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covolutional neural network based coexpression analysis
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README.md

CNNC

Title: Deep learning for inferring gene relationships from single-cell expression data

Originally, CNNC is short for Convolutional neural network co-expression analysis. Co-expression is just one of the tasks CNNC can do, but the name (CNNC) derived from it looks very nice.

see https://www.pnas.org/content/early/2019/12/09/1911536116 for details.

date: 2019-07-07

1, CNNC

CNNC aims to infer gene-gene relationships using single cell expression data. For each gene pair, sc RNA-Seq expression levels are transformed into 32×32 normalized empirical probability function (NEPDF) matrices. The NEPDF serves as an input to a convolutional neural network (CNN). The intermediate layer of the CNN can be further concatenated with input vectors representing Dnase-seq and PWM data. The output layer can either have a single, three or more values, depending on the application. For example, for causality inference the output layer contains three probability nodes where p0 represents the probability that genes a and b are not interacting, p1 encodes the case that gene a regulates gene b, and p2 is the probability that gene b regulates gene a.

2, Pipelines

(a) Pipeline for TF-target, KEGG and Reactome edge predictions. Users only need to provide gene-pair candidate list. TF-tatget prediction is cell type specific. Here we provide the model for mESC TF prediction. Please use mESC expression data to generate mESC subset and then do following NEPDF generation and classification of training and test, and use the big scRNA-seq and bulk data to do pathway tasks. (b) Pipeline for a new task with the expression data we collected. Users need to provide gene-pair candidate list to generate NEPDF list and label list to train and test model. (c) Pipeline for a new task with the expression data users collect. Users need to provide gene-pair candidate list, their own expression data to generate NEPDF list, and label list to train and test model.

3, Data sources

3.1 scRNA-seq :

https://s3.amazonaws.com/mousescexpression/rank_total_gene_rpkm.h5

3.2 bone marrow drived macrophage scRNA-seq :

https://mousescexpression.s3.amazonaws.com/bone_marrow_cell.h5

3.3 bulk RNA-seq :

https://s3.us-east-2.amazonaws.com/mousebulkexprssion/mouse_bulk.h5

3.4 mesc single cell RNA-seq:

https://mousescexpression.s3.amazonaws.com/mesc_cell.h5

3.5 dendritic single cell RNA-seq:

https://mousescexpression.s3.amazonaws.com/dendritic_cell.h5

4, Code environment

Users need to install the latest python and all the modules required by the code.

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.

And Author uses theano as the Keras backend in python.

Author's GPU is GeForce GTX 1080. If the latest theano does not work, please try some older versions.

Although not necessary, we strongly recommend GPU acceleration and conda management for package, dependency and environment to save time. With conda, the total software, package module installation time in Python should be less than one hour.

5, Trained model for

(see folder for details)

5.1 KEGG Pathway prediction model

5.2 Reactome Pathway prediction model

5.3 GTRD bone TF prediction model

6, Train model for a new task

Users can define their own tasks by providing new expression data and (or) new gene pair labels.

7, Command lines for Trained model

7.1 step1, users need to provide gene pair list;

gene_pair_list is the list that contains gene pairs and their labels. format : 'GeneA GeneB ' or 'GeneA GeneB 0' such as mmukegg_new_new_unique_rand_labelx_sy.txt and mmukegg_new_new_unique_rand_labelx.txt in data folder. users also need to provide data_separation index_list which is a number list dividing gene_pair_list into small parts.

Here we use data separation index list to divide gene pairs into small data parts, and make sure that the gene pairs in each index inteval is completely isolated from others. We can evaluate CNNC's performance on only a small data part. If users do not want to specified separate data, they can just generate a index list to divide the data into N equal parts.

7.2 step2, use get_xy_label_data_cnn_combine_from_database.py to get gene pair NEPDF list;

Usage: python get_xy_data_cnn_combine_from_database.py bulk_gene_list.txt sc_gene_list.txt gene_pair_list data_separation_index_list bulk_expression_data sc_exprsssion_data 0

command line in author's linux machine :

python get_xy_label_data_cnn_combine_from_database.py /home/yey/CNNC-master/data/bulk_gene_list.txt /home/yey/CNNC-master/data/sc_gene_list.txt /home/yey/CNNC-master/data/mmukegg_new_new_unique_rand_labelx.txt /home/yey/CNNC-master/data/mmukegg_new_new_unique_rand_labelx_num_sy.txt /home/yey/sc_process_1/new_bulk_mouse/prs_calculation/mouse_bulk.h5 /home/yey/sc_process_1/rank_total_gene_rpkm.h5 0 

#################INPUT################################################################################################################################

#1, bulk_gene_list.txt is the list that converts bulk expression data gene set into gene symbol IDs. Format: 'gene symbol IDs\t bulk gene ID'

#2, sc_gene_list.txt is the list that converts sc expression data gene set into gene symbol IDs. Format: 'gene symbol IDs\t sc gene ID'. please notice that mesc single cell data is not from the big 40k data, so we proivde a new sc_gene_list.txt file for it.

#3, gene_pair_list is the list that contains gene pairs and their labels. format : 'GeneA GeneB'

#4, data_separation index_list is a number list that divides gene_pair_list into small parts

#Here we use data separation index list to divide gene pairs into small data parts, and make sure that the gene pairs in each index inteval is completely isolated from others. And we can evaluate CNNC's performance on only a small data part.

#if users do not need to separate data, they can just generate a index list to divide the data into N equal parts.

#5, bulk_expression_data it should be a hdf5 format. users can use their own data or data we provided.

#6, sc expression data it should be a hdf5 format. users can use their own data or data we provided.

