Trying to tell a compelling story with numbers? Data visualization is an important way analysts and number-crunchers can convey results to many different audiences. In this presentation, David Kretch, a Consultant at Summit, shares some best practices for Data Visualization.
Racines en haut et feuilles en bas : les arbres en mathstuxette
1. The document discusses methods for clustering and differential analysis of Hi-C matrices, which represent the 3D organization of DNA.
2. It proposes extending Ward's hierarchical clustering to directly use Hi-C similarity matrices while enforcing adjacency constraints. A fast algorithm was also developed.
3. A new method called "treediff" was created to perform differential analysis of Hi-C matrices based on the Wasserstein distance between hierarchical clusterings. Software implementations of these methods were also developed.
Méthodes à noyaux pour l’intégration de données hétérogènestuxette
The document discusses a presentation about multi-omics data integration methods using kernel methods. The presentation introduces kernel methods, how they can be used to integrate heterogeneous omics data, and examples of applications. Specifically, it discusses using kernel methods to perform unsupervised transformation-based integration of multi-omics data. It also presents an application of constrained kernel hierarchical clustering to analyze Hi-C data by directly using Hi-C matrices as kernels.
Méthodologies d'intégration de données omiquestuxette
This document presents a presentation on multi-omics data integration methods given by Nathalie Vialaneix on December 13, 2023. The presentation discusses different types of omics data that can be integrated, both vertically across different levels of omics data on the same samples and horizontally across similar types of omics data on different samples. It also discusses different analysis approaches that can be taken, including supervised and unsupervised methods. The rest of the presentation focuses on unsupervised transformation-based integration methods using kernels.
The document discusses current and future work on analyzing Hi-C data and differential analysis of Hi-C matrices. It describes a clustering method developed to partition chromosomes based on Hi-C matrix similarity. It also introduces a new method called treediff for differential analysis of Hi-C data that calculates the distance between hierarchical clusterings. Current work includes reviewing differential analysis methods, investigating differential subtrees with multiple testing control, and inferring chromatin interaction networks.
Can deep learning learn chromatin structure from sequence?tuxette
This document discusses a deep learning model called ORCA that can predict chromatin structure from DNA sequence. The model uses a neural network with an encoder to extract features from sequence and a decoder to predict Hi-C matrices. It was trained on Hi-C data from multiple cell types and can predict interactions between regions at various resolutions. The model accurately captures features like CTCF-mediated loops and can predict effects of structural variants on chromatin structure. It allows for in silico mutagenesis to study how mutations may alter 3D genome organization.
Multi-omics data integration methods: kernel and other machine learning appro...tuxette
The document discusses multi-omics data integration methods, particularly kernel methods. It describes how kernel methods transform data into similarity matrices between samples rather than relying on variable space. Multiple kernel integration approaches are presented that combine multiple similarity matrices into a consensus kernel in an unsupervised manner, such as through a STATIS-like framework that maximizes the similarity between kernels. Examples of applications to datasets from the TARA Oceans expedition are given.
Trying to tell a compelling story with numbers? Data visualization is an important way analysts and number-crunchers can convey results to many different audiences. In this presentation, David Kretch, a Consultant at Summit, shares some best practices for Data Visualization.
Racines en haut et feuilles en bas : les arbres en mathstuxette
1. The document discusses methods for clustering and differential analysis of Hi-C matrices, which represent the 3D organization of DNA.
2. It proposes extending Ward's hierarchical clustering to directly use Hi-C similarity matrices while enforcing adjacency constraints. A fast algorithm was also developed.
3. A new method called "treediff" was created to perform differential analysis of Hi-C matrices based on the Wasserstein distance between hierarchical clusterings. Software implementations of these methods were also developed.
Méthodes à noyaux pour l’intégration de données hétérogènestuxette
The document discusses a presentation about multi-omics data integration methods using kernel methods. The presentation introduces kernel methods, how they can be used to integrate heterogeneous omics data, and examples of applications. Specifically, it discusses using kernel methods to perform unsupervised transformation-based integration of multi-omics data. It also presents an application of constrained kernel hierarchical clustering to analyze Hi-C data by directly using Hi-C matrices as kernels.
