Learning Meaningful Representations of Cells

Visa enkel post
dc.contributor.authorAndrekson, Leo
dc.contributor.departmentChalmers tekniska högskola / Institutionen för life sciencessv
dc.contributor.departmentChalmers University of Technology / Department of Life Sciencesen
dc.contributor.examinerBengtsson-Palme, Johan
dc.contributor.supervisorOropeza Mercado, Rocío
dc.date.accessioned2024-06-04T07:45:09Z
dc.date.available2024-06-04T07:45:09Z
dc.date.issued2024
dc.date.submitted
dc.description.abstractBatch effects are a significant concern in single-cell RNA sequencing (scRNA-Seq) data analysis, where variations in the data can be attributed to factors unrelated to cell types. This can make downstream analysis a challenging task. In this study, a neural network model is designed utilizing contrastive learning and a novel loss func tion for learning an generalizable embedding space from scRNA-Seq data. When benchmarked against multiple established methods for scRNA-Seq integration, the model outperforms existing methods in learning a generalizable embedding space on multiple datasets. A downstream application that was investigated for the embedding space was cell type annotation. When compared against multiple well established cell type classifiers, the model in this study displayed a performance competitive with top performing methods across multiple metrics, such as accuracy, balanced accuracy, and F1 score. These findings aim to quantify the “meaningfulness” of the embedding space learned by the model, and highlight the potential applications of these learned cellular representations. The model is currently being structured into an open-source Python package, simplifying and streamlining its usage.
dc.identifier.coursecodeBBTX60
dc.identifier.urihttp://hdl.handle.net/20.500.12380/307710
dc.language.isoeng
dc.setspec.uppsokLifeEarthScience
dc.subjectscRNA-Seq
dc.subjectDeep learning
dc.subjectContrastive learning
dc.subjectBioinformatics
dc.subjectCell type annotation
dc.subjectNovel cell type detection
dc.subjectCell type representations
dc.subjectMachine learning
dc.subjectAI
dc.subjectTransformer
dc.titleLearning Meaningful Representations of Cells
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeBiotechnology (MPBIO), MSc
Ladda ner
Original bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
Learning Meaningful Representations of Cells.pdf
Storlek:
13.99 MB
Format:
Adobe Portable Document Format
Beskrivning:
Ladda ner
License bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
license.txt
Storlek:
2.35 KB
Format:
Item-specific license agreed upon to submission
Beskrivning:
Ladda ner
Samling
Examensarbeten för masterexamen