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Introduction Differential Expression vs Gene Co-Expression Analyses In transcriptomics, differential expression analysis (DEA) is a standard step to identify genes whose expression levels significantly differ between conditions. This approach highlights candidate genes associated with a phenotype but focuses only on changes in expression magnitude and typically evaluates a limited number of factors or pairwise comparisons. As a result, it does

  Long-read sequencing technologies, led by Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), have transformed transcriptomics research1. They enable scientists to study alternative splicing and isoform diversity in unprecedented detail. Unlike short-read sequencing, which requires computational assembly of millions of fragments into transcript models, long reads can capture entire RNA molecules in a single read. However, due to various

Accurate cell type prediction is a key step in single-cell RNA-seq (scRNA-seq) analysis, as downstream biological interpretations strongly depend on the quality of these predictions. However, most cell annotation strategies start with an unsupervised clustering step, where parameter choices can substantially affect the resulting cell groupings. Different clustering configurations may reveal distinct biological insights, making it important to evaluate and

Accurate cell type prediction is a crucial step in the interpretation of single-cell RNA-seq data, as downstream biological insights strongly depend on these predictions. However, most annotation strategies rely on an initial unsupervised clustering step that is sensitive to parameter choices, thus leading to substantial variation in cell grouping. While it is widely acknowledged that clustering quality influences downstream analyses,

  The analysis of long-read RNA-sequencing data, such as from the platforms of Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), can be rather complicated, and there are few well-established gold-standard tools in the field. This leads different researchers to choose different tools despite similar research objectives, which can lead to notable differences in their results. One crucial step in