Méthodologies d'intégration de données omiquestuxette
This document presents a presentation on multi-omics data integration methods given by Nathalie Vialaneix on December 13, 2023. The presentation discusses different types of omics data that can be integrated, both vertically across different levels of omics data on the same samples and horizontally across similar types of omics data on different samples. It also discusses different analysis approaches that can be taken, including supervised and unsupervised methods. The rest of the presentation focuses on unsupervised transformation-based integration methods using kernels.
The document discusses current and future work on analyzing Hi-C data and differential analysis of Hi-C matrices. It describes a clustering method developed to partition chromosomes based on Hi-C matrix similarity. It also introduces a new method called treediff for differential analysis of Hi-C data that calculates the distance between hierarchical clusterings. Current work includes reviewing differential analysis methods, investigating differential subtrees with multiple testing control, and inferring chromatin interaction networks.
Can deep learning learn chromatin structure from sequence?tuxette
This document discusses a deep learning model called ORCA that can predict chromatin structure from DNA sequence. The model uses a neural network with an encoder to extract features from sequence and a decoder to predict Hi-C matrices. It was trained on Hi-C data from multiple cell types and can predict interactions between regions at various resolutions. The model accurately captures features like CTCF-mediated loops and can predict effects of structural variants on chromatin structure. It allows for in silico mutagenesis to study how mutations may alter 3D genome organization.
Multi-omics data integration methods: kernel and other machine learning appro...tuxette
The document discusses multi-omics data integration methods, particularly kernel methods. It describes how kernel methods transform data into similarity matrices between samples rather than relying on variable space. Multiple kernel integration approaches are presented that combine multiple similarity matrices into a consensus kernel in an unsupervised manner, such as through a STATIS-like framework that maximizes the similarity between kernels. Examples of applications to datasets from the TARA Oceans expedition are given.
This document provides an overview of the MetaboWean and Idefics projects. MetaboWean aims to study the co-evolution of gut microbiota and epithelium during suckling-to-weaning transition in rabbits, using metabolomics, metagenomics, and single-cell RNA sequencing data. Idefics integrates multiple omics datasets from human skin samples to understand relationships between microorganisms and molecules and how they are structured in patient groups. The datasets include metagenomics, metabolomics, and proteomics from host and microbiota.
Rserve, renv, flask, Vue.js dans un docker pour intégrer des données omiques ...tuxette
ASTERICS is an interactive and integrative data analysis tool for omics data. It uses Rserve and PyRserve with Flask and Vue.js in a Docker container to integrate omics data. The backend uses Rserve and PyRserve with Flask on the server side, while the frontend uses Vue.js. This architecture was chosen for its open source and light design. Data communication between Rserve and PyRserve is limited, requiring an object database. ASTERICS is deployed using three Docker containers for R, Python, and
Apprentissage pour la biologie moléculaire et l’analyse de données omiquestuxette
This document summarizes a scientific presentation about molecular biology and omics data analysis. The presentation covers topics related to analyzing large omics datasets using methods like kernel methods, graphical models, and neural networks to learn gene regulation networks and predict phenotypes. Key challenges addressed are handling big data, missing values, non-Gaussian data types like counts and compositional data. The goal is to better understand complex biological systems from multi-omics data.
Quelques résultats préliminaires de l'évaluation de méthodes d'inférence de r...tuxette
The document summarizes preliminary results from evaluating methods for inferring gene regulatory networks from expression data in Bacillus subtilis. It finds that recall of the known network is generally poor (<20% for random forest), but inferred clusters still retain biological information about common regulators. It plans to confirm results, test restricting edges to sigma factors, and explore other inference methods like Bayesian networks and ARACNE.
Intégration de données omiques multi-échelles : méthodes à noyau et autres ap...tuxette
The document discusses methods for integrating multi-scale omics data using kernel and machine learning approaches. It describes how omics data is large, heterogeneous, and multi-scaled, creating bottlenecks for analysis. Methods discussed for data integration include multiple kernel learning to combine different relational datasets in an unsupervised way. The methods are applied to integrate different datasets from the TARA Oceans expedition to identify patterns in ocean microbial communities. Improving interpretability of the methods and making them more accessible to biological users is discussed.