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

If user does not have bulk (single cell) data, just put Nones for bulk_gene_list.txt (sc_gene_list.txt) and bulk_expression_data (sc expression data).

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

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

Here we use gene symbol information to align bulk, scRNA-seq and gene pair's gene sets. In our own data, scRNA-seq used entrez ID, bulk RNA-seq used ensembl ID, gene pair list used gene symbol ID, thus we used bulk_gene_list.txt and sc_gene_list.txt to convert all the IDs to gene symbols. Please also do IDs convertion for bulk and scRNA-seq data if users want to use their own expression data.

7.3 step3, use predict_no_y.py to do prediction;

Usage: python predict_no_y.py number_of_separation NEPDF_pathway number_of_categories model_pathway

command line in author's linux machine :

python predict_no_y.py  9 /home/yey/CNNC-master/NEPDF_data  3 /home/yey/CNNC-master/trained_models/KEGG_keras_cnn_trained_model_shallow.h5

(In the models folder are trained models for KEGG and Reactome database respectively)

8, Command lines for Train new model:

8.1 step1, users need to provide gene pair candidate list (the same to step1 in trained_model except the flag setting);

gene_pair_list is the list that contains gene pairs and their labels. format : 'GeneA GeneB 0' such as mmukegg_new_new_unique_rand_labelx_sy.txt and mmukegg_new_new_unique_rand_labelx.txt in data folder. users also need to provide data_separation index_list which is a number list dividing gene_pair_list into small parts

Here we use data separation index list to divide gene pairs into small data parts, and make sure that the gene pairs in each index inteval is completely isolated from others. And we can evaluate CNNC's performance on only a small data part.

If users do not need to separate data, they can just generate a index list to divide the data into N equal parts.

8.2 step2, use get_xy_label_data_cnn_combine_from_database.py to get gene pair NEPDF list and their labels (the same to step2 in trained_model except flag setting);

Usage: python get_xy_data_cnn_combine_from_database.py bulk_gene_list.txt sc_gene_list.txt gene_pair_list data_separation index list bulk expression data sc exprsssion data 1

command line in author's linux machine :

python get_xy_label_data_cnn_combine_from_database.py /home/yey/CNNC-master/data/bulk_gene_list.txt /home/yey/CNNC-master/data/sc_gene_list.txt /home/yey/CNNC-master/data/mmukegg_new_new_unique_rand_labelx.txt /home/yey/CNNC-master/data/mmukegg_new_new_unique_rand_labelx_num_sy.txt /home/yey/sc_process_1/new_bulk_mouse/prs_calculation/mouse_bulk.h5 /home/yey/sc_process_1/rank_total_gene_rpkm.h5 1 

#################INPUT################################################################################################################################

#1, bulk_gene_list.txt is the list that converts bulk expression data gene set into gene symbol IDs. Format: 'gene symbol IDs\t bulk gene ID'

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

#3, gene_pair_list is the list that contains gene pairs and their labels. format : 'GeneA GeneB 0'

#4, data_separation_index_list is a number list that divide gene_pair_list into small parts

#Here we use data separation index list to divide gene pairs into small data parts, and make sure that the gene pairs in each index inteval is completely isolated from others. And we can evaluate CNNC's performance on only a small data part.

#if users do not want to separate data, they can just generate a index list to divide the data into N equal parts.

#5, bulk_expression_data it should be a hdf5 format. users can use their own data or data we provided.

#6, sc_expression_data it should be a hdf5 format. users can use their own data or data we provided.

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

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

8.3 step3, use train_with_labels_three_foldx.py to train a new model with three-fold cross validation;

Usage python train_with_labels_three_foldx.py number_of_data_parts_divided NEPDF_pathway number_of_categories

command line in author's linux machine :

module load cuda-8.0 (it is to use GPU)
srun -p gpu --gres=gpu:1 -c 2 --mem=20Gb python train_with_labels_three_foldx.py 9 /home/yey/CNNC-master/NEPDF_data 3 > results.txt

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

It generates three cross_Validation folder whose name begins with YYYYY, in which keras_cnn_trained_model_shallow.h5 is the trained model

8.4 step4, use train_with_labels_wholedatax.py to train a new model with whole data;

Usage python train_with_labels_wholedata.py number_of_separation NEPDF_data_path num_of_categories

command line in author's linux machine :

module load cuda-8.0 using GPU
srun -p gpu --gres=gpu:1 -c 2 --mem=20Gb python train_with_labels_wholedatax.py 9 /home/yey/CNNC-master/NEPDF_data 3 > results_whole.txt

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

It generates a folder whose name begins with xwhole, in which keras_cnn_trained_model_shallow.h5 is the final trained model

8.5 step5, use predict_no_y.py to do prediction; (the same to # step3 in trained_model)

Usage: python predict_no_y.py number_of_separation NEPDF_data_pathway number_of_categories model_pathway

command line in author's linux machine :

python predict_no_y.py  9 /home/yey/CNNC-master/NEPDF_data  3 /home/yey/CNNC-master/train_new_model/xwhole_saved_models_T_32-32-64-64-128-128-512_e200/keras_cnn_trained_model_shallow.h5 (it is the newly trained model )

9 Attentions:

Please read the readme very carefully and make sure that all files have correct paths, since users may have different computer environments.

All command lines are just used for demo. If you want to run the real data, plz replace "mmukegg_new_new_unique_rand_labelx_num_sy.txt" with "mmukegg_new_new_unique_rand_labelx_num.txt", and replace all "9" with 3057, which is the real number of seperations.

When label list is very large, say more than 100,000 gene pairs, we recommend users to feed a series of small number_of_data_parts_divided to run the NEPDF generation in parallel.

We are exploring new tasks and new datatypes for CNNC, to be continued...

Enjoy our CNNC!!

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