Tracing 700 million years of cell evolution
A new AI tool helps identify hidden similarities across distant species. Why do traditional genetic tools struggle to compare distant species? Scientists often compare cells using single-cell RNA sequencing, which shows which genes are active in each cell. When ...
DeOPUS improves cell-type estimates from bulk RNA sequencing
Single-cell RNA sequencing allows researchers to examine gene activity in individual cells, making it possible to study the many different cell types within a tissue. However, single-cell experiments can be expensive and technically demanding, especially when large numbers of ...
CellTypeAI – cell annotation for scRNA-seq using local generative-AI
Single-cell RNA sequencing, or scRNA-seq, allows researchers to examine gene activity in individual cells rather than averaging signals across thousands or millions of cells. This level of detail can reveal important differences between cell populations, but it also creates ...
Rectangle – robust and scalable multiscale deconvolution informed by single-cell RNA sequencing data
Bulk RNA sequencing remains one of the most widely used methods for measuring gene activity because it is cost effective and works well for large research studies. However, it averages signals from millions of cells, making it difficult to ...
DeepDETAILS – high-resolution reconstruction of cell-type-specific transcriptional regulatory processes from bulk sequencing samples
Modern sequencing technologies have made it possible to study individual cells in remarkable detail. Techniques such as single-cell RNA sequencing and single-cell ATAC sequencing have helped researchers understand how different cell types contribute to health and disease. However, many ...
ScPanKD – new AI framework improves T cell subtype identification in single cell RNA sequencing
Single cell RNA sequencing has become one of the most powerful tools for studying complex tissues because it allows researchers to examine the gene activity of individual cells. One of its most common uses is identifying different cell types ...
Graph AI and Mamba models improve single-cell RNA sequencing cell classification
Single-cell RNA sequencing allows researchers to study gene activity in individual cells, making it possible to identify distinct cell types within complex tissues. However, analyzing these datasets is challenging because they contain thousands of genes, many missing values, and ...
Multiple AI models improve cell type identification in single-cell RNA sequencing
Single-cell RNA sequencing has become one of the most powerful tools for studying complex biological systems. By measuring gene expression in individual cells, researchers can identify different cell types, track cellular development, and better understand disease processes. However, one ...
scSurvival – Single-Cell Survival Analysis of Clinical Cancer Cohort Data at Cellular Resolution
Oregon Health & Science University researchers have developed a first-of-its-kind method to predict cancer patient survival using advanced molecular data from individual cells. Survival analysis is central to clinical oncology. Modern cancer studies can now measure gene activity in ...
PhytoCell – an ensemble learning framework for identifying cell states in plant scRNA-seq data
With the rapid development of single-cell RNA sequencing (scRNA-seq), researchers can now examine gene activity in individual plant cells at unprecedented resolution, opening new opportunities to study cell differentiation, tissue development, and stress responses. However, scRNA-seq datasets compile data ...
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Most Shared Posts
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- Upcoming Workshop – Advanced Topics in Single Cell RNA-Seq Analysis: Multiomics ATAC-Seq & RNA-Seq
- BAP-seq – bioorthogonal masked acylating agents for proximity-dependent RNA labelling
- Single-cell RNA-seq of the rare virosphere reveals the native hosts of giant viruses in the marine environment
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