extrinsic control of single-cell fates in cancer and stem/progenitor cell pedigrees with competing risks analysis. Scientific Reports 6, 1 (2016). 10.1038/srep27100. [10] DENG, Y., BAO, F., DAI, Q., WU, L. F., AND ALTSCHULER, S. J. Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning. Nature methods 16, 4 (2019), 311-314. [11] DING, J., CONDON, A., AND SHAH, S. P. Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Nature communications 9, 1 (2018), 1-13. [12] ERASLAN, G., SIMON, L. M., MIRCEA, M., MUELLER, N. S., AND THEIS, F. J. Singlecell rna-seq denoising using a deep count autoencoder. Nature Communications 10, 1 (2019). 10.1038/s41467-018-07931-2. [13] FAN, X., TANG, D., LIAO, Y., LI, P., ZHANG, Y., WANG, M., LIANG, F., WANG, X., GAO, Y., WEN, L., ET AL. Single-cell rna-seq analysis of mouse preimplantation embryos by third-generation sequencing. PLoS Biology 18, 12 (2020), e3001017. [14] GE, S. X., JUNG, D., AND YAO, R. Shinygo: a graphical gene-set enrichment tool for animals and plants. Bioinformatics 36, 8 (2020), 2628-2629. [15] GIERAHN, T. M., WADSWORTH, M. H., HUGHES, T. K., BRYSON, B. D., BUTLER, A., SATIJA, R., FORTUNE, S., LOVE, J. C., AND SHALEK, A. K. Seq-well: portable, low-cost rna sequencing of single cells at high throughput. Nature methods 14, 4 (2017), 395-398. [16] GROSS, A., SCHOENDUBE, J., ZIMMERMANN, S., STEEB, M., ZENGERLE, R., AND KOLTAY, P. Technologies for single-cell isolation. International Journal of Molecular Sciences 16, 8 (2015), 16897-16919. 10.3390/ijms160816897. [17] HAGHVERDI, L., LUN, A. T., MORGAN, M. D., AND MARIONI, J. C. Batch effects in single-cell rna-sequencing data are corrected by matching mutual nearest neighbors. Nature biotechnology 36, 5 (2018), 421-427. [18] HAN, X., WANG, R., ZHOU, Y., FEI, L., SUN, H., LAI, S., SAADATPOUR, A., ZHOU, Z., CHEN, H., YE, F., ET AL. Mapping the mouse cell atlas by microwell-seq. Cell 172, 5 (2018), 1091-1107. [19] HASHIMSHONY, T., SENDEROVICH, N., AVITAL, G., KLOCHENDLER, A., DE LEEUW, Y., ANAVY, L., GENNERT, D., LI, S., LIVAK, K. J., ROZENBLATT-ROSEN, O., ET AL. Cel-seq2: sensitive highly-multiplexed single-cell rna-seq. Genome biology 17, 1 (2016), 1-7. [20] HUBERT, L., AND ARABIE, P. Comparing partitions. Journal of classification 2, 1 (1985), 193-218. [21] HWANG, B., LEE, J. H., AND BANG, D. Single-cell rna sequencing technologies and bioinformatics pipelines. Experimental amp; Molecular Medicine 50, 8 (2018), 1-14. 10.1038/s12276-018-0071-8. [22] INNES, B. T., AND BADER, G. D. scclustviz-single-cell rnaseq cluster assessment and visualization. F1000Research 7 (2018). [23] JI, Z., AND JI, H. Tscan: Pseudo-time reconstruction and evaluation in single-cell rna-seq analysis. 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Molecular Cell 58, 4 (2015), 610-620. 10.1016/j.molcel.2015.04.005. [30] KUMAR, M. P., DU, J., LAGOUDAS, G., JIAO, Y., SAWYER, A., DRUMMOND, D. C., LAUFFENBURGER, D. A., AND RAUE, A. Analysis of single-cell rna-seq identifies cell-cell communication associated with tumor characteristics. Cell reports 25, 6 (2018), 1458-1468. [31] LAFZI, A., MOUTINHO, C., PICELLI, S., AND HEYN, H. Tutorial: guidelines for the experimental design of single-cell rna sequencing studies. Nature protocols 13, 12 (2018), 2742-2757. [32] LAMBRECHTS, D., WAUTERS, E., BOECKX, B., AIBAR, S., NITTNER, D., BURTON, O., BASSEZ, A., DECALUWÉ, H., PIRCHER, A., VAN DEN EYNDE, K., ET AL. Phenotype molding of stromal cells in the lung tumor microenvironment. Nature medicine 24, 8 (2018), 1277-1289. [33] LEE, J. Y., AND HONG, S.-H. Hematopoietic stem cells and their roles in tissue regeneration. International Journal of Stem Cells 13, 1 (2020), 1-12. [34] LI, Y., YI, M., AND ZOU, X. The linear interplay of intrinsic and extrinsic noises ensures a high accuracy of cell fate selection in budding yeast. Scientific Reports 4, 1 (2014). 