ASPEN – robust detection of allelic dynamics in single cell RNA-seq
Scientists from the University of New South Wales, Australia, have developed a new statistical approach called ASPEN to better understand how individual copies of genes behave inside single cells using RNA sequencing data. Cells contain two copies (or alleles) ...
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 ...
HALO – hierarchical causal modeling for single cell multi-omics data
Researchers at the University of Pittsburgh have developed an innovative framework called HALO that reveals how changes in gene activity and chromatin structure interact over time. While open chromatin often signals active transcription, these two processes don’t always move ...
RCANE – whole-genome pan-cancer somatic copy number aberration prediction using RNA-seq data
Researchers are finding smarter ways to get more information from the data scientists already collect. In cancer research, transcriptome sequencing, or RNA sequencing, is commonly used to measure gene activity and understand how tumors grow and change. But what ...
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 ...
M&M – an RNA-seq based pan-cancer classifier for paediatric tumours
Diagnosing childhood cancers is one of the most difficult challenges in modern medicine. While many cancers can be identified by looking at tumor cells under a microscope, rare tumors are much harder to classify. An incorrect or delayed diagnosis ...
TRADE – ranscriptome-wide analysis of differential expression
CRISPR technology has made it possible to systematically explore the effects of turning genes on or off, and when paired with RNA sequencing, it allows researchers to see how those genetic changes affect the entire transcriptome—essentially, a full snapshot ...
CoupleVAE – coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data
As single-cell RNA sequencing becomes more advanced, scientists are now able to analyze the genetic activity of individual cells in extraordinary detail. This is especially useful for studying how cells respond to things like drugs, infections, or other environmental ...
A realistic FastQ-based framework – FastQDesign for scRNA-seq study design issues
Single-cell RNA sequencing (scRNA-seq) has revolutionized the way scientists study gene expression at the individual cell level. However, designing an scRNA-seq experiment requires careful planning, particularly when balancing the number of cells analyzed and sequencing depth while staying within ...
Combining deep learning techniques with statistical boosting to understanding single-cell RNA sequencing data
Researchers at the University of Freiburg have developed a novel method to improve how scientists analyze single-cell RNA sequencing (scRNA-seq) data. Their approach, called the boosting autoencoder (BAE), combines deep learning techniques with statistical boosting to make it easier ...
RNA-Seq Products & Services
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
Stay Connected