Genomics to Notebook's Projects
End to end RNA-seq quantification, differential expression, and gene set enrichment analysis using GenePattern
Quantify cell migration for circular migration regions
Demonstrates how to preprocess and analyze a large single-cell dataset, This version of the notebook includes all the Python and R code used for the analysis
A user-friend widget that accepts a Cytoscape file and visualizes the network
Perform differential expression.
Companion project to the publication A FZD7-specific antibody-drug conjugate induces solid tumor regression in preclinical models by Myan Do et al.
Documentation website for genomics 2 notebook (g2nb)
Widget that displays a FASTA file
Meta-package for installing all of the g2nb tools
Connect to the Galaxy platform within Jupyter notebooks.
Gene Set Enrichment Analysis (GSEA) Gene Set Enrichment Analysis (GSEA) identifies gene sets that are upnregulated or downregulated between two conditions/phenotypes. The GSEA method can be summarized as: Take gene expression data from two different types of samples (e.g., treated vs non-treated) and rank all genes according to their degree of differential expression between the phenotypes. Take a set of genes of interest (e.g., pathway, locus, etc.) and determine whether they are differentially expressed as a group (enriched) within the ranked gene expression data. Note: you can repeat this step for multiple gene sets 3. Determine the significance of the enrichment analysis score via a permutation test: randomly swap the gene-set labels of the data and repeat the test many times. For more detailed information, visit the GSEA website.
A best-practices template notebook for authoring GenePattern Notebook analyses.
Identify cancer subtype specific compounds using DiSCoVER (Hanaford et al, Clinical Cancer Research, 2016)
Sign in to JupyterHub using anonymous guest credentials
Cluster genes and/or samples agglomeratively, based on how close they are to one another.
Use RNA-seq data to cluster genes and/or samples agglomeratively, based on how close they are to one another.
g2nb theme for JupyterHub
Extension for Jupyter which integrates igv.js
A Jupyter file widget with improved upload capabilities
Issue tracking for the g2nb project
Notebook used for ITCR2019 by EFJ
A rich text editor for markdown cells in Jupyter
A theme extension for JupyterLab with the g2nb logo and colors
Perform Copy Number Variation Analysis on Illumina 450k/EPIC methylation array data.
Authenticate JupyterHub with multiple services
A framework for creating user-friendly widgets and tools in Jupyter
Code for notebook project publishing and sharing
Principal component analysis (PCA) is a method that reduces the dimensionality of a dataset while retaining most of its variation.
RNA-velocity analysis using Alevin, Scanpy, and scVelo
QC, preprocessing, clustering, and visualization of scRNA-seq data using Seurat.
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