Time-course Deep Learning (TDL) models for gene interaction inference

Title, Deep learning of gene interactions from single cell time-course expression data

1, TDL overview

Time-course deep learning (TDL) model architecture. To infer gene interactions (top left) we first convert time-course single cell expression data to a 3D tensor, which we term normalized empirical probability distribution function (NEPDF). Each 2D slice of the NEPDF captures the co-expression of a pair of genes at one of the time points profiled and the 3D NEPDF represents their co-expression over time. 3D NEPDF is then used as input to atime-course deep learning (TDL) model. The model is trained using labeled positive and negative pairs. The figure shows the convolutional Long short-term memory (LSTM) architecture which is one of the two TDL model we tested. This mode consists of two stacked LSTM layers, followed by a dense layer which concatenates all convolutional hidden state from LSTM layer and then a final output (classifica-tion) layer. See Supporting Figure S1 for the other TDL architecture we tested, 3D CNN.

2, Code environment

Users need to install python and ‘Keras’ and ‘theano’ modules, and all ohther modules required by the code. We recommend Anaconda to do this.

Author's environment is python 3.6.3 in a Linux server which is now running Centos 6.5 as the underlying OS and Rocks 6.1.1 as the cluster management revision.

3, Demo for running in interaction task

Firstly, gene pair plus label list and normalized time-course single cell gene expression data are provided by users to generated 3D NEDPF for each (gene pair, label). Secondly, a cross validation strategy is used to train and test our TDL models. Finally the model can predict new gene pair’s label.

Users should first figure out the right paths for all the scripts and datasets.

3.0 gene pair list generation from known gene pair set

Usage:

python gene_pair_set_generation.py <gene name reference list> <known gene pair list> <path of generated pair list> <path of generated pair num list>

command line in author's linux machine :

python gene_pair_set_generation.py /home/yey3/nn_project2/data/hesc_2_GSE75748_firstone/hesc2_gene_list_ref.txt /home/yey3/nn_project2/data/hesc_2_GSE75748_firstone/TF_target_prediction/hesc2_TF_target.txt /home/yey3/nn_project2/data/hesc_2_GSE75748_firstone/TF_target_prediction/hesc2_gene_pairs_400.txt /home/yey3/nn_project2/data/hesc_2_GSE75748_firstone/TF_target_prediction/hesc2_gene_pairs_400_num.txt

gene_pair_set_generation.py uses the konwn gene pair list to generate a gene pair list where konwn gene pair is positve sample and random pair is negative sample.

#1, gene name reference list is the list that converts sc expression data gene set into gene symbol IDs. Format: 'gene symbol IDs\t sc gene ID'.

#2, known pair list is the list that contains konwn gene pairs. format : 'GeneA GeneB'.

#3, path of generated pair list is the destination of the list that contains positive and negative gene pairs and their labels. format : 'GeneA GeneB label'.

#4, path of generated pair num list is the destination of a number list that divides gene_pair_list into small parts, where each part corresponds to one TF.

#################OUTPUT

It generates a gene pair list with positive and negative gene pairs, and also a gene pair number list to divide gene_pair_list into small parts, where each part corresponds to one TF.

Here we use gene symbol information to align scRNA-seq and gene pair's gene sets. The scRNA-seq may use entrez ID or ensembl ID, or other IDs, gene pair list for GTRD used gene symbol ID, thus we generated gene name reference list to convert all the IDs to gene symbols. Please also do IDs convertion if users want to use their own expression data.

The file path of 'data_path'in the following code is used in the author's own machine, so please change it to the approprite path according to user's own environment.

3.1 Training and test data generation

Usage:

python 3D_tensor_generation.py <None> <gene name reference list> <TF-target pair list> <TF-target pair count list> <None> <time-course scRNA-seq path+file prefix> <time point number> <flag if pair list has ground truth> <data generation folder name> 

command line in author's linux machine :

python 3D_tensor_generation.py None /home/yey3/nn_project2/data/hesc_2_GSE75748_firstone/hesc2_gene_list_ref.txt /home/yey3/nn_project2/data/hesc_2_GSE75748_firstone/TF_target_prediction/hesc2_gene_pairs_400.txt /home/yey3/nn_project2/data/hesc_2_GSE75748_firstone/TF_target_prediction/hesc2_gene_pairs_400_num.txt None /home/yey3/nn_project2/data/hesc_2_GSE75748_firstone/RPKM 6 1 GTRD_NT_8X8_6 

(where the path for the expession data is

'/home/yey3/nn_project2/data/hesc_2_GSE75748_firstone/RPKM_0.h5',

'/home/yey3/nn_project2/data/hesc_2_GSE75748_firstone/RPKM_1.h5'......)

3D_tensor_generation.py uses the normalized scRNA-seq expresson data to generate time-course normalized emperical PDF for each TF-candidate gene pair, and save it in <data generation folder name> folder.

#2, gene name reference list is the list that converts sc expression data gene set into gene symbol IDs. Format: 'gene symbol IDs\t sc gene ID'.

#3, TF-target pair list is the list that contains gene pairs and their labels. format : 'GeneA GeneB label'.

#4, TF-target pair count list is a number list that divides gene_pair_list into small parts, where each part corresponds to one TF.

#By file4, we are able to evaluate TDL's performance on only one TF.

#6, time-course scRNA-seq path+file prefix it should be a list of h5 format files, with name of 'prefix0.h5','prefix1.h5'.......

#7， flag, 0 means do not generate label list; 1 means to generate label list.

#8, data generation folder name is a new folder will be created to save generated train and test data.

#################OUTPUT

It generates a 3D NEPDF_data folder, and a series of data files containing Nxdata_tf (3D NEPDF file) ,ydata_TF(label file) and zdata_tf (gene symbol pair file) for each data part divided.

Here we use gene symbol information to align scRNA-seq and gene pair's gene sets. The scRNA-seq may use entrez ID or ensembl ID, or other IDs, gene pair list for GTRD used gene symbol ID, thus we generated gene name list to convert all the IDs to gene symbols. Please also do IDs convertion if users want to use their own expression data.

The file path of 'data_path'in the following code is used in the author's own machine, so please change it to the approprite path according to user's own environment.

3.2 Training and test 3D CNN model

python 3D_CNN.py

3D_CNN.py uses 3D NEPDF and ground truth list to train and test 3D CNN model in three fold cross validation. Please change 'data_path' accordingly.

3.3 Training and test LSTM model

 python conv_lstm_deep.py

conv_lstm_deep.py uses 3D NEPDF and ground truth list to train and test deep conv lstm models in three fold cross validation. Please change 'data_path' accordingly.

3.4 Get performance of optimal model for three fold cross validation

 python cross_validation_summary.py

cross_validation_summary.py collects and summrzies results of the model in each fold to present the final results. Please change 'data_path', 'save_dir', 'the path of trained model' accordingly.

3.5 Get prediction using the trained model.

 python conv_lstm_deep_prediction.py

Usage:

python conv_lstm_deep_prediction.py <model_path> <newNEPDF_path> 

command line in author's linux machine :

python conv_lstm_deep_prediction.py /home/yey3/nn_project2/data/hesc_2_GSE75748_firstone/TF_target_prediction/new_Xlr00001_KEGG_3d_conv_ddeeper_NT_p600_e100 /home/yey3/nn_project2/data/hesc_2_GSE75748_firstone/TF_target_prediction/GTRD_NT_8X8_6 

conv_lstm_deep_prediction.py uses the trained model to give prediction for new gene pair list. Please change 'data_path', 'save_dir', 'the path of trained model' accordingly.