AtlasXbrowser enables spatial multi-omics data analysis through the precise determination of the region of interest
Barnett, J.; Silverman, J.; Wetzel, M.; Rao, P.; Sotudeh, N.; Wang, L.
Show abstract
Recent developments in novel spatial sequencing technologies allow for the incorporation of spatial information into high-throughput sequencing assays. One such method, Deterministic Barcoding in Tissue for spatial omics sequencing (DBiT-seq, abbreviated herein as DBiT), utilizes perpendicular microfluidic channels to deliver DNA barcodes across the tissue in a spatially-encoded manner, allowing for sequenced reads to be mapped back onto the 2-D coordinates of the tissue to provide spatial coordinates to cells. DBiT has been the first spatial sequencing technology developed for epigenomic assays beyond transcriptome and proteome. However, despite existing of many open-source software packages for downstream bioinformatics analysis, there is no software available for processing DBiT image data with evenly spaced channels. To facilitate the integration of DBiT spatial and sequenced data, here we proposed a new method to precisely capture the spatial information and further developed AtlasXbrowser based on the new method to extract spatial data from the image data. AtlasXbrowser is a python-based tool with GUI that requires no technical expertise to operate and enables researchers to incorporate brightfield and epifluorescence images of processed tissue samples into downstream bioinformatics analysis tools. Availability and implementationFreely available at https://github.com/atlasxomics/AtlasXbrowser.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- SpatialView: An interactive web application forvisualization of multiple samples in spatialtranscriptomics experiments 94%
- SpatialExperiment: infrastructure for spatially resolved transcriptomics data in R using Bioconductor 94%
- spatialGE: Quantification and visualization of the tumor microenvironment heterogeneity using spatial transcriptomics 94%
Similar papers in this journal
Similar papers in this journal
- SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolution 92%
- CellProfiler 4: Improvements in Speed, Utility and Usability 92%
- LLAMA: a robust and scalable machine learning pipeline for analysis of cellsurface projections in large scale 4D microscopy data 92%
Similar papers in this journal
- VistoSeg: processing utilities for high-resolution Visium/Visium-IF images for spatial transcriptomics data 93%
- Performant web-based interactive visualization tool for spatially-resolved transcriptomics experiments 93%
- Visualization & Quality Control Tools for Large-scale Multiplex Tissue Analysis in TissUUmaps 3 92%
Similar papers in this journal
- STEAM: Spatial Transcriptomics Evaluation Algorithm and Metric for clustering performance 94%
- CSRefiner: A lightweight framework for fine-tuning cell segmentation models with small datasets 93%
- Sincast: a computational framework to predict cell identities in single cell transcriptomes using bulk atlases as references 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.