Channel: PyData

Category: Science & Technology

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Description: Sparcle: Assigning Transcripts to Cells in Multiplexed Images Speaker: Sandhya Prabhakaran Summary 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. Description 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. (biorxiv.org/content/10.1101/2021.02.13.431099v1). The project was partially funded by Python Software Foundation (PSF) Sandhya Prabhakaran's Bio Dr. Sandhya Prabhakaran is a Research Scientist at Moffitt Cancer Centre, Florida. Before that she was a Research Scientist at Memorial Sloan Kettering Cancer Centre and Columbia University. Her Ph.D. in Computer Science is from University of Basel and her Masters in Intelligent Systems (Robotics) is from University of Edinburgh. Her research deals with developing statistical theory and Bayesian inference models, particularly to problems in Cancer Biology and Computer Vision. Prior to academics, she was an Assembler programmer working with the Mainframe Operating System (z/OS) at IBM Software Laboratories and has developed Mainframe applications. She has completed 4 out of the 6 World Marathon Majors. GitHub: github.com/sandhya212 Twitter: twitter.com/sandhya212 LinkedIn: linkedin.com/in/sandhyaprabhakaran Website: sandhyaprabhakaran.com PyData Global 2021 Website: pydata.org/global2021 LinkedIn: linkedin.com/company/pydata-global Twitter: twitter.com/PyData pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: github.com/numfocus/YouTubeVideoTimestamps