BioTuring's Blog

Given a spatial slide with cell type annotations, the goal is to identify regions with homogeneous cell type compositions. For example, with a slide containing two distinct cell types, we aim to identify regions such as: Region 1: only cell type A Region 2: composed of both cell type A Read More

Cell type annotation is a critical step in scRNA-seq data analysis that allows scientists to gain deeper insight into identifying the cell types that drive a biological process. However, the task of accurately assigning cell type labels to individual cells remains a challenge due to the need to navigate the Read More

Authors: BioTuring Team Spatial omics have revolutionized our understanding of gene expression patterns across tissues and organs, enabling the study of spatial domains—regions defined by distinct biological processes and cell type compositions. Here, we present a novel approach for detecting spatial domains using Weighted Gene Co-expression Network Analysis (WGCNA-https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-559) Read More

TLDR: We released a specialized model of TuringSegment for H&E images. Our model outperforms the original Cellpose model by a large margin, achieving approximately 40 percentage points higher AP50 on the Panuke and 48 for Nuinsseg datasets, and 43 percentage points higher on the Monuseg dataset. The model has been Read More

TLDR: We optimized Cellpose to be 300x faster and 23x more memory-efficient in 2D without compromising accuracy. We also developed an efficient algorithm to stitch series of 2D segmentations into 3D segmentations, and obtained 400x speedup in time, and 300x less memory consumption. We named the new tool as Read More