PointFISH -- learning point cloud representations for RNA localization patterns

Arthur Imbert, Florian Mueller, Thomas Walter
2/21/2023
q-bio.QMcs.CV

No official code found. Best implementation: Henley13/PointFISH (3 stars, BSD-3-Clause, last commit 2022-08-19).

Abstract

Subcellular RNA localization is a critical mechanism for the spatial control of gene expression. Its mechanism and precise functional role is not yet very well understood. Single Molecule Fluorescence in Situ Hybridization (smFISH) images allow for the detection of individual RNA molecules with subcellular accuracy. In return, smFISH requires robust methods to quantify and classify RNA spatial distribution. Here, we present PointFISH, a novel computational approach for the recognition of RNA localization patterns. PointFISH is an attention-based network for computing continuous vector representations of RNA point clouds. Trained on simulations only, it can directly process extracted coordinates from experimental smFISH images. The resulting embedding allows scalable and flexible spatial transcriptomics analysis and matches performance of hand-crafted pipelines.

View on arXivView PDF

Code Implementations(1)

Henley13/PointFISHCommunity100%
BSD-3-Clause

Python scripts used for the paper "PointFISH: learning point cloud representations for RNA localization patterns" published in ECCV 2022 BioImage Computing workshop.

30Shell, PythonAug 6, 20224 years agoBSD-3-Clause

Similar Papers

MagNet: Multi-Level Attention Graph Network for Predicting High-Resolution Spatial Transcriptomics

83% match1 repo122025

Fusing Pixels and Genes: Spatially-Aware Learning in Computational Pathology

83% match1 repo112026

STINR: Deciphering Spatial Transcriptomics via Implicit Neural Representation

83% match1 repo212026

MIST: Multi-Scale Spatial Context for Gene Expression Prediction from Histology

83% match2026

From Spots to Pixels: Dense Spatial Gene Expression Prediction from Histology Images

82% match1 repo52025

Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data

82% match2023

Cite this paper

@article{imbert2023pointfish,
  title  = {PointFISH -- learning point cloud representations for RNA localization patterns},
  author = {Arthur Imbert and Florian Mueller and Thomas Walter},
  year   = {2023},
  eprint = {2302.10923},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.10923v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2302.10923.svg)](https://codeofpaper.com/paper/2302.10923)

Discussion