USADAE – a deep learning approach to disentangle hidden covariates in RNA-seq data
RNA sequencing has become one of the most widely used tools for studying gene expression. Researchers use it to identify disease mechanisms, discover biomarkers, and understand how genes are regulated. However, analyzing RNA sequencing data is often more complicated ...
Benchmarking single-cell RNA-seq data normalization
When scientists use single-cell RNA sequencing to study thousands of individual cells, they must first correct the raw data so that meaningful biological differences aren’t obscured by technical quirks. This step, called normalization, adjusts for things like differences in ...
Controlling noise in single-cell RNA sequencing with pseudobulk approaches
High-throughput RNA sequencing is a powerful way to study how genes are turned on or off in cells, but it comes with a challenge. Not all differences detected in the data reflect real biological signals, some are caused by ...
D3Impute – dropout-aware discrimination, distribution-aware modeling, and density-guide imputation for scRNA-seq data
Single cell technologies have made it possible to look at gene expression in individual cells, revealing differences that were once hidden in averaged data. However, one major challenge in single cell RNA sequencing is the presence of large numbers ...
sysVI – integrating single-cell RNA-seq datasets with substantial batch effects
Researchers at Helmholtz Munich developed sysVI, a machine learning method that improves integration of RNA sequencing data across diverse systems while preserving vital...
iRECODE – A new computational method that brings clarity to single-cell analysis
The world of cells is surprisingly noisy. Each cell carries unique genetic information, but when we try to measure cellular activity, signals can be lost or blurred, and differences between experiments can further obscure the data. These challenges have ...
MBCdeg4 – a modified clustering-based method for identifying differentially expressed genes from RNA-seq data
RNA sequencing (RNA-seq) is a powerful tool for studying gene expression, enabling researchers to identify differentially expressed genes (DEGs)—genes that are turned "on" or "off" under specific conditions, such as healthy versus diseased cells. Identifying DEGs helps scientists understand ...
A benchmark of RNA-seq data normalization methods for transcriptome mapping
Metabolism is the set of life-sustaining chemical reactions in organisms that allows them to grow, reproduce, maintain their structures, and respond to their environments. To study how these metabolic processes change in different conditions, scientists use genome-scale metabolic models ...
Use of synthetic circular RNA spike-ins (SynCRS) for normalization of circular RNA sequencing data
High-throughput RNA sequencing has revolutionized how scientists study gene expression, enabling us to measure the abundance of RNA transcripts and discover new types of RNA. One such type is circular RNA (circRNA), a molecule that forms a closed loop, ...
Artificial spike-ins could improve accuracy of plant RNA-seq analysis
Researchers at NC State have published a simple trick that improves the accuracy of techniques that help us understand how external variables – such as temperature – affect gene activity in plants...
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Most Shared Posts
- Upcoming Conference – The Festival of Genomics & Biodata
- Gene trajectory inference for single-cell data by optimal transport metrics
- 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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