README.md

idr-notebooks

A set of Python Notebooks to demonstrate how to access the images and metadata from the Image Data Resource (IDR), including the features and all of the descriptive tags.

Full access to IDR metadata and images is provided through the standard OMERO API, documentation for which can be found here, with the Python bindings found specifically here. The notebooks in this repository are meant to exemplify the use of that API in the context of the IDR, and the sort of queries that can be done. In particular, they show how to reproduce Figure 1 and Figure 2 of the paper.¹ They also make use of the scipy ecosystem, including pandas.

Running the notebooks

Running on cloud resources

Running in Docker

Alternatively, if you have Docker installed, you can use the repo2docker tool to run this repository as a local Docker instance:

$ git clone https://github.com/IDR/idr-notebooks
$ cd idr-notebooks
$ repo2docker .

Then follow the instructions that are printed after the Docker image is built. Depending on the permissions, you might have to run the command as an admin

Running locally

Finally, if you would like to install the necessary requirements locally, we suggest using conda:

You can for example install Anaconda https://www.anaconda.com/products/individual#Downloads

Create an environment:

$ git clone https://github.com/IDR/idr-notebooks
$ cd idr-notebooks
$ conda env create -n idr_env -f binder/environment.yml

and activate the newly created environment:

$ conda activate idr_env

The following steps are only required if you want to run the notebooks

• If you have Anaconda installed:
  • Start Jupyter from the Anaconda-navigator
  • Select the notebook you wish to run and select the Kernel>Change kernel>Python [conda env:idr_env]
• If Anaconda is not installed:
  • In the environment, install jupyter e.g. pip install jupyter
  • Add the virtualenv as a jupyter kernel i.e. ipython kernel install --name "idr_env" --user
  • Open jupyter notebook i.e. jupyter notebook and select the idr_env kernel or [conda env:idr_env] according to what is available

Notebook	Lang	Level	Description
Using Jupyter	Markdown	Intro	Getting a login account
IDR API example script	Py	Intro	Shows an example of using the web API to extract metadata from the IDR. Can also be launched locally.
Getting Started	Py	Intro	How to connect, some simple data access
Figure 1 Sample of Phenotypes	Py	IDR Paper	Reproduces Fig. 1 of the paper: downloads annotations from all screens and computes and plots some statistics
Gene Network	Py	IDR Paper	Reproduces Fig. 2 of the paper: downloads annotations from 3 screens,with a phenotype in common, queries StringDB for interactions and plots the resulting network. Uses a conversion table for orthologues and gene identifiers which was built offline using biomart (see article for more details). It uses Bokeh and NetworkX to display networks.
PCA analysis of Charm features	Py	Study details	Shows how to access the computed CHARM features using OMERO.tables, and performs some analysis on them, showing that single cell information can be accessed from generic tile-based features, without segmentation.
Rohn Phenotype Clustering	Py	Study details	Downloads annotations from idr0008-rohn-actinome, and performs some simple phenotypic clustering, building a figure, similar to Fig. 1 of the corresponding paper. Builds a gallery of thumbnails from images of several phenotypes.
Sysgro ROI Length	Py	Study details	Loads polygons which are linked to the images of idr0001-graml-sysgro and compares the length of cells labelled with a particular gene such as ASH2 versus the wild type.
Calculate Sharpness	Py	Example	Calculates sharpness of images and generates heatmaps.

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1: Available on bioRxiv under https://doi.org/10.1101/089359

2: If you don't yet have an account, notebooks can be viewed under https://github.com/IDR/idr-metadata or more completely under https://nbviewer.jupyter.org/github/idr/idr-notebooks/tree/master/