RNA-Seq Blog Transcriptome Research & Industry News
Accurate identification and effective removal of unwanted variation is essential to derive meaningful biological results from RNA sequencing (RNA-seq)...
Read More »RUV-III – removing unwanted variation from large-scale RNA sequencing data
Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data
Constructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene...
Read More »Normalized counts performs better than TPM, FPKM for hierarchical clustering of replicate RNA-Seq samples
In order to correctly decode phenotypic information from RNA-sequencing (RNA-seq) data, careful selection of the RNA-seq quantification measure is critical for inter-sample comparisons and for...
Read More »SMIXnorm – fast and accurate RNA-Seq data normalization for FFPE Samples
RNA-sequencing (RNA-seq) provides a comprehensive quantification of transcriptomic activities in biological samples. Formalin-Fixed Paraffin-Embedded (FFPE) samples are collected as part of routine clinical procedure, and are the most...
Read More »BatchBench – flexible comparison of batch correction methods for single-cell RNA-seq
As the cost of single-cell RNA-seq experiments has decreased, an increasing number of datasets are now available. Combining newly generated and publicly accessible datasets is challenging due to non-biological signals, commonly known as batch effects...
Read More »RNA-Seq signatures normalized by mRNA abundance allow absolute deconvolution of human immune cell types
The molecular characterization of immune subsets is important for designing effective strategies to understand and treat diseases. A team led by researchers from the University of Liverpool characterized...
Read More »ISnorm – normalizing single-cell RNA sequencing data with internal spike-in-like genes
Normalization with respect to sequencing depth is a crucial step in single-cell RNA sequencing preprocessing. Most methods normalize data using the whole transcriptome based on the assumption that the majority of transcriptome remains constant...
Read More »Scone – performance assessment and selection of normalization procedures for single-cell RNA-Seq
Systematic measurement biases make normalization an essential step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple competing considerations behind the assessment of normalization performance, of which some may be study specific. Researchers at UC Berkeley have developed “scone”- ...
Read More »SCBN – a statistical normalization method and differential expression analysis for RNA-seq data between different species
High-throughput techniques bring novel tools and also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover...
Read More »A combined approach with gene-wise normalization improves the analysis of RNA-seq data in human breast cancer subtypes
Breast cancer (BC) is increasing in incidence and resistance to treatment worldwide. The challenges in limited therapeutic options and poor survival outcomes in BC subtypes persist because of its molecular heterogeneity and resistance to standard endocrine therapy. Recently, high throughput ...
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