FedPyDESeq2 – a federated framework for bulk RNA-seq differential expression analysis
RNA sequencing (RNA-seq) has opened up new frontiers in understanding gene activity and its role in health and disease. However, when researchers work with sensitive patient data—especially data collected from multiple institutions—sharing and combining information becomes a tricky balancing act. On one hand, pooling data can lead to deeper insights and more robust statistical analyses. On the other, privacy concerns and regulations can restrict the sharing of raw data across institutions, creating "data silos." This challenge has been a significant barrier to large-scale transcriptome studies. The Promise of Federated Learning A new approach called federated learning (FL) offers a solution to this problem. Federated learning allows researchers to train computational models on data from multiple sources without requiring the raw data to leave its original location. Instead of moving data, institutions share only the results of their local analyses, ensuring privacy while enabling collaboration. Building on this approach, scientists ...
TDEseq – temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data
A new statistical method developed by researchers at Xi'an Jiaotong University called TDEseq is poised to revolutionize the way we study temporal gene expression dynamics in single-cell RNA sequencing...
INVADEseq – cataloging bacterial diversity within tumor samples
It is said in Spanish, "si no existe, créalo" or "if it does not exist, create it." This is exactly what sometimes occurs at the Fred Hutch. When we do not have the tools to test a hypothesis, we must be creative! A great example is a collaboration between Dr. Susan Bullman, an Assistant professor of Human Biology, and Dr. Christopher Johnson, an assistant professor of Vaccine and Infectious Disease, that resulted in a recent Nature Protocols publication. The research teams developed a method called INVADEseq (invasion-adhesion-directed expression sequencing) for identifying bacterial transcripts in cancer cells. A growing number of human cancer types are characterized by the presence and composition of a tumor microbiome, a collection of cancer-associated bacterial species that add yet another layer of complexity to this disease and can influence prognosis or treatment response. Until recently, researchers studying tumor samples have primarily relied on next-generation sequencing-based methods, such as single-cell RNA sequencing (scRNAseq) to study the ...
INVADEseq – invasion adhesion directed expression sequencing
Single-cell RNA sequencing (scRNAseq) technologies have been beneficial in revealing and describing cellular heterogeneity within mammalian tissues, including solid tumors. However, many of these...
INVADEseq – invasion adhesion directed expression sequencing
The tumour-associated microbiota is an intrinsic component of the tumour microenvironment across human cancer types. Intratumoral host–microbiota studies have so far largely relied on bulk tissue...
Comparison of TMM (edgeR), RLE (DESeq2), and MRN Normalization Methods
In the past 5 years, RNA-Seq approaches, based on high-throughput sequencing technologies, are becoming an essential tool in transcriptomics studies. It is now commonly accepted that a normalization preprocessing step can significantly improve the quality of the analysis, in particular, for the differential gene expression analysis. Nevertheless, a gold standard normalization method has not yet been found. Université de Toulouse researchers compared two widely used and very important normalization methods and a third method related to these. The first method is the “Trimmed Mean of M-values” normalization (TMM) described in and implemented in the edgeR package. The second method is the “Relative Log Expression” normalization (RLE) implemented in the DESeq2 package. The third method is the “Median Ratio Normalization” (MRN). It has been shown that TMM and RLE give similar results both with real and simulated data sets. These two methods, as does MRN, deal efficiently with the intrinsic bias ...
SARTools – A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data
Several R packages exist for the detection of differentially expressed genes from RNA-Seq data. The analysis process includes three main steps, namely normalization, dispersion estimation and test for differential expression. Quality control steps along this process are recommended but not mandatory, and failing to check the characteristics of the dataset may lead to spurious results. In addition, normalization methods and statistical models are not exchangeable across the packages without adequate transformations the users are often not aware of. Thus, dedicated analysis pipelines are needed to include systematic quality control steps and prevent errors from misusing the proposed methods. SARTools is an R pipeline for differential analysis of RNA-Seq count data. It can handle designs involving two or more conditions of a single biological factor with or without a blocking factor (such as a batch effect or a sample pairing). It is based on DESeq2 and edgeR and is composed of ...
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