The analysis of Spatial transcriptomics images, normally containing cells and mRNA quantified as genes, involves computational challenges due to improper cell segmentation leading to a biologically inaccurate cells-by-genes count matrix. We propose Sparcle to recover the spots, correct the count matrix and improve identification of cell types. The project was partially funded by PSF.
Imaging-based spatial transcriptomics (ST) has the power to reveal patterns of single-cell gene expression by detecting mRNA transcripts as individually resolved spots in multiplexed images. However, molecular quantification has been severely limited by the computational challenges of segmenting poorly outlined, overlapping cells, and of overcoming technical noise; the majority of transcripts are routinely discarded because they fall outside the segmentation boundaries. This lost information leads to less accurate gene count matrices and weakens downstream analyses, such as cell type or gene program identification. Here, we present Sparcle, a probabilistic model that reassigns transcripts to cells based on gene covariation patterns and incorporates spatial features such as distance to nucleus. We demonstrate its utility on multiplexed error-robust fluorescence in situ hybridization (MERFISH) single-molecule FISH (smFISH) data, probabilistic cell typing by In situ Sequencing (pciSeq) and spatially-resolved transcript amplicon readout mapping (STARmap). Sparcle improves transcript assignment, providing more realistic per-cell quantification of each gene, better delineation of cell boundaries, and improved cluster assignments. Critically, our approach does not require an accurate segmentation and is agnostic to technological platform. (https://www.biorxiv.org/content/10.1101/2021.02.13.431099v1). The project was partially funded by Python Software Foundation (PSF)
Intermediate and Advanced researchers and data scientists
Computational Biology, Multiplexed Imaging, Machine Learning, High-Throughput, New Tools
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The open-source, platform independent software implementation of Sparcle is available on Github: https://github.com/sandhya212/Sparcle_for_spot_reassignments