Journal club: Validation of cluster analysis results on validation datatuxette
This document presents a framework for validating cluster analysis results on validation data. It describes situations where clustering is inferential versus descriptive and recommends using validation data separate from the data used for clustering. A typology of validation methods is provided, including validation based on the clustering method or results, and evaluation using internal validation, external validation, visual properties, or stability measures.
The document discusses the differences between overfitting and overparametrization in machine learning models. It explores how random forests may exhibit a phenomenon known as "double descent" where test error initially decreases then increases with more parameters before decreasing again. While double descent has been observed in other models, the document questions whether it is directly due to model complexity in random forests since very large trees may be unable to fully interpolate extremely large datasets.
Selective inference and single-cell differential analysistuxette
This document discusses selective inference and single-cell differential analysis. It introduces the problem of "double dipping" in the standard single-cell analysis pipeline where the same dataset is used for clustering and differential analysis. Two approaches for addressing this are presented: 1) A method that perturbs clusters before testing for differences, and 2) A test based on a truncated distribution that assumes clusters and genes are given separately. Experiments applying these methods to real single-cell datasets are described. The document outlines challenges in extending these approaches to more complex analyses.
SOMbrero : un package R pour les cartes auto-organisatricestuxette
SOMbrero is an R package that implements self-organizing map (SOM) algorithms. It can handle numeric, non-numeric, and relational data. The package contains functions for training SOMs, diagnosing results, and plotting maps. It also includes tools like a shiny app and vignettes to aid users without programming experience. SOMbrero supports missing data imputation and extends SOM to relational datasets through non-Euclidean distance measures.
Graph Neural Network for Phenotype Predictiontuxette
This document describes a study on using graph neural networks (GNNs) for phenotype prediction from gene expression data. The objectives are to determine if including network information can improve predictions, which network types work best, and if GNNs can learn network inferences. It provides background on GNNs and how they generalize convolutional layers to graph data. The authors implemented a GNN model from previous work as a starting point and tested it on different network types to see which network information is most useful for predictions. Their methodology involves comparing GNN performance to other methods like random forests using 10-fold cross validation.
A short and naive introduction to using network in prediction modelstuxette
The document provides an introduction to using network information in prediction models. It discusses representing a network as a graph with a Laplacian matrix. The Laplacian captures properties like random walks on the graph and heat diffusion. Eigenvectors of the Laplacian related to small eigenvalues are strongly tied to graph structure. The document discusses using the Laplacian in prediction models by working in the feature space defined by the Laplacian eigenvectors or directly regularizing a linear model with the Laplacian. This introduces network information and encourages similar contributions from connected nodes. The approaches are applied to problems like predicting phenotypes from gene expression using a known gene network.
Kernel methods and variable selection for exploratory analysis and multi-omic...tuxette
Nathalie Vialaneix
4th course on Computational Systems Biology of Cancer: Multi-omics and Machine Learning Approaches
International course, Curie training
https://training.institut-curie.org/courses/sysbiocancer2021
(remote)
September 29th, 2021
This document summarizes different approaches for structure learning in graph neural networks. It discusses three main classes of methods: 1) metric-based learning which learns a similarity matrix between nodes, 2) probabilistic models which learn the parameters of a distribution over graphs, and 3) direct optimization which directly optimizes the graph adjacency matrix. The document provides examples of methods within each class and notes challenges such as the simplicity of probabilistic models and computational difficulties of direct optimization.
La statistique et le machine learning pour l'intégration de données de la bio...tuxette
This document summarizes a presentation on using statistics and machine learning for integrating high-throughput biological data. It discusses how biological data is large in volume, multi-scaled and heterogeneous in type, creating bottlenecks for analysis. It presents different methods for integrating multiple data tables, including multiple kernel learning to combine similarity matrices. An example application to TARA Oceans data is described, identifying Rhizaria abundance as structuring ocean differences. Interpretability of results is discussed along with prospects for deep learning and predicting phenotypes while understanding relationships.
This document summarizes and compares two popular Python libraries for graph neural networks - Spektral and PyTorch Geometric. It begins by providing an overview of the basic functionality and architecture of each library. It then discusses how each library handles data loading and mini-batching of graph data. The document reviews several common message passing layer types implemented in both libraries. It provides an example comparison of using each library for a node classification task on the Cora dataset. Finally, it discusses a graph classification comparison in PyTorch Geometric using different message passing and pooling layers on the IMDB-binary dataset.
