scLS – a computationally efficient differentially expressed gene detection algorithm
The proposed algorithm enables pseudotime-based dynamics without the need for explicit regression models or branch assignment Single-cell RNA sequencing (scRNA-seq) is a method to measure gene expression of individual cells, allowing observation of various cellular processes, including cell differentiation, ...
The Lomb–Scargle periodogram-based differentially expressed gene detection along pseudotime
Single-cell RNA sequencing has become an important tool for studying how cells change over time. Instead of measuring an average signal from millions of cells, it examines individual cells, allowing researchers to observe biological processes such as development, immune ...
ZINB-GRAN – A ZINB-prior graph adversarial framework for gene regulatory network inference from scRNA-seq data
ZINB-GRAN integrates global network topology with biologically informed distributional regularization to achieve superior performance in identifying regulatory interactions A new graph-based deep learning framework may improve the reconstruction of gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data by ...
NBSR – a Negative Binomial Softmax Regression model for microRNA-seq data analysis
MicroRNAs are small RNA molecules that play a major role in controlling gene expression. By regulating how genes are translated into proteins, microRNAs influence many biological processes, including development, immune responses, cancer progression, and numerous other diseases. As microRNA ...
PSGRN: Gene regulatory network inference from single-cell perturbational data through self-training with synthetic gold standards
Understanding how genes work together inside a cell is one of the central challenges in biology. Genes rarely act alone, instead they form complex gene regulatory networks that control everything from cell growth to response to disease. Mapping these ...
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 ...
scSurv – a deep generative model for single-cell survival analysis
A computational method called scSurv, developed by researchers at Institute of Science Tokyo, links individual cells to patient outcomes using widely available bulk RNA sequencing data. The approach uses single-cell reference datasets together with patient survival data to infer ...
Simple methods rival ai models in single cell RNA analysis
Single-cell RNA sequencing, often called scRNA-seq, allows scientists to measure gene activity in thousands or even millions of individual cells. Because these datasets are large and complex, researchers have recently developed powerful artificial intelligence systems, including transformer-based foundation models ...
ASPEN – robust detection of allelic dynamics in single cell RNA-seq
Researchers often study how genes are turned on and off in single cells to understand what makes cells different from one another. One powerful way to do this is through RNA sequencing, which tells us how much RNA, a ...
A new way to map how cells choose their fate
Researchers present an innovative computational method based on modern mathematics to shed light on cell state dynamics and development Researchers from Kyushu University have developed an innovative computational method, called ddHodge, that can reconstruct the complex dynamics of how ...
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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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