Français
Matrix factorisation techniques for data integration
Multi angle
Authors : Lê Cao, Kim-Anh (Author of the conference)
CIRM (Publisher )
15A23 - Factorization of matrices
92B15 - General biostatistics, See also {62P10}
CIRM (Publisher )
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Abstract :
Gene module detection methods aim to group genes with similar expression profiles to shed light into functional relationships and co-regulation, and infer gene regulatory networks. Methods proposed so far use clustering to group genes based on global similarity in their expression profiles (co-expression), bi-clustering to group genes and samples simultaneously, network inference to model regulatory relationships between genes. In this talk I will focus on multivariate matrix decomposition techniques that enable dimension reduction and the identification of molecular signatures.
We will consider two different types of assays: bulk and single cell assays. Bulk transcriptomics assays use RNA-sequencing techniques to monitor the average expression profile of all the constituent cells, but fail to identify the distinct transcriptional profiles from different cell types. Single cell assays use similar RNA-seq techniques (scRNA-seq) to those used for bulk cell populations, but provide unprecedented resolution at the cell level to understand cellular heterogeneity and uncover new biology. However, scRNA-seq present new computational and analytical challenges, because of their sheer size (100K – 500K of cells are sequenced) and their zero inflated distribution due to technical drop-outs.
I will illustrate how we can use matrix factorisation technique to mine these data and identify gene modules that underpin molecular mechanisms in cell identity in scRNA-seq. I will also give further perspective on how we could extend similar concepts to integrate different omics data types (e.g. bulk transcriptomics, proteomics, metabolomics) to identify tightly connected multi-omics signatures that holistically describe a biological system.
Keywords : biomathematics; reduction dimension; data integration
MSC Codes : We will consider two different types of assays: bulk and single cell assays. Bulk transcriptomics assays use RNA-sequencing techniques to monitor the average expression profile of all the constituent cells, but fail to identify the distinct transcriptional profiles from different cell types. Single cell assays use similar RNA-seq techniques (scRNA-seq) to those used for bulk cell populations, but provide unprecedented resolution at the cell level to understand cellular heterogeneity and uncover new biology. However, scRNA-seq present new computational and analytical challenges, because of their sheer size (100K – 500K of cells are sequenced) and their zero inflated distribution due to technical drop-outs.
I will illustrate how we can use matrix factorisation technique to mine these data and identify gene modules that underpin molecular mechanisms in cell identity in scRNA-seq. I will also give further perspective on how we could extend similar concepts to integrate different omics data types (e.g. bulk transcriptomics, proteomics, metabolomics) to identify tightly connected multi-omics signatures that holistically describe a biological system.
Keywords : biomathematics; reduction dimension; data integration
15A23 - Factorization of matrices
92B15 - General biostatistics, See also {62P10}
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Information on the Video
Film maker : Hennenfent, GuillaumeLanguage : English Available date : 23/03/2020 Conference Date : 05/03/2020 Subseries : Research talks arXiv category : Machine Learning ; Quantitative Biology Mathematical Area(s) : Probability & Statistics Format : MP4 (.mp4) - HD Video Time : 01:26:46 Targeted Audience : Researchers Download : https://videos.cirm-math.fr/2020-03-05_Le Cao.mp4 
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Information on the Event
Event Title : Thematic Month Week 5: Networks and Molecular Biology / Mois thématique Semaine 5 : Réseaux et biologie moléculaireEvent Organizers : Baudot, Anais ; Hubert, Florence ; Moss, Brigitte ; Rémy, Elisabeth ; Tichit, Laurent ; Vignes, Matthieu Dates : 02/03/2020 - 06/03/2020 Event Year : 2020 Event URL : https://conferences.cirm-math.fr/2305.html
Citation Data
DOI : 10.24350/CIRM.V.19620803Cite this video as: Lê Cao, Kim-Anh (2020). Matrix factorisation techniques for data integration. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19620803 URI : http://dx.doi.org/10.24350/CIRM.V.19620803 |
See Also
- [Multi angle] Part 2 - Dynamic logic models complement machine learning for personalized medicine / Author of the conference Saez-Rodriguez, Julio.
- [Post-edited] Network-guided feature selection in high-dimensional genomic data / Author of the conference Azencott, Chloé.
- [Multi angle] Cell processes in space and time - exemplified with yeast mating / Author of the conference Klipp, Edda.
- [Multi angle] Network approaches for personalized medicine / Author of the conference Martinez-Rodriguez, Maria.
- [Multi angle] Learning interpretable networks from multivariate information in biological and clinical data / Author of the conference Isambert, Hervé.
- [Multi angle] From data to biological processes: interactions, networks and analyses, to understand cellular functions / Author of the conference Brun, Christine.
- [Multi angle] Part 1 - Networks of prior knowledge as frames to understand complex biological data / Author of the conference Saez-Rodriguez, Julio.
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