Extraordinary Bioinformatics

In bioinformatics, careful attention to detail is critical when analyzing gene expression and protein expression data. One of the most frequent questions we hear is: What background should be used when performing pathway or other gene set enrichment analysis? Let’s unpack why this choice matters and how using the right background ensures accurate, reproducible results. What Is a Background (or Reference) Gene Set? Before we explore why background selection matters, it's important to clarify what the background (or reference) gene set is. In pathway and gene set enrichment analysis, the background defines the full set of genes or [...]

Discover how researchers are tackling one of spatial transcriptomics’ biggest computational challenges: multi-slice data alignment and integration. Spatial transcriptomics (ST) enables researchers to map gene expression within the native architecture of tissues, providing unprecedented insight into cellular context, tissue organization, and microenvironment dynamics. As the field continues to expand, so do the analytical challenges. Integrating data across multiple tissue slices, managing spatial variability, reconstructing 3D structures, and maintaining resolution without adding noise are major pain points researchers face in ST analysis. This comprehensive review by Khan et al. surveys 24 tools built to solve these complex problems. In this [...]

If you are using or contemplating using single-cell technologies, you may be interested in this review and benchmarking article on cellular deconvolution. Deconvolution is a technique that allows you to get information at the cell type level from bulk RNA data. If you want to know how many cells you have for each cell type present in your assay, you have two options. One way is to sequence every cell, assign a cell type to each cell, and then combine all data to get cell type abundance levels. That's the classical single cell approach. The alternative is to take bulk [...]