Omics data allow us to decipher the different languages of biology: that of DNA, RNA, proteins, and metabolites. Each layer—genomics, epigenomics, transcriptomics, proteomics, and metabolomics—provides an essential piece for understanding biological systems in all their complexity. At Datharsis, we analyze and integrate this information to transform biomedical data into explainable and applicable knowledge.
The hidden language of omics data Read Post »
When analyzing repeated measures designs—such as a patient’s evolution or a machine’s logs—an error in modeling the individual can lead to misleading conclusions, like false positives. To avoid this, ASCA+ correctly models individual variability, ensuring that results are robust, reliable, and reproducible, even with complex and unbalanced data.
Modeling the individual in Repeated Measures Models with ASCA+ Read Post »
Which variables should you actually interpret in a statistical model?
In this article, we explain how Sparse PCA (SPCA) — and a new corrected version — can help you identify key variables, such as biomarkers, without losing interpretability. A practical approach from biostatistics for projects with lots of data and little certainty.
Sparse PCA and biomarkers: understanding more, assuming less Read Post »