10.1038/srep05764. [35] LIKAS, A., VLASSIS, N., AND VERBEEK, J. J. The global k-means clustering algorithm. Pattern recognition 36, 2 (2003), 451-461. [36] LIN, P., TROUP, M., AND HO, J. W. Cidr: Ultrafast and accurate clustering through imputation for single-cell rna-seq data. Genome biology 18, 1 (2017), 1-11. [37] LOPEZ, R., REGIER, J., COLE, M. B., JORDAN, M. I., AND YOSEF, N. Deep generative modeling for single-cell transcriptomics. Nature methods 15, 12 (2018), 1053-1058. [38] MACOSKO, E. Z., BASU, A., SATIJA, R., NEMESH, J., SHEKHAR, K., GOLDMAN, M., TIROSH, I., BIALAS, A. R., KAMITAKI, N., MARTERSTECK, E. M., ET AL. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 5 (2015), 1202-1214. [39] MACPARLAND, S. A., LIU, J. C., MA, X.-Z., INNES, B. T., BARTCZAK, A. M., GAGE, B. K., MANUEL, J., KHUU, N., ECHEVERRI, J., LINARES, I., ET AL. Single cell rna sequencing of human liver reveals distinct intrahepatic macrophage populations. Nature communications 9, 1 (2018), 1-21. [40] MCINNES, L., HEALY, J., AND MELVILLE, J. Umap: Uniform manifold approximation and projection for dimension reduction, 2018. 10.48550/ARXIV.1802.03426. [41] NAIR, V., AND HINTON, G. E. Rectified linear units improve restricted boltzmann machines. In Icml (2010). [42] PACKER, J., AND TRAPNELL, C. Single-cell multi-omics: an engine for new quantitative models of gene regulation. Trends in Genetics 34, 9 (2018), 653-665. [43] PALASCA, O., SANTOS, A., STOLTE, C., GORODKIN, J., AND JENSEN, L. J. Tissues 2.0: an integrative web resource on mammalian tissue expression. Database 2018 (2018). [44] PASZKE, A., GROSS, S., CHINTALA, S., CHANAN, G., YANG, E., DEVITO, Z., LIN, Z., DESMAISON, A., ANTIGA, L., AND LERER, A. Automatic differentiation in pytorch. [45] PENG, L., TIAN, X., TIAN, G., XU, J., HUANG, X., WENG, Y., YANG, J., AND ZHOU, L. Single-cell rna-seq clustering: datasets, models, and algorithms. RNA biology 17, 6 (2020), 765-783. [46] RAND, W. M. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical association 66, 336 (1971), 846-850. [47] REDDI, S. J., KALE, S., AND KUMAR, S. On the convergence of adam and beyond. arXiv preprint arXiv:1904.09237 (2019). [48] SALIBA, A.-E., WESTERMANN, A. J., GORSKI, S. A., AND VOGEL, J. Single-cell rnaseq: Advances and future challenges. Nucleic Acids Research 42, 14 (2014), 8845-8860. 10.1093/nar/gku555. [49] SASAGAWA, Y., DANNO, H., TAKADA, H., EBISAWA, M., TANAKA, K., HAYASHI, T., KURISAKI, A., AND NIKAIDO, I. Quartz-seq2: a high-throughput single-cell rnasequencing method that effectively uses limited sequence reads. Genome biology 19, 1 (2018), 1-24. [50] SATIJA, R., FARRELL, J. A., GENNERT, D., SCHIER, A. F., AND REGEV, A. Spatial reconstruction of single-cell gene expression data. Nature biotechnology 33, 5 (2015), 495-502. [51] SHEKHAR, K., LAPAN, S. W., WHITNEY, I. E., TRAN, N. M., MACOSKO, E. Z., KOWALCZYK, M., ADICONIS, X., LEVIN, J. Z., NEMESH, J., GOLDMAN, M., ET AL. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166, 5 (2016), 1308-1323. [52] STREHL, A., AND GHOSH, J. Cluster ensembles¿a knowledge reuse framework for combining multiple partitions. Journal of machine learning research 3, Dec (2002), 583-617. [53] STUART, T., AND SATIJA, R. Integrative single-cell analysis. Nature reviews genetics 20, 5 (2019), 257-272. [54] TIAN, L., DONG, X., FREYTAG, S., LÊ CAO, K.