RMarkdown allows combining Markdown documents with R code to produce dynamic documents. It was developed by RStudio as a way to write reproducible reports and analyses. Basic RMarkdown documents can be rendered to HTML, PDF, and other formats. Advanced options allow customizing appearance, adding parameters, and producing slides or books from RMarkdown.
Differential analyses of structures in HiC datatuxette
When Hi-C matrices are collected from two different conditions, methods can compare the matrices to identify regions with significant structural differences between conditions. TADpole and TADcompare are two available methods. TADpole represents hierarchical TAD structures and detects differences by computing a difference index between normalized binarized matrices. TADcompare represents Hi-C matrices as networks and uses the eigenvectors of the graph Laplacian and gap scores to define boundaries and detect differential boundaries between conditions. Both methods were shown to recover known breakpoints and have boundaries enriched for biological marks.
This document provides an overview of the MetaboWean and Idefics projects. MetaboWean aims to study the co-evolution of gut microbiota and epithelium during suckling-to-weaning transition in rabbits, using metabolomics, metagenomics, and single-cell RNA sequencing data. Idefics integrates multiple omics datasets from human skin samples to understand relationships between microorganisms and molecules and how they are structured in patient groups. The datasets include metagenomics, metabolomics, and proteomics from host and microbiota.
Rserve, renv, flask, Vue.js dans un docker pour intégrer des données omiques ...tuxette
ASTERICS is an interactive and integrative data analysis tool for omics data. It uses Rserve and PyRserve with Flask and Vue.js in a Docker container to integrate omics data. The backend uses Rserve and PyRserve with Flask on the server side, while the frontend uses Vue.js. This architecture was chosen for its open source and light design. Data communication between Rserve and PyRserve is limited, requiring an object database. ASTERICS is deployed using three Docker containers for R, Python, and
Apprentissage pour la biologie moléculaire et l’analyse de données omiquestuxette
This document summarizes a scientific presentation about molecular biology and omics data analysis. The presentation covers topics related to analyzing large omics datasets using methods like kernel methods, graphical models, and neural networks to learn gene regulation networks and predict phenotypes. Key challenges addressed are handling big data, missing values, non-Gaussian data types like counts and compositional data. The goal is to better understand complex biological systems from multi-omics data.
Quelques résultats préliminaires de l'évaluation de méthodes d'inférence de r...tuxette
The document summarizes preliminary results from evaluating methods for inferring gene regulatory networks from expression data in Bacillus subtilis. It finds that recall of the known network is generally poor (<20% for random forest), but inferred clusters still retain biological information about common regulators. It plans to confirm results, test restricting edges to sigma factors, and explore other inference methods like Bayesian networks and ARACNE.
Intégration de données omiques multi-échelles : méthodes à noyau et autres ap...tuxette
The document discusses methods for integrating multi-scale omics data using kernel and machine learning approaches. It describes how omics data is large, heterogeneous, and multi-scaled, creating bottlenecks for analysis. Methods discussed for data integration include multiple kernel learning to combine different relational datasets in an unsupervised way. The methods are applied to integrate different datasets from the TARA Oceans expedition to identify patterns in ocean microbial communities. Improving interpretability of the methods and making them more accessible to biological users is discussed.
Journal club: Validation of cluster analysis results on validation datatuxette
This document presents a framework for validating cluster analysis results on validation data. It describes situations where clustering is inferential versus descriptive and recommends using validation data separate from the data used for clustering. A typology of validation methods is provided, including validation based on the clustering method or results, and evaluation using internal validation, external validation, visual properties, or stability measures.
The document discusses the differences between overfitting and overparametrization in machine learning models. It explores how random forests may exhibit a phenomenon known as "double descent" where test error initially decreases then increases with more parameters before decreasing again. While double descent has been observed in other models, the document questions whether it is directly due to model complexity in random forests since very large trees may be unable to fully interpolate extremely large datasets.
Selective inference and single-cell differential analysistuxette
This document discusses selective inference and single-cell differential analysis. It introduces the problem of "double dipping" in the standard single-cell analysis pipeline where the same dataset is used for clustering and differential analysis. Two approaches for addressing this are presented: 1) A method that perturbs clusters before testing for differences, and 2) A test based on a truncated distribution that assumes clusters and genes are given separately. Experiments applying these methods to real single-cell datasets are described. The document outlines challenges in extending these approaches to more complex analyses.
