GithubHelp home page GithubHelp logo

jenna-tomkinson / benchmarking_nf1_data Goto Github PK

View Code? Open in Web Editor NEW

This project forked from wayscience/benchmarking_nf1_data

1.0 0.0 1.0 2.58 GB

License: Creative Commons Zero v1.0 Universal

Jupyter Notebook 98.40% Python 1.24% Shell 0.01% R 0.35%

benchmarking_nf1_data's Introduction

NF1 Schwann Cell Data Project

Data

The data used in this project is a modified Cell Painting assay on Schwann cells from patients with Neurofibromatosis type 1 (NF1). In this modified Cell Painting, there are three channels:

  • DAPI (Nuclei)
  • GFP (Endoplasmic Reticulum)
  • RFP (Actin)

Modified_Cell_Painting.png

There are two genotypes of the NF1 gene in these cells:

  • Wild type (WT +/+): In column 6 from the plate (e.g C6, D6, etc.)
  • Null (Null -/-): In column 7 from the plate (e.g C7, D7, etc.)

It is important to study Schwann cells from NF1 patients because NF1 causes patients to develop neurofibromas, which are red bumps on the skin (tumors) that appear due to the loss of Ras-GAP neurofibromin. This loss occurs when the NF1 gene is mutated (NF1 +/-).

Goal

The goal of this project is to predict NF1 genotype from Schwann cell morphology. We apply cell image analysis to Cell Painting images and use representation learning to extract morphology features. We will apply machine learning to the morphology features to discover a biomarker of NF1 genotype. Once we discover a biomarker from these cells, we hope that our method can be used for drug discovery to treat this rare disease.

Repository Structure

Module Purpose Description
0_download_data Download NF1 pilot data Download images from each of NF1 dataset (e.g. pilot and second plate) for analysis
1_preprocessing_data Perform Illumination Correction (IC) Use BaSiCPy to perform IC on images per channel
2_segmenting_data Segment Objects Perform segmentation using Cellpose and outputing center (x,y) coordinates for each object
3_extracting_features Extract features Use center (x,y) coordinates in DeepProfiler to extract features from all channels
4_processing_features Normalize CellProfiler features Use Pycytominer functions to merge and normalize features acquired from CellProfiler
CellProfiler_pipelines Perform a full pipeline on NF1 data using CellProfiler (from IC to feature extraction) We run two CellProfiler pipelines (1. illumination correction and 2. segmenation and feature extraction)
TBD TBD TBD

benchmarking_nf1_data's People

Contributors

jenna-tomkinson avatar gwaybio avatar

Stargazers

DorianD avatar

Forkers

xiuqij

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.