Stories
Read and share use cases, tutorials, articles and ideasConstellab Contest Guidelines
The Constellab Contest isn't just a competition; it's a movement towards open source innovation and collective knowledge in the life sciences. Are you passionate about pushing the boundaries of knowle...
Single-cell RNA-Seq Analysis Pipeline
In 2023, Batignes et al. published an article intitled "Enhanced Inflammatory Signaling Driven by Metabolic Switch in Aicardi-Goutières Syndrome". Aicardi-Goutières syndrome (AGS) is a genetic ...
PyDESeq2 for differential expression analysis (DEA) with bulk RNA-seq data
PyDESeq2, a Python implementation of the DESeq2 method originally developed in R (click here) , is a versatile tool for conducting differential expressi...
Crafting Metabolic Models: A Guide for Robustness
To delve into the study of digital twins, one requires a meticulously crafted metabolic model to facilitate in-depth analysis. Unfortunately, the current state of metabolic models lacks uniformity and...
Live tasks
Explore and share code snippets to empower collaborationMotif Search in DNA/Protein Sequences
The following livetask is designed to identify specific motifs within a set of DNA or Protein sequences provided in a FASTA format file.
Version :
Version 1
Task visibility :
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Version :
Version 1
Task visibility :
PyDESeq2 for differential expression analysis
This livetask utilizes the PyDESeq2 library for differential expression analysis of gene expression data. It takes as input a count table and metadata file
Version :
Version 1
Task visibility :
Convert gene names to gene IDs
This livetask streamlines the process of converting gene names to gene IDs by leveraging the MyGeneInfo API and pandas library in Python. It takes a list of gene names stored in a csv format as input.
Version :
Version 1
Task visibility :
Bricks
Explore and share Constellab libraries, access detailed documentationgws_gaia
GAIA (Gencovery Artificial Intelligence Analytics) provides core features for machine learning and deep learning modelling. It offers a broad range of AI algorithms, covering among others classification, regression, clustering methods, neural networks.
gws_ode
Brick for Ordinary Differential Equations
gws_stats
STATS (Statistical Tools Suite) provides core features for statistical analysis of biological data. It offers the most widely used statistical methods for biological data analysis to quantitatively assess your hypothesis from your data.
gws_scomix
This brick allows users to analyse scRNAseq datasets
Documentation
Product documentation
Learn how to use Constellab with a series of tutorial and videos
Technical documentation
Learn how to code in Constellab, from simple live task to fully integrated bricks
Icons
List of icons available in Constellab to style your task, resource and live tasks
