All Projects → saezlab → cosmosR

saezlab / cosmosR

Licence: GPL-3.0 license
COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets.

Programming Languages

r
7636 projects
CSS
56736 projects

Projects that are alternatives of or similar to cosmosR

bacnet
BACNET is a Java based platform to develop website for multi-omics analysis
Stars: ✭ 12 (-60%)
Mutual labels:  proteomics, transcriptomics
R-Learning-Journey
Some of the projects i made when starting to learn R for Data Science at the university
Stars: ✭ 19 (-36.67%)
Mutual labels:  data-integration
bio2bel
A Python framework for integrating biological databases and structured data sources in Biological Expression Language (BEL)
Stars: ✭ 16 (-46.67%)
Mutual labels:  data-integration
scarches
Reference mapping for single-cell genomics
Stars: ✭ 175 (+483.33%)
Mutual labels:  data-integration
assignPOP
Population Assignment using Genetic, Non-genetic or Integrated Data in a Machine-learning Framework. Methods in Ecology and Evolution. 2018;9:439–446.
Stars: ✭ 16 (-46.67%)
Mutual labels:  data-integration
Awesome Single Cell
Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
Stars: ✭ 1,937 (+6356.67%)
Mutual labels:  data-integration
doctoral-thesis
📖 Generation and Applications of Knowledge Graphs in Systems and Networks Biology
Stars: ✭ 26 (-13.33%)
Mutual labels:  data-integration
OmicLearn
🧪 🖥 Transparent exploration of machine learning for biomarker discovery from proteomics and omics data
Stars: ✭ 46 (+53.33%)
Mutual labels:  proteomics
Rudder Server
Privacy and Security focused Segment-alternative, in Golang and React
Stars: ✭ 2,874 (+9480%)
Mutual labels:  data-integration
data-product-batch
Template to deploy a Data Product for Batch data processing into a Data Landing Zone of the Data Management & Analytics Scenario (former Enterprise-Scale Analytics). The Data Product template can be used by cross-functional teams to ingest, provide and create new data assets within the platform.
Stars: ✭ 27 (-10%)
Mutual labels:  data-integration
thymeflow
Installer for Thymeflow, a personal knowledge management system.
Stars: ✭ 27 (-10%)
Mutual labels:  data-integration
DataBridge.NET
Configurable data bridge for permanent ETL jobs
Stars: ✭ 16 (-46.67%)
Mutual labels:  data-integration
Hudi
Upserts, Deletes And Incremental Processing on Big Data.
Stars: ✭ 2,586 (+8520%)
Mutual labels:  data-integration
SchemaMapper
A .NET class library that allows you to import data from different sources into a unified destination
Stars: ✭ 41 (+36.67%)
Mutual labels:  data-integration
nomenklatura
Framework and command-line tools for integrating FollowTheMoney data streams from multiple sources
Stars: ✭ 158 (+426.67%)
Mutual labels:  data-integration
CogStack-NiFi
Building data processing pipelines for documents processing with NLP using Apache NiFi and related services
Stars: ✭ 22 (-26.67%)
Mutual labels:  data-integration
kuwala
Kuwala is the no-code data platform for BI analysts and engineers enabling you to build powerful analytics workflows. We are set out to bring state-of-the-art data engineering tools you love, such as Airbyte, dbt, or Great Expectations together in one intuitive interface built with React Flow. In addition we provide third-party data into data sc…
Stars: ✭ 474 (+1480%)
Mutual labels:  data-integration
Mara Pipelines
A lightweight opinionated ETL framework, halfway between plain scripts and Apache Airflow
Stars: ✭ 1,841 (+6036.67%)
Mutual labels:  data-integration
CommonCoreOntologies
The Common Core Ontology Repository holds the current released version of the Common Core Ontology suite.
Stars: ✭ 109 (+263.33%)
Mutual labels:  data-integration
bioc 2020 tidytranscriptomics
Workshop on tidytranscriptomics: Performing tidy transcriptomics analyses with tidybulk, tidyverse and tidyheatmap
Stars: ✭ 25 (-16.67%)
Mutual labels:  transcriptomics

cosmosR

R-CMD-check

Overview

COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets. COSMOS leverages extensive prior knowledge of signaling pathways, metabolic networks, and gene regulation with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. This pipeline can provide mechanistic explanations for experimental observations across multiple omic data sets.

COSMOS uses CARNIVAL’s Integer Linear Programming (ILP) optimization strategy to find the smallest coherent subnetwork causally connecting as many deregulated TFs, kinases/phosphatases and metabolites as possible. The subnetwork is extracted from a novel integrated PKN (available here) spanning signaling, transcriptional regulation and metabolism. Transcription factors activities are inferred from gene expression with DoRothEA, a meta resource of TF/target links. Kinase activities are inferred from phosphoproteomic with a kinase/substrate network of Omnipath, a meta resource of protein-protein. CARNIVAL was adapted to find mechanistic hypotheses connecting the TF and kinase activities with metabolites from a signaling/metabolic prior knowledge network combining Omnipath, STITCHdb and Recon3D.

You can also use COSMOS if you don't have metabolomic data, to connect TF activities (from transcriptomic) with kinase activities (from phosphoproteomic) for exmaple !

Installation

R >= 4.1 is required

# install from bioconductor
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("cosmosR")

# We advise to instal from github to get the latest version of the tool.
if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")
    
devtools::install_github("saezlab/cosmosR")

If you don't have R 4.1, you can also clone the github repository on your machine, create a new R project with R studio from the cosmosR folder, change the R version to your own R version in the DESCRIPTION file and then install it with devtools:install()

But 4.1 is advised in any case.

Prerequisites

COSMOS is dependent on CARNIVAL for exhibiting the signalling pathway optimisation. CARNIVAL requires the interactive version of IBM Cplex, Gurobi or CBC-COIN solver as the network optimiser. The IBM ILOG Cplex is freely available through Academic Initiative here. Gurobi license is also free for academics, request a license here. The CBC solver is open source and freely available for any user, but has a significantly lower performance than CPLEX or Gurobi. Obtain CBC executable directly usable for cosmos here. Alternatively for small networks, users can rely on the freely available lpSolve R-package, which is automatically installed with the package.

Small note to package dependencies:

CARNIVAL is currently under active development. We try to ensure the compatibility of both packages, please install CARNIVAL from the our Github:

# Install 
remotes::install_github("saezlab/CARNIVAL")

Tutorial (video)

We recorded a video guide for cosmosR tutorial in the context of a course organised by EBI-EMBL. You can access the recording at this link for a step by step introduction to cosmosR : https://embl-ebi.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=318f7091-b6bf-44ee-939f-adb10121fc1b

Tutorial (NCI60 playground)

We made a repository that contains pre-processed inputs and an example script to use cosmos with the NCI60 RNA+metabolomic datasets. You can find the repository here.

Access

The meta PKN used with the biorXiv version of COSMOS is available here.

An updated meta PKN is available with the package (using data(meta_network) in R)

Citation

If you use cosmosR for your research please cite the original publication:

Dugourd A, Kuppe C, Sciacovelli M, Gjerga E, Gabor A, Emdal KB, Vieira V, Bekker-Jensen DB, Kranz J, Bindels EMJ, Jesper V Olsen, Christian Frezza, Rafael Kramann, Julio Saez-Rodriguez et al (2021) Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses. Mol Syst Biol 17: e9730

License

The code is distributed under the GNU General Public License v3.0. The meta PKN is distributed under the Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0) License.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].