Biomedical subjects

Raphael Gottardo

Publications and source records attributed to Raphael Gottardo.

4 recordsLinked to original sources

PubMed2026

Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.

Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcriptomics, delineating higher-order anatomical structures is critical for understanding how cellular organization affects function. However, the reliability of current benchmarks of spatially aware clustering (SAC) methods is undermined by their narrow focus on Visium and brain tissue datasets and the incorrect interpretation of manual annotation as ground truth. Here we present SACCELERATOR, a community-driven, extensible framework that standardizes data formatting, method integration and metric evaluation, enabling rapid inclusion of new methods and datasets. Our analysis revealed substantial limitations in the generalizability and reproducibility of SAC methods and shows that anatomical labels commonly used as ground truths are often biased, error prone and unsuitable for benchmarking. Rather than ranking methods, we propose a consensus-guided workflow where descriptive spatial metrics highlight high-entropy regions of method disagreement, enabling targeted feedback for tissue experts. Applied to brain and cancer datasets, this approach uncovered biologically meaningful patterns overlooked by individual SAC methods and manual annotations, highlighting the need for iterative, expert-in-the-loop evaluation.

Benchmarking↗

PubMed2004

Evaluation of the host transcriptional response to human cytomegalovirus infection.

Gene expression data from human cytomegalovirus (HCMV)-infected cells were analyzed using DNA-Chip Analyzer (dChip) followed by singular value decomposition (SVD) and compared with a previous analysis of the same data that employed GeneChip software and a fold change filtering approach. dChip and SVD analysis revealed two clusters of coexpressed human genes responding differently to HCMV infection: one containing some genes identified previously, and another that was largely unique to this analysis. Annotating these genes, we identified several functional categories important to host cell responses to HCMV infection. These categories included genes involved in transcriptional regulation, oncogenesis, and cell cycle regulation, which were more prevalent in cluster 1, and genes involved in immune system regulation, signal transduction, and cell adhesion, which were more prevalent in cluster 2. Within these categories, we found genes involved in the host response to HCMV infection (mainly in cluster 1), as well as genes targeted by HCMV's immune evasion strategies (mainly in cluster 2). As the second group of genes identified by the dChip and SVD approach was statistically and biologically significant, our results point out the advantages of using different methods to analyze gene expression data.

Cytomegalovirus Infections↗

PubMed2003

Lateral gene transfer and ancient paralogy of operons containing redundant copies of tryptophan-pathway genes in Xylella species and in heterocystous cyanobacteria.

BACKGROUND: Tryptophan-pathway genes that exist within an apparent operon-like organization were evaluated as examples of multi-genic genomic regions that contain phylogenetically incongruous genes and coexist with genes outside the operon that are congruous. A seven-gene cluster in Xylella fastidiosa includes genes encoding the two subunits of anthranilate synthase, an aryl-CoA synthetase, and trpR. A second gene block, present in the Anabaena/Nostoc lineage, but not in other cyanobacteria, contains a near-complete tryptophan operon nested within an apparent supraoperon containing other aromatic-pathway genes. RESULTS: The gene block in X. fastidiosa exhibits a sharply delineated low-GC content. This, as well as bias of codon usage and 3:1 dinucleotide analysis, strongly implicates lateral gene transfer (LGT). In contrast, parametric studies and protein tree phylogenies did not support the origination of the Anabaena/Nostoc gene block by LGT. CONCLUSIONS: Judging from the apparent minimal amelioration, the low-GC gene block in X. fastidiosa probably originated by LGT at a relatively recent time. The surprising inability to pinpoint a donor lineage still leaves room for alternative, albeit less likely, explanations other than LGT. On the other hand, the large Anabaena/Nostoc gene block does not seem to have arisen by LGT. We suggest that the contemporary Anabaena/Nostoc array of divergent paralogs represents an ancient ancestral state of paralog divergence, with extensive streamlining by gene loss occurring in the lineage of descent representing other (unicellular) cyanobacteria.

Amino Acid Sequence↗

PubMed2003

Statistical analysis of microarray data: a Bayesian approach.

The potential of microarray data is enormous. It allows us to monitor the expression of thousands of genes simultaneously. A common task with microarray is to determine which genes are differentially expressed between two samples obtained under two different conditions. Recently, several statistical methods have been proposed to perform such a task when there are replicate samples under each condition. Two major problems arise with microarray data. The first one is that the number of replicates is very small (usually 2-10), leading to noisy point estimates. As a consequence, traditional statistics that are based on the means and standard deviations, e.g. t-statistic, are not suitable. The second problem is that the number of genes is usually very large (approximately 10,000), and one is faced with an extreme multiple testing problem. Most multiple testing adjustments are relatively conservative, especially when the number of replicates is small. In this paper we present an empirical Bayes analysis that handles both problems very well. Using different parametrizations, we develop four statistics that can be used to test hypotheses about the means and/or variances of the gene expression levels in both one- and two-sample problems. The methods are illustrated using experimental data with prior knowledge. In addition, we present the result of a simulation comparing our methods to well-known statistics and multiple testing adjustments.

Algorithms↗