DULoc-images

It's a deep-learning-based pattern unmixing pipeline for protein subcellular localization (DULoc) to quantitatively estimate the fractions of proteins localizing in different subcellular compartments from immunofluorescence images. The publication about this source code is 'DULoc: quantitatively unmixing protein subcel-lular location patterns in immunofluorescence images based on deep learning features'

1 part1 Dataset

Real_dataset can be accessed by 'http://murphylab.cbd.cmu.edu/software/2010_PNAS_Unmixing/'. The samples of synthetic dataset are shown in .\Synthetic_dataset\data, and the synthetic processing is in .\Synthetic_dataset\code.

2 part2 Bestfitting

The deep learning model, Bestfitting, can be obtained by ‘https://github.com/CellProfiling/HPA-competition-solutions/tree/master/bestfitting’. Run .\src\run\test.py to get the deep-CNN features. Then, run .\src\run\Real_dataset_mat.py or Synthetic_dataset_mat.py to get the input of unmixing methods (part3).

3 part3 Unmixing methods

Before running, replace '.\functions\RNNMF' in .\functions\do_RNNMF.mat with full path of 'RNNMF'. Run MasterSamples.m.

4 part4 Large-scale validation experiment

Run the codes in .\code step by step.