SOMbrero : un package R pour les cartes auto-organisatricestuxette
SOMbrero is an R package that implements self-organizing map (SOM) algorithms. It can handle numeric, non-numeric, and relational data. The package contains functions for training SOMs, diagnosing results, and plotting maps. It also includes tools like a shiny app and vignettes to aid users without programming experience. SOMbrero supports missing data imputation and extends SOM to relational datasets through non-Euclidean distance measures.
Graph Neural Network for Phenotype Predictiontuxette
This document describes a study on using graph neural networks (GNNs) for phenotype prediction from gene expression data. The objectives are to determine if including network information can improve predictions, which network types work best, and if GNNs can learn network inferences. It provides background on GNNs and how they generalize convolutional layers to graph data. The authors implemented a GNN model from previous work as a starting point and tested it on different network types to see which network information is most useful for predictions. Their methodology involves comparing GNN performance to other methods like random forests using 10-fold cross validation.
A short and naive introduction to using network in prediction modelstuxette
The document provides an introduction to using network information in prediction models. It discusses representing a network as a graph with a Laplacian matrix. The Laplacian captures properties like random walks on the graph and heat diffusion. Eigenvectors of the Laplacian related to small eigenvalues are strongly tied to graph structure. The document discusses using the Laplacian in prediction models by working in the feature space defined by the Laplacian eigenvectors or directly regularizing a linear model with the Laplacian. This introduces network information and encourages similar contributions from connected nodes. The approaches are applied to problems like predicting phenotypes from gene expression using a known gene network.
Kernel methods and variable selection for exploratory analysis and multi-omic...tuxette
Nathalie Vialaneix
4th course on Computational Systems Biology of Cancer: Multi-omics and Machine Learning Approaches
International course, Curie training
https://training.institut-curie.org/courses/sysbiocancer2021
(remote)
September 29th, 2021
This document summarizes different approaches for structure learning in graph neural networks. It discusses three main classes of methods: 1) metric-based learning which learns a similarity matrix between nodes, 2) probabilistic models which learn the parameters of a distribution over graphs, and 3) direct optimization which directly optimizes the graph adjacency matrix. The document provides examples of methods within each class and notes challenges such as the simplicity of probabilistic models and computational difficulties of direct optimization.
La statistique et le machine learning pour l'intégration de données de la bio...tuxette
This document summarizes a presentation on using statistics and machine learning for integrating high-throughput biological data. It discusses how biological data is large in volume, multi-scaled and heterogeneous in type, creating bottlenecks for analysis. It presents different methods for integrating multiple data tables, including multiple kernel learning to combine similarity matrices. An example application to TARA Oceans data is described, identifying Rhizaria abundance as structuring ocean differences. Interpretability of results is discussed along with prospects for deep learning and predicting phenotypes while understanding relationships.
This document summarizes and compares two popular Python libraries for graph neural networks - Spektral and PyTorch Geometric. It begins by providing an overview of the basic functionality and architecture of each library. It then discusses how each library handles data loading and mini-batching of graph data. The document reviews several common message passing layer types implemented in both libraries. It provides an example comparison of using each library for a node classification task on the Cora dataset. Finally, it discusses a graph classification comparison in PyTorch Geometric using different message passing and pooling layers on the IMDB-binary dataset.
RMarkdown allows combining Markdown documents with R code to produce dynamic documents. It was developed by RStudio as a way to write reproducible reports and analyses. Basic RMarkdown documents can be rendered to HTML, PDF, and other formats. Advanced options allow customizing appearance, adding parameters, and producing slides or books from RMarkdown.
Differential analyses of structures in HiC datatuxette
When Hi-C matrices are collected from two different conditions, methods can compare the matrices to identify regions with significant structural differences between conditions. TADpole and TADcompare are two available methods. TADpole represents hierarchical TAD structures and detects differences by computing a difference index between normalized binarized matrices. TADcompare represents Hi-C matrices as networks and uses the eigenvectors of the graph Laplacian and gap scores to define boundaries and detect differential boundaries between conditions. Both methods were shown to recover known breakpoints and have boundaries enriched for biological marks.
2. Full vs simplified visualization
Framework: Static graph visualization.
