Laura Cantini

Laura Cantini
Ecole Normale Supérieure de Paris | ENS · Département de Biologie

Research associate

About

53
Publications
7,371
Reads
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542
Citations
Additional affiliations
November 2018 - present
French National Centre for Scientific Research
Position
  • Chergé de recherche
January 2016 - October 2018
Institut Curie
Position
  • PostDoc Position
January 2013 - December 2015
Università degli Studi di Torino
Position
  • PhD Student

Publications

Publications (53)
Article
Full-text available
Motivation High-throughput single-cell molecular profiling is revolutionizing biology and medicine by unveiling the diversity of cell types and states contributing to development and disease. The identification and characterization of cellular heterogeneity is typically achieved through unsupervised clustering, which crucially relies on a similarit...
Article
Full-text available
Networks are powerful tools to represent and investigate biological systems. The development of algorithms inferring regulatory interactions from functional genomics data has been an active area of research. With the advent of single-cell RNA-seq data (scRNA-seq), numerous methods specifically designed to take advantage of single-cell datasets have...
Preprint
Full-text available
The recent advent of high-throughput single-cell molecular profiling is revolutionizing biology and medicine by unveiling the diversity of cell types and states contributing to development and disease. The identification and characterization of cellular heterogeneity is typically achieved through unsupervised clustering, which crucially relies on a...
Preprint
Full-text available
Optimal Transport (OT) defines geometrically meaningful "Wasserstein" distances, used in machine learning applications to compare probability distributions. However, a key bottleneck is the design of a "ground" cost which should be adapted to the task under study. In most cases, supervised metric learning is not accessible, and one usually resorts...
Article
Full-text available
High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve proper integration, joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR methods are available, urging the need for a comprehens...
Article
Full-text available
Insulin resistance decreases the ability of insulin to inhibit hepatic gluconeogenesis, a key step in the development of metabolic syndrome. Metabolic alterations, fat accumulation, and fibrosis in the liver are closely related and contribute to the progression of comorbidities, such as hypertension, type 2 diabetes, or cancer. Omega 3 (n-3) polyun...
Preprint
Full-text available
Networks are powerful tools to represent and investigate biological systems. The development of algorithms inferring regulatory interactions from functional genomics data has been an active area of research. With the advent of single-cell RNA-seq data (scRNA-seq), numerous methods specifically designed to take advantage of single-cell datasets have...
Article
Full-text available
English Wikipedia, containing more than five millions articles, has approximately eleven thousands web pages devoted to proteins or genes most of which were generated by the Gene Wiki project. These pages contain information about interactions between proteins and their functional relationships. At the same time, they are interconnected with other...
Preprint
Full-text available
High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve this multi-omics data integration, Joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR methods are available, urging the need f...
Article
Full-text available
Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveili...
Article
Full-text available
Aim: Growing evidence shows a strong interplay between post-transcriptional regulation, mediated by miRs and epigenetic regulation. Nevertheless, the number of experimentally validated miRs (called epi-miRs) involved in these regulatory circuitries is still very small. Material & methods: We propose a pipeline to prioritize candidate epi-miRs and t...
Article
Full-text available
Motivation Matrix factorization (MF) methods are widely used in order to reduce dimensionality of transcriptomic datasets to the action of few hidden factors (metagenes). MF algorithms have never been compared based on the between-datasets reproducibility of their outputs in similar independent datasets. Lack of this knowledge might have a crucial...
Article
Full-text available
Independent component analysis (ICA) is a matrix factorization approach where the signals captured by each individual matrix factors are optimized to become as mutually independent as possible. Initially suggested for solving source blind separation problems in various fields, ICA was shown to be successful in analyzing functional magnetic resonanc...
Article
Full-text available