-A., SU, S., JALALABADI, A., AMANNZALCENSTEIN, D., WEBER, T. S., SEIDI, A., JABBARI, J. S., ET AL. Benchmarking single cell rna-sequencing analysis pipelines using mixture control experiments. Nature methods 16, 6 (2019), 479-487. [55] TIAN, T., WAN, J., SONG, Q., AND WEI, Z. Clustering single-cell rna-seq data with a model-based deep learning approach. Nature Machine Intelligence 1, 4 (2019), 191-198. [56] TIAN, T., ZHANG, J., LIN, X., WEI, Z., AND HAKONARSON, H. Model-based deep embedding for constrained clustering analysis of single cell rna-seq data. Nature communications 12, 1 (2021), 1-12. [57] VAN DER MAATEN, L. Learning a parametric embedding by preserving local structure. In Artificial intelligence and statistics (2009), PMLR, pp. 384-391. [58] WOLD, S., ESBENSEN, K., AND GELADI, P. Principal component analysis. Chemometrics and Intelligent Laboratory Systems 2, 1 (1987), 37-52. https://doi.org/10.1016/0169-7439(87)80084-9. Proceedings of the Multivariate Statistical Workshop for Geologists and Geochemists. [59] WOLF, F. A., ANGERER, P., AND THEIS, F. J. Scanpy: large-scale single-cell gene expression data analysis. Genome biology 19, 1 (2018), 1-5. [60] XIE, J., GIRSHICK, R., AND FARHADI, A. Unsupervised deep embedding for clustering analysis. 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An interpretable framework for clustering single-cell rna-seq datasets. BMC bioinformatics 19, 1 (2018), 1-12. [66] ZHANG, Y., ZENG, F., HAN, X., WENG, J., AND GAO, Y. Lineage tracing: Technology tool for exploring the development, regeneration, and disease of the digestive system. Stem Cell Research amp; Therapy 11, 1 (2020). 10.1186/s13287-020-01941-y. [67] ZHENG, G. X., TERRY, J. M., BELGRADER, P., RYVKIN, P., BENT, Z. W., WILSON, R., ZIRALDO, S. B., WHEELER, T. D., MCDERMOTT, G. P., ZHU, J., ET AL. Massively parallel digital transcriptional profiling of single cells. Nature communications 8, 1 (2017), 1-12. |
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Atribución-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Duitama Castellanos, Jorge Alexandervirtual::4014-1Forigua Díaz, Cristhian David9304be85-1128-47b6-b90c-99b9868629736002023-01-13T19:21:28Z2023-01-13T19:21:28Z2022-12-16http://hdl.handle.net/1992/63770instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/This work presents a ZINB model-based denoising autoencoder that offers interpretable deep embeddings through a gene attention mechanism for single-cell RNA-seq clustering. Our method performs a dimensionality reduction into a latent space that embeds semantic information from gene expression inputs and uses the latent representations for further clustering into cell groups. Our gene attention mechanism offers a sense of interpretability to how the autoencoder is embedding the gene expression data and offers the possibility to perform gene analysis for clustering. We perform extensive ablation experiments on the configuration of the autoencoder configuration and the attention mechanism. We test our method on six scRNA-seq datasets with different cell types. The results indicate that our method is competitive compared to previous approaches. In particular, it outperforms previous methods on the 10XPBMC and Worm Neuron Cells datasets. Functional enrichment analysis of genes highlighted by attention vectors offers interpretability on how the network processes the gene expression data. The gene analysis shows a correspondence between what the network learns and the cell types in the datasets.Ingeniero de Sistemas y ComputaciónPregrado26 páginasapplication/pdfengUniversidad de los AndesIngeniería de Sistemas y ComputaciónFacultad de IngenieríaDepartamento de Ingeniería Sistemas y ComputaciónInterpretable Deep Embeddings for Single-cell RNA-seq Clustering Analysis via Gene AttentionTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPscRNA-seqAutoencoderGene attentionInterpretabilityClusteringBiologíaIngeniería[1] ANDOR, N., SIMONDS, E. F., CZERWINSKI, D. K., CHEN, J., GRIMES, S. M., WOODBOUWENS, C., ZHENG, G. X., KUBIT, M. A., GREER, S., WEISS, W. A., ET AL. Singlecell rna-seq of follicular lymphoma reveals malignant b-cell types and coexpression of t-cell immune checkpoints. 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