Omics Data Integration in Computational Biology Viewed Through the Prism of Machine Learning Paradigms

Overview

Journal Front Bioinform

Specialty Biology

Date 2023 Aug 21

PMID 37600970

Authors

Aziz Fouche

Andrei Zinovyev

Affiliations

Soon will be listed here.

Abstract

Important quantities of biological data can today be acquired to characterize cell types and states, from various sources and using a wide diversity of methods, providing scientists with more and more information to answer challenging biological questions. Unfortunately, working with this amount of data comes at the price of ever-increasing data complexity. This is caused by the multiplication of data types and batch effects, which hinders the joint usage of all available data within common analyses. Data integration describes a set of tasks geared towards embedding several datasets of different origins or modalities into a joint representation that can then be used to carry out downstream analyses. In the last decade, dozens of methods have been proposed to tackle the different facets of the data integration problem, relying on various paradigms. This review introduces the most common data types encountered in computational biology and provides systematic definitions of the data integration problems. We then present how machine learning innovations were leveraged to build effective data integration algorithms, that are widely used today by computational biologists. We discuss the current state of data integration and important pitfalls to consider when working with data integration tools. We eventually detail a set of challenges the field will have to overcome in the coming years.

Citing Articles

A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data.

Loers J, Vermeirssen V Brief Bioinform. 2024; 25(5).

PMID: 39207727 PMC: 11359808. DOI: 10.1093/bib/bbae382.

scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration.

Yang X, Mann K, Wu H, Ding J Genome Biol. 2024; 25(1):198.

PMID: 39075536 PMC: 11285326. DOI: 10.1186/s13059-024-03338-z.

References

Duren Z, Chen X, Zamanighomi M, Zeng W, Satpathy A, Chang H . Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations. Proc Natl Acad Sci U S A. 2018; 115(30):7723-7728. PMC: 6065048. DOI: 10.1073/pnas.1805681115. View

Chen H, Albergante L, Hsu J, Lareau C, Lo Bosco G, Guan J . Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM. Nat Commun. 2019; 10(1):1903. PMC: 6478907. DOI: 10.1038/s41467-019-09670-4. View

Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay P, Swerdlow H . Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017; 14(9):865-868. PMC: 5669064. DOI: 10.1038/nmeth.4380. View

Buenrostro J, Wu B, Litzenburger U, Ruff D, Gonzales M, Snyder M . Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015; 523(7561):486-90. PMC: 4685948. DOI: 10.1038/nature14590. View

Li B, Zhang W, Guo C, Xu H, Li L, Fang M . Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nat Methods. 2022; 19(6):662-670. DOI: 10.1038/s41592-022-01480-9. View

Camps J, Noel F, Liechti R, Massenet-Regad L, Rigade S, Gotz L . Meta-Analysis of Human Cancer Single-Cell RNA-Seq Datasets Using the IMMUcan Database. Cancer Res. 2022; 83(3):363-373. PMC: 9896021. DOI: 10.1158/0008-5472.CAN-22-0074. View

Cantini L, Zakeri P, Hernandez C, Naldi A, Thieffry D, Remy E . Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nat Commun. 2021; 12(1):124. PMC: 7785750. DOI: 10.1038/s41467-020-20430-7. View

Fouche A, Chadoutaud L, Delattre O, Zinovyev A . : a unifying computational framework for modular single-cell RNA-seq data integration. NAR Genom Bioinform. 2023; 5(3):lqad069. PMC: 10336778. DOI: 10.1093/nargab/lqad069. View

Luecken M, Buttner M, Chaichoompu K, Danese A, Interlandi M, Mueller M . Benchmarking atlas-level data integration in single-cell genomics. Nat Methods. 2021; 19(1):41-50. PMC: 8748196. DOI: 10.1038/s41592-021-01336-8. View

10.

Johansen N, Quon G . scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data. Genome Biol. 2019; 20(1):166. PMC: 6693154. DOI: 10.1186/s13059-019-1766-4. View

11.

Sugihara R, Kato Y, Mori T, Kawahara Y . Alignment of single-cell trajectory trees with CAPITAL. Nat Commun. 2022; 13(1):5972. PMC: 9568509. DOI: 10.1038/s41467-022-33681-3. View

12.

Stahl P, Salmen F, Vickovic S, Lundmark A, Fernandez Navarro J, Magnusson J . Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016; 353(6294):78-82. DOI: 10.1126/science.aaf2403. View

13.

Cao K, Hong Y, Wan L . Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona. Bioinformatics. 2021; 38(1):211-219. PMC: 8696097. DOI: 10.1093/bioinformatics/btab594. View

14.

La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H, Petukhov V . RNA velocity of single cells. Nature. 2018; 560(7719):494-498. PMC: 6130801. DOI: 10.1038/s41586-018-0414-6. View

15.

Xu Y, Das P, McCord R . SMILE: mutual information learning for integration of single-cell omics data. Bioinformatics. 2021; 38(2):476-486. PMC: 10060712. DOI: 10.1093/bioinformatics/btab706. View

16.

Macosko E, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M . Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015; 161(5):1202-1214. PMC: 4481139. DOI: 10.1016/j.cell.2015.05.002. View

17.

Loza M, Teraguchi S, Standley D, Diez D . Unbiased integration of single cell transcriptome replicates. NAR Genom Bioinform. 2022; 4(1):lqac022. PMC: 8923008. DOI: 10.1093/nargab/lqac022. View

18.

Eraslan G, Simon L, Mircea M, Mueller N, Theis F . Single-cell RNA-seq denoising using a deep count autoencoder. Nat Commun. 2019; 10(1):390. PMC: 6344535. DOI: 10.1038/s41467-018-07931-2. View

19.

Welch J, Hartemink A, Prins J . MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics. Genome Biol. 2017; 18(1):138. PMC: 5525279. DOI: 10.1186/s13059-017-1269-0. View

20.

Johnson W, Li C, Rabinovic A . Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2006; 8(1):118-27. DOI: 10.1093/biostatistics/kxj037. View