Standard (FDP) approach: visualize the whole graph
aims at being aesthetic ⇒ tends to place the hubs in the center of the
figure (edges with uniform length); does not emphasize dense groups
Dagstuhl Seminar 12081 (2012/02/21) Graph visualization & clustering Nathalie Villa-Vialaneix 2 / 4
3. Full vs simplified visualization
Framework: Static graph visualization.
Simplified approach: find communities and represent each one by a
glyph
Dagstuhl Seminar 12081 (2012/02/21) Graph visualization & clustering Nathalie Villa-Vialaneix 2 / 4
4. Full vs simplified visualization
Framework: Static graph visualization.
Simplified approach: find communities and represent each one by a
glyph and investigate sub-structure by a hierarchical clustering
Dagstuhl Seminar 12081 (2012/02/21) Graph visualization & clustering Nathalie Villa-Vialaneix 2 / 4
7. Basic description
1 Search for communities: node clustering (e.g., modularity
optimization)
Is the clustering relevant / significant?
Possible answer: generate N random graphs with the same degree
distribution and compare the observed optimal modularity to the
optimal modularity distribution among the N random graphs
Dagstuhl Seminar 12081 (2012/02/21) Graph visualization & clustering Nathalie Villa-Vialaneix 3 / 4
8. Basic description
1 Search for communities: node clustering (e.g., modularity
optimization)
2 Iterate the clustering in each class in a hierarchical way.
When to stop the process? Is the clustering relevant /
significant?
Dagstuhl Seminar 12081 (2012/02/21) Graph visualization & clustering Nathalie Villa-Vialaneix 3 / 4
9. Basic description
1 Search for communities: node clustering (e.g., modularity
optimization)
2 Iterate the clustering in each class in a hierarchical way.
3 Visualize the graph (in a simplified way) at various levels of the
clustering hierarchy.
Dagstuhl Seminar 12081 (2012/02/21) Graph visualization & clustering Nathalie Villa-Vialaneix 3 / 4
10. Basic description
1 Search for communities: node clustering (e.g., modularity
optimization)
2 Iterate the clustering in each class in a hierarchical way.
3 Visualize the graph (in a simplified way) at various levels of the
clustering hierarchy.
How to have consistent representations? (a cluster and its
subclusters are approximately displayed at the same place) How to
take into account the space needed for a cluster of the last level of the
hierarchy in any representation (at any level)?
Dagstuhl Seminar 12081 (2012/02/21) Graph visualization & clustering Nathalie Villa-Vialaneix 3 / 4
11. Basic description
1 Search for communities: node clustering (e.g., modularity
optimization)
2 Iterate the clustering in each class in a hierarchical way.
3 Visualize the graph (in a simplified way) at various levels of the
clustering hierarchy.
How to have consistent representations? (a cluster and its
subclusters are approximately displayed at the same place) How to
take into account the space needed for a cluster of the last level of the
hierarchy in any representation (at any level)?
Possible solution: Recursively estimate the place needed for each
cluster in the hierarchy (by a circle encompassing the visualization of
all sub-clusters) ⇒ over-estimation
Dagstuhl Seminar 12081 (2012/02/21) Graph visualization & clustering Nathalie Villa-Vialaneix 3 / 4
12. Basic description
1 Search for communities: node clustering (e.g., modularity
optimization)
2 Iterate the clustering in each class in a hierarchical way.
3 Visualize the graph (in a simplified way) at various levels of the
clustering hierarchy.
Include information about the quality of the clustering in the
representation? (user warning)
Example: Color and weight edges
between clusters according to their
contribution to the modularity
Dagstuhl Seminar 12081 (2012/02/21) Graph visualization & clustering Nathalie Villa-Vialaneix 3 / 4
13. Open issues
• Clustering: what is a meaningful clustering? When to stop the
hierarchy?
• Clustering hierarchy representation: how to anticipate, at a given
level, the place needed for the representation of the finest levels?
• Including estimation about the clustering quality in the
representation: at the node level (“quality” of the clustering for the
cluster? What does that mean?) or at the edge level (contribution to
the modularity between clusters?)
Dagstuhl Seminar 12081 (2012/02/21) Graph visualization & clustering Nathalie Villa-Vialaneix 4 / 4