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Biomedical subjects

Chiara Sabatti

Publications and source records attributed to Chiara Sabatti.

7 recordsLinked to original sources
PubMed2026

Uncovering heterogeneous effects via localized feature selection.

Identifying features that interact to trigger disease, while accounting for heterogeneity across diverse populations, is essential for the development of precision and targeted medicine. Despite the availability of vast and complex health-related datasets, most existing works focus on identifying disease-associated features at the population level or within a few subpopulations, often overlooking individual-level heterogeneity within these groups. To address this limitation, we propose a framework that utilizes localized test statistics to identify disease-associated features tailored to individual profiles. Our method leverages the recently developed knockoffs methodology to control the noise level of the selection set so that the results are replicable. Moreover, it allows for the discovery of hidden heterogeneous effects within the data, as demonstrated in an application to single-cell RNA sequencing data for Alzheimer's disease. By aggregating localized feature selection results, our framework also enables powerful population-level feature selection. Our framework provides a powerful tool for exploratory studies of precision medicine, offering the potential to generate novel hypotheses for confirmatory biological experiments.

Alzheimer Disease↗
PubMed2003

False discovery rate in linkage and association genome screens for complex disorders.

We explore the implications of the false discovery rate (FDR) controlling procedure in disease gene mapping. With the aid of simulations, we show how, under models commonly used, the simple step-down procedure introduced by Benjamini and Hochberg controls the FDR for the dependent tests on which linkage and association genome screens are based. This adaptive multiple comparison procedure may offer an important tool for mapping susceptibility genes for complex diseases.

Bipolar Disorder↗
PubMed2002

Co-expression pattern from DNA microarray experiments as a tool for operon prediction.

The prediction of operons, the smallest unit of transcription in prokaryotes, is the first step towards reconstruction of a regulatory network at the whole genome level. Sequence information, in particular the distance between open reading frames, has been used to predict if adjacent Escherichia coli genes are in an operon. While appreciably successful, these predictions need to be validated and refined experimentally. As a growing number of gene expression array experiments on E.coli became available, we investigated to what extent they could be used to improve and validate these predictions. To this end, we examined a large collection of published microarry data. The correlation between expression ratios of adjacent genes was used in a Bayesian classification scheme to predict whether the genes are in an operon or not. We found that for the genes whose expression levels change significantly across the experiments in the data set, the currently available gene expression data allowed a significant refinement of the sequenced-based predictions. We report these co-expression correlations in an E.coli genomic map. For a significant portion of gene pairs, however, the set of array experiments considered did not contain sufficient information to determine whether they are in the same transcriptional unit. This is not due to unreliability of the array data per se, but to the design of the experiments analyzed. In general, experiments that perturb a large number of genes offer more information for operon prediction than confined perturbations. These results provide a rationale for conducting expression studies comparing conditions that cause global changes in gene expression.

Chromosome Mapping↗
PubMed2002

An evaluation of tyramide signal amplification and archived fixed and frozen tissue in microarray gene expression analysis.

Archival formalin-fixed, paraffin-embedded and ethanol-fixed tissues represent a potentially invaluable resource for gene expression analysis, as they are the most widely available material for studies of human disease. Little data are available evaluating whether RNA obtained from fixed (archival) tissues could produce reliable and reproducible microarray expression data. Here we compare the use of RNA isolated from human archival tissues fixed in ethanol and formalin to frozen tissue in cDNA microarray experiments. Since an additional factor that can limit the utility of archival tissue is the often small quantities available, we also evaluate the use of the tyramide signal amplification method (TSA), which allows the use of small amounts of RNA. Detailed analysis indicates that TSA provides a consistent and reproducible signal amplification method for cDNA microarray analysis, across both arrays and the genes tested. Analysis of this method also highlights the importance of performing non-linear channel normalization and dye switching. Furthermore, archived, fixed specimens can perform well, but not surprisingly, produce more variable results than frozen tissues. Consistent results are more easily obtainable using ethanol-fixed tissues, whereas formalin-fixed tissue does not typically provide a useful substrate for cDNA synthesis and labeling.

Animals↗
PubMed2002

Thresholding rules for recovering a sparse signal from microarray experiments.

We consider array experiments that compare expression levels of a high number of genes in two cell lines with few repetitions and with no subject effect. We develop a statistical model that illustrates under which assumptions thresholding is optimal in the analysis of such microarray data. The results of our model explain the success of the empirical rule of two-fold change. We illustrate a thresholding procedure that is adaptive to the noise level of the experiment, the amount of genes analyzed, and the amount of genes that truly change expression level. This procedure, in a world of perfect knowledge on noise distribution, would allow reconstruction of a sparse signal, minimizing the false discovery rate. Given the amount of information actually available, the thresholding rule described provides a reasonable estimator for the change in expression of any gene in two compared cell lines.

Animals↗
PubMed2002

Homozygosity and linkage disequilibrium.

We illustrate how homozygosity of haplotypes can be used to measure the level of disequilibrium between two or more markers. An excess of either homozygosity or heterozygosity signals a departure from the gametic phase equilibrium: We describe the specific form of dependence that is associated with high (low) homozygosity and derive various linkage disequilibrium measures. They feature a clear biological interpretation, can be used to construct tests, and are standardized to allow comparison across loci and populations. They are particularly advantageous to measure linkage disequilibrium between highly polymorphic markers.

Genetic Markers↗