Matrix factorization (MF) is an established paradigm for large-scale biological data analysis with tremendous potential in computational biology. Here, we challenge MF in depicting the molecular bases of epidemiologically described disease–disease (DD) relationships. As a use case, we focus on the inverse comorbidity association between Alzheimer’s...
Preprint
Full-text available
Matrix Factorization (MF) is an established paradigm for large-scale biological data analysis with tremendous potential in computational biology. We here challenge MF in depicting the molecular bases of epidemiologically described Disease-Disease (DD) relationships. As use case, we focus on the inverse comorbidity association between Alzheimer’s di...
Preprint
Full-text available
English Wikipedia, containing more than five millions articles, has approximately eleven thousands pages devoted to proteins or genes most of which were generated by the Gene Wiki project. These pages contain information about interactions between proteins and their functional relationships. At the same time, they are interconnected with other Wiki...
Article
Full-text available
After its introduction in 1982, the Hopfield model has been extensively applied for classification and pattern recognition. Recently, its great potential in gene expression patterns retrieval has also been shown. Following this line, we develop Hope4Genes a single-sample class prediction algorithm based on a Hopfield-like model. Differently from pr...
Conference Paper
Independent Component Analysis (ICA) can be used to model gene expression data as an action of a set of statistically independent hidden factors. The ICA analysis with a downstream component analysis was successfully applied to transcriptomic data previously in order to decompose bulk transcriptomic data into interpretable hidden factors. Some of t...
Preprint
Full-text available
Motivation Matrix factorization methods are widely exploited in order to reduce dimensionality of transcriptomic datasets to the action of few hidden factors (metagenes). Applying such methods to similar independent datasets should yield reproducible inter-series outputs, though it was never demonstrated yet. Results We systematically test state-o...
Article
Full-text available
MicroRNAs play important roles in many biological processes. Their aberrant expression can have oncogenic or tumor suppressor function directly participating to carcinogenesis, malignant transformation, invasiveness and metastasis. Indeed, miRNA profiles can distinguish not only between normal and cancerous tissue but they can also successfully cla...
Preprint
Full-text available
MicroRNAs play important roles in many biological processes. Their aberrant expression can have oncogenic or tumor suppressor function directly participating to carcinogenesis, malignant transformation, invasiveness and metastasis. Indeed, miRNA profiles can distinguish not only between normal and cancerous tissue but they can also successfully cla...
Preprint
Full-text available
Cancer is a complex and heterogeneous disease. It is crucial to identify the key driver genes and their role in cancer mechanisms with attention to different cancer stages, types or subtypes. Cancer driver genes are elusive and their discovery is complicated by the fact that the same gene can play a diverse role in different contexts. Key biologica...
Article
Gene signatures are more and more used to interpret results of omics data analyses but suffer from compositional (large overlap) and functional (correlated read-outs) redundancy. Moreover, many gene signatures rarely come out as significant in statistical tests. Based on pan-cancer data analysis, we construct a restricted set of 962 signatures defi...
Article
Full-text available
Background Independent Component Analysis (ICA) is a method that models gene expression data as an action of a set of statistically independent hidden factors. The output of ICA depends on a fundamental parameter: the number of components (factors) to compute. The optimal choice of this parameter, related to determining the effective data dimension...
Preprint
Full-text available
Background Independent Component Analysis (ICA) is a method that models gene expression data as an action of a set of statistically independent hidden factors. The output of ICA depends on a fundamental parameter: the number of components (factors) to compute. The optimal choice of this parameter, related to determining the effective data dimension...
Article
MicroRNAs (miRNAs) are small non-coding RNAs playing an essential role in gene expression regulation. Multiple studies have demonstrated that miRNAs are dysregulated in cancer initiation and progression, pointing out their potential as biomarkers for diagnosis, prognosis and response to treatment. With the introduction of high-throughput technologi...
Preprint
