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Sahay, S.

Publications and source records attributed to Sahay, S..

2 recordsLinked to original sources

SSAM-lite: a light-weight web app for rapid analysis of spatially resolved transcriptomics data

1The combination of a cells transcriptional profile and location defines its function in a spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay of choice for characterizing cells in situ. SRT methods can resolve gene expression up to single-molecule resolution. A particular computational problem with single-molecule SRT methods is the correct aggregation of mRNA molecules into cells. Traditionally, aggregating mRNA molecules into cell-based features begins with the identification of cells via segmentation of the nucleus or the cell membrane. However, recently a number of cell-segmentation-free approaches have emerged. While these methods have been demonstrated to be more performant than segmentation-based approaches, they are still not easily accessible since they require specialized knowledge of programming languages and access to large computational resources. Here we present SSAM-lite, a tool that provides an easy-to-use graphical interface to perform rapid and segmentation-free cell-typing of SRT data in a web browser. SSAM-lite runs locally and does not require computational experts or specialized hardware. Analysis of a tissue slice of the mouse somatosensory cortex took less than a minute on a laptop with modest hardware. Parameters can interactively be optimized on small portions of the data before the entire tissue image is analyzed. A server version of SSAM-lite can be run completely offline using local infrastructure. Overall, SSAM-lite is portable, lightweight, and easy to use, thus enabling a broad audience to investigate and analyze single-molecule SRT data. Availability and ImplementationSSAM-lite is an open-source browser-based web application with source code freely available on Github via https://github.com/HiDiHlabs/ssam-lite. Stable releases can be accessed via https://ssam-lite.bihealth.org and https://ssam-lite.netlify.app, and developmental releases can be accessed via https://dev--ssam-lite.netlify.app. The source code for a locally deployable server version, SSAM-lite-server, is available on GitHub via https://github.com/HiDiHlabs/ssam-lite-server. Both versions require a modern browser with JavaScript and WebGL support. Detailed user guides and documentation can be found at https://ssam-lite.readthedocs.io.

bioinformatics↗

Reference-free deconvolution of complex DNA methylation data - a detailed protocol

Epigenomic profiling enables unique insights into human development and diseases. Often the analysis of bulk samples remains the only feasible option for studying complex tissues and organs in large patient cohorts, masking the signatures of important cell populations in convoluted signals. DNA methylomes are highly cell type-specific, and enable recovery of hidden components using advanced computational methods without the need for reference profiles. We propose a three-stage protocol for reference-free deconvolution of DNA methylomes comprising: (i) data preprocessing, confounder adjustment and feature selection, (ii) deconvolution with multiple parameters, and (iii) guided biological inference and validation of deconvolution results. Our protocol simplifies the analysis and integration of DNA methylomes derived from complex samples, including tumors. Applying this protocol to lung cancer methylomes from TCGA revealed components linked to stromal cells, tumor-infiltrating immune cells, and associations with clinical parameters. The protocol takes less than four days to complete and requires basic R skills.

bioinformatics↗