The rate of raw sequence production through Next-Generation Sequencing (NGS) has been growing exponentially due to improved technology and reduced costs. This has enabled researchers to answer many biological questions through "multi-omics"...
Read More »Single-cell RNA counting using a complex set of molecular spike-ins
Single-cell sequencing methods rely on molecule-counting strategies to account for amplification biases, yet no experimental strategy to evaluate counting performance exists. Researchers from...
Read More »One Cell At a Time (OCAT) – a unified framework to integrate and analyze single-cell RNA-seq data
Integrative analysis of large-scale single-cell RNA sequencing (scRNA-seq) datasets can aggregate complementary biological information from different datasets. However, most existing methods fail to efficiently integrate multiple large-scale scRNA-seq...
Read More »MarcoPolo – discover differentially expressed genes in single-cell RNA-seq data without depending on prior clustering
The standard analysis pipeline for single-cell RNA-seq data consists of sequential steps initiated by clustering the cells. An innate limitation of this pipeline is that an imperfect clustering result can irreversibly affect the succeeding steps. For example, there can be ...
Read More »TWO-SIGMA-G – a new competitive gene set testing framework for scRNA-seq data accounting for inter-gene and cell–cell correlation
Researchers at Harvard T.H. Chan School of Public Health and the University of North Carolina at Chapel Hill propose TWO-SIGMA-G, a competitive gene set...
Read More »SeqCVIBE – interactive analysis, exploration, and visualization of RNA-Seq data
The rise of modern gene expression profiling techniques, such as RNA-Seq, has generated a wealth of high-quality datasets spanning all fields...
Read More »baredSC – Bayesian approach to retrieve expression distribution of single-cell data
The number of studies using single-cell RNA sequencing (scRNA-seq) is constantly growing. This powerful technique provides a sampling of the whole transcriptome of a cell. However, sparsity of the data can be a major hurdle when studying the...
Read More »scCorr – A novel graph-based k-partitioning approach improves the detection of gene-gene correlations by single-cell RNA sequencing
Gene expression is regulated by transcription factors, cofactors, and epigenetic mechanisms. Coexpressed genes indicate similar functional categories and gene networks. Detecting gene-gene coexpression is important for understanding the underlying...
Read More »Statistical approaches for differential expression analysis in single-cell RNA sequencing studies
Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential...
Read More »IDEAS – individual level differential expression analysis for single-cell RNA-seq data
A team led by researchers at the Fred Hutchison Cancer Research Center considers an increasingly popular study design where single-cell RNA-seq...
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