Improving Recall of In Situ Sequencing by Self-Learned Features and a Graphical Model

TL;DR

This paper introduces a novel method combining self-learned features and a graphical model to significantly improve signal recall in in situ sequencing, enabling better spatial gene expression analysis.

Contribution

It presents a new approach that uses a CNN for self-learning signal probabilities and a graphical model for optimal signal decoding across cycles, enhancing recall over existing methods.

Findings

Increased recall by 27% while maintaining sensitivity.

Most previously lost signals are now correctly identified.

Improved spatial resolution in sequencing data analysis.

Abstract

Image-based sequencing of mRNA makes it possible to see where in a tissue sample a given gene is active, and thus discern large numbers of different cell types in parallel. This is crucial for gaining a better understanding of tissue development and disease such as cancer. Signals are collected over multiple staining and imaging cycles, and signal density together with noise makes signal decoding challenging. Previous approaches have led to low signal recall in efforts to maintain high sensitivity. We propose an approach where signal candidates are generously included, and true-signal probability at the cycle level is self-learned using a convolutional neural network. Signal candidates and probability predictions are thereafter fed into a graphical model searching for signal candidates across sequencing cycles. The graphical model combines intensity, probability and spatial distance to find optimal paths representing decoded signal sequences. We evaluate our approach in relation to state-of-the-art, and show that we increase recall by $27\%$ at maintained sensitivity. Furthermore, visual examination shows that most of the now correctly resolved signals were previously lost due to high signal density. Thus, the proposed approach has the potential to significantly improve further analysis of spatial statistics in in situ sequencing experiments.