Large collections of gene signatures play a pivotal role in interpreting results of omics data analysis but suffer from compositional (large overlap) and functional (redundant read-outs) redundancy, and many gene signatures rarely pop-up in statistical tests. Based on pan-cancer data analysis, here we define a restricted set of 962 so called inform...
Thesis
Full-text available
Cancer is a complex disease involving progressive accumulation of molecular alterations by neoplastic cells. During the last decade, systematic assessment of these alterations has been carried out through genomic technologies. However, we still have not succeeded in translating this wealth of information into actionable knowledge about disease path...
Article
Full-text available
Colorectal cancer (CRC) transcriptional subtypes have been recently identified by gene expression profiling. Here we describe an analytical pipeline, microRNA master regulator analysis (MMRA), developed to search for microRNAs potentially driving CRC subtypes. Starting from a microRNA-mRNA tumour expression data set, MMRA identifies candidate regul...
Data
List of microRNAs differentially expressed in each subtype. For each subtype of each classfier, the table reports differential microRNAs and whether they are up-regulated or down-regulated.
Data
A double filter for significant regulation, based on t-test (p< 0.05) and fold-change (+/-1.5x), was applied to the 14927 genes expressed in SW403 cells, to identify mRNAs modulated by the various microRNA silencers. The total stem genes mapped on this dataset were 114 and 245 , respectively Up and Down in the Stem subtype (See online methods for f...
Data
Supplementary Figures 1-2, Supplementary Tables 1-6, Supplementary Note and Supplementary References.
Data
A double filter for significant regulation, based on t-test (p< 0.05) and fold-change (+/- 1.5x), was applied to the 14345 genes expressed in HT-29 cells, to identify mRNAs modulated by the various microRNA silencers. The total stem genes mapped on this dataset were 185 and 230, respectively Up and Down in the Stem subtype (See online methods for f...
Data
A double filter for significant regulation, based on t-test (p<0.05) and fold-change (+/-1.5x), was applied to the 14345 genes expressed in HT-29 cells, to identify mRNAs modulated by the various microRNA silencers. The total stem genes mapped on this dataset were 122 and 246, respectively Up and Down in the Stem subtype (See online methods for fur...
Article
Full-text available
We propose a new multiplex-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multiplex networks that we consider combine transcription factor co-targeting, microRNA co-targeting, protein-protein interaction and gene co-expression networks. The rationale behind...
Article
A stochastic model of intracellular calcium oscillations is analytically studied. The governing master equation is expanded under the linear noise approximation and a closed prediction for the power spectrum of fluctuations analytically derived. A peak in the obtained power spectrum profile signals the presence of stochastic, noise induced, oscilla...
Article
The problem of pattern formation in a generic two species reaction-diffusion model is studied, under the hypothesis that only one species can diffuse. For such a system, the classical Turing instability cannot take place. At variance, by working in the generalized setting of a stochastic formulation to the inspected problem, spatially organized pat...
Article
Full-text available
The problem of pattern formation in a generic two species reaction--diffusion model is studied, under the hypothesis that only one species can diffuse. For such a system, the classical Turing instability cannot take place. At variance, by working in the generalized setting of a stochastic formulation to the inspected problem, Turing like patterns c...

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Projects

Project (1)
Project
Single-cell data constitute a major breakthrough in life sciences. Their integration will enable us to investigate outstanding biological and medical questions thus far inaccessible. However, still few methods exist to integrate different single-cell modalities, corresponding to omics data (e.g. DNA methylation, proteome, chromatin accessibility), plus spatial positioning and images. Single-cell multi-modal integration requires novel computational developments to overcome the numerous intrinsic challenges of single-cell data and exploit their richness. Our goal is to develop dimensionality reduction and network-based approaches enabling the integration of multi-modal single-cell data. We will convert the resulting methods in open-source algorithms. These methodological developments will be guided by two applications in cancer research aiming at: assessing the heterogeneity of colorectal cancer subtypes and delineating markers and resistance mechanisms for a-PD1 treatment in metastatic melanoma. Our interdisciplinary project will impact several research fields, including clinics, precision medicine and all the biological fields provided with multi-omics profiles.