An interpretable deep-learning architecture of capsule networks for identifying cellular-type gene expression programs from single-cell RNA-seq data

This repository contains the official Keras implementation of:

An interpretable deep-learning architecture of capsule networks for identifying cellular-type gene expression programs from single-cell RNA-seq data

Requirements

• Python 3.6
• conda 4.4.10
• keras 2.2.4
• tensorflow 1.11.0
• establish the environment through code "conda create -n sccaps python=3.6 keras=2.2.4 tensorflow=1.11.0"

1. Model training

To unzip the PBMC_data.rar into dictionary 'data' then run:

• About this article

#- default:
python Model_Training.py

#Augments:
#'--inputdata', type=str, default='data/PBMC_data.npy', help='address for input data')
#'--inputcelltype', type=str, default='data/PBMC_celltype.npy', help='address for celltype label')
#'--num_classes', type=int, default=8, help='number of cell type')
#'--randoms', type=int, default=30, help='random number to split dataset')

#- with augments
python Model_Training.py --randoms 20

• Further Explore

#- default:
python FE_Model_Training.py

#Augments:
#'--inputdata', type=str, default='data/PBMC_data.npy', help='address for input data'
#'--inputcelltype', type=str, default='data/PBMC_celltype.npy', help='address for celltype label'
#'--num_classes', type=int, default=8, help='number of cell type'
#'--randoms', type=int, default=30, help='random number to split dataset into training and testing set'
#'--dim_capsule', type=int, default=16, help='dimension of the capsule'
#'--num_capsule', type=int, default=16, help='number of the primary capsule'
#'--batch_size', type=int, default=400, help='training parameters batch_size'
#'--epochs', type=int, default=10, help='training parameters epochs'
#'--Model_weights', type=str, default='Modelweight.weights', help='Model_weight'


#- with augments
python FE_Model_Training --randoms 20 --dim_capsule 32

2. Model analysis

• Demo -- About this article

The following documents (Jupyter Notebook) contain the codes for reproducing Figure in the main text.

demo_reproducing_figures.ipynb

In case you don't comfortable with Jupyter Notebook, you could copy the following file from the dictionary 'old_files' into current dictionary then run:

python Fig1CD_Fig2DE_Fig3.py
python Fig2BC.py
python Fig4.py
python Fig5D.py

• Further Explore

#- 1. heatmap for coupling coefficients, relationship between primary capsule and type capsule

python FE_Model_analysis_1.py

#Augments:
#'--inputdata', type=str, default='data/PBMC_data.npy', help='address for input data'
#'--inputcelltype', type=str, default='data/PBMC_celltype.npy', help='address for celltype label'
#'--num_classes', type=int, default=8, help='number of cell type'
#'--randoms', type=int, default=30, help='random number to split dataset'
#'--dim_capsule', type=int, default=16, help='dimension of the capsule'
#'--num_capsule', type=int, default=16, help='number of the primary capsule'
#'--weights', type=str, default='Modelweight.weights', help='trained weights'

#Output:
FE_Model_analysis_1_heatmap.png
FE_Model_analysis_1_overall_heatmap.png

#- 2. select genes along one principle component for a specific primary capsule according to the heatmap

python FE_Model_analysis_2.py

#Augments:
#'--inputdata', type=str, default='data/PBMC_data.npy', help='address for input data'
#'--inputcelltype', type=str, default='data/PBMC_celltype.npy', help='address for celltype label'
#'--num_classes', type=int, default=8, help='number of cell type'
#'--randoms', type=int, default=30, help='random number to split dataset'
#'--dim_capsule', type=int, default=16, help='dimension of the capsule'
#'--num_capsule', type=int, default=16, help='number of the primary capsule'
#'--weights', type=str, default='Modelweight.weights', help='trained weights'
#'--PC', type=int, default=1, help='indicate which principle component will be analyzed '
#'--Primary_capsule', type=int, default=4, help='indicate which primary capsule will be analyzed'
#'--Cell_type', type=int, default=-1, help='indicate which cell type will be analyzed'

#Output:
Max2Min_genes.npy
Min2Max_genes.npy
FE_Model_analysis_2_resutls.png

3. Comparison Model

• SVM,RF,LDA,KNN

cd comparison_model
python Machine_Learning.py

• Neural Networks

cd comparison_model
python Neural_Networks.py

capsule networks implementation

the capsule parts refer to https://github.com/bojone/Capsule and https://kexue.fm/archives/5112