Helmholtz Gemeinschaft


Inference of differentiation trajectories by transfer learning across biological processes

PDF (Original Article) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
[img] Other (Supporting Information)

Item Type:Article
Title:Inference of differentiation trajectories by transfer learning across biological processes
Creators Name:Jumde, G. and Spanjaard, B. and Junker, J.P.
Abstract:Stem cells differentiate into distinct fates by transitioning through a series of transcriptional states. Current computational approaches allow reconstruction of differentiation trajectories from single-cell transcriptomics data, but it remains unknown to what degree differentiation can be predicted across biological processes. Here, we use transfer learning to infer differentiation processes and quantify predictability in early embryonic development and adult hematopoiesis. Overall, we find that non-linear methods outperform linear approaches, and we achieved the best predictions with a custom variational autoencoder that explicitly models changes in transcriptional variance. We observed a high accuracy of predictions in embryonic development, but we found somewhat lower agreement with the real data in adult hematopoiesis. We demonstrate that this discrepancy can be explained by a higher degree of concordant transcriptional processes along embryonic differentiation compared with adult homeostasis. In summary, we establish a framework for quantifying and exploiting predictability of cellular differentiation trajectories.
Keywords:Single-Cell Transcriptomics, Differentiation Trajectories, Predictability, Transfer Learning
Source:Cell Systems
Publisher:Cell Press / Elsevier
Page Range:75-82.e5
Date:17 January 2024
Official Publication:https://doi.org/10.1016/j.cels.2023.12.002
PubMed:View item in PubMed

Repository Staff Only: item control page


Downloads per month over past year

Open Access
MDC Library