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Benchmarking data processing strategies for Cell Painting data of NF1 Schwann cells. See analysis repository (https://github.com/WayScience/NF1_SchwannCell_data_analysis) for information on how the data was interpreted.

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WayScience/Benchmarking_NF1_data

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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

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Benchmarking data processing strategies for Cell Painting data of NF1 Schwann cells. See analysis repository (https://github.com/WayScience/NF1_SchwannCell_data_analysis) for information on how the data was interpreted.

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