Machine Learning for Uncovering Biological Insights in Spatial
Transcriptomics Data
- URL: http://arxiv.org/abs/2303.16725v1
- Date: Wed, 29 Mar 2023 14:22:08 GMT
- Title: Machine Learning for Uncovering Biological Insights in Spatial
Transcriptomics Data
- Authors: Alex J. Lee, Robert Cahill, Reza Abbasi-Asl
- Abstract summary: Development and homeostasis in multicellular systems require exquisite control over spatial molecular pattern formation and maintenance.
Advances in spatial transcriptomics (ST) have led to rapid development of innovative machine learning (ML) tools.
We summarize major ST analysis goals that ML can help address and current analysis trends.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Development and homeostasis in multicellular systems both require exquisite
control over spatial molecular pattern formation and maintenance. Advances in
spatially-resolved and high-throughput molecular imaging methods such as
multiplexed immunofluorescence and spatial transcriptomics (ST) provide
exciting new opportunities to augment our fundamental understanding of these
processes in health and disease. The large and complex datasets resulting from
these techniques, particularly ST, have led to rapid development of innovative
machine learning (ML) tools primarily based on deep learning techniques. These
ML tools are now increasingly featured in integrated experimental and
computational workflows to disentangle signals from noise in complex biological
systems. However, it can be difficult to understand and balance the different
implicit assumptions and methodologies of a rapidly expanding toolbox of
analytical tools in ST. To address this, we summarize major ST analysis goals
that ML can help address and current analysis trends. We also describe four
major data science concepts and related heuristics that can help guide
practitioners in their choices of the right tools for the right biological
questions.
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