This week, please read the paper titled as “Spotiflow: accurate and efficient spot detection for imaging-based spatial transcriptomics with stereographic flow regression”.
Abstract
Identification of spot-like structures in large, noisy microscopy images is a crucial step for many life-science applications. Imaging-based spatial transcriptomics (iST), in particular, relies on the precise detection of millions of transcripts in low signal-to-noise images. Despite recent advances in computer vision, most of the currently used spot detection techniques are still based on classical signal processing and require tedious manual tuning per dataset. Here we introduce Spotiflow, a deep learning method for subpixel-accurate spot detection that formulates spot detection as a multiscale heatmap and stereographic flow regression problem. Spotiflow supports 2D and 3D images, generalizes across different imaging conditions and is more time and memory efficient than existing methods. We show the efficacy of Spotiflow by extensive quantitative experiments on diverse datasets and demonstrate that its increased accuracy leads to meaningful improvements in biological insights obtained from iST and live imaging experiments. Spotiflow is available as an easy-to-use Python library as well as a napari plugin at https://github.com/weigertlab/spotiflow.
Assignment
- Read the paper and use the Question Sheet which contains a set of questions designed to guide your reading and understanding.
- Fill out your question sheet and submit before the journal club.
- Be ready to discuss in the journal club.
Deadline: Submit your PDF before the journal club session.
Join the Journal Club
During the journal club, we will walk through the question sheet together. Everyone will be selected at random to answer one or more questions from the question sheet, and/or describe selected figures.
Note that it is mandatory to attend the journal club session, and participate in the discussion.