FRoGS

We introduced a form of "word2vec" for bioinformatics named Functional Representation of Gene Signature (FRoGS), where FRoGS vectors encode known human genes' functions. FRoGS has pretrained gene embeddings and can generate embeddings of gene signatures based on the embeddings of individual genes.

In our Nature Communications paper, we unveil how FRoGS outshines other models in predicting compound targets. FRoGS can enable many general machine-learning utilities as exemplified in our blog post.

Dependencies

• python 3.7.3
  
• numpy 1.17.3
  
• pandas 1.3.3
  
• scikit-learn 1.0.1
  
• tensorflow 2.0.0
  

Operating system

The software has been tested on the Rocky Linux 8.8 system.

Installation

Run the command to download the package:

git clone https://github.com/chenhcs/FRoGS.git

Using FRoGS embeddings to classify tissue specific genes and gene signatures

In this demonstration, we first showcase a simple application of FRoGS gene embeddings for the classification of tissue-specific genes. Run the command within the demo/ directory to initiate the demo:

python classifier.py

Within the demo/data/ directory, we have provided three gene lists, each containing tissue-specific genes (Entrez Gene IDs) associated with a specific tissue. In the script classifier.py, we assign a vector representation to each gene using our pre-trained FRoGS gene embeddings. Then a t-SNE plot is generated to visualize the clustering patterns of genes based on their vector representations. For comparison, we also employ one-hot encoding for each gene and utilize the t-SNE plot to visualize the genes. Clearly, there is no inherent similarity of tissue-specific genes in this alternate representation, as shown below.

Finally in this example, we build a random forest classifier to predict the tissue specificity of genes based on their FRoGS vector representations. The model achieves an accuracy of 80% (±5%), in contrast to the accuracy of 29% (±3%) achieved when the model is trained on one-hot encodings.

In the second example, FRoGS gene embeddings are used to produce vector representations for gene signatures acquired from three different tissues. These vector representations of gene signatures are then mapped onto a t-SNE plot to illustrate the clustering patterns of gene signatures originating from different tissues. For comparison, the sum of one-hot encodings of genes in each gene list is also used to represent the gene lists.

A random forest classifier is next trained on the representation of gene signatures to predict their tissue specificity. The model achieves a remarkable accuracy of 100% (±0.4%) when trained on the FRoGS representations. In contrast, the accuracy drops to 85% (±4%) when the model is trained on representations derived from one-hot gene encodings.

Execute the command below within the demo/ directory to run the demo:

python gene_sig_representation.py

Training models for predicting compound targets using L1000 gene signatures

This section describes how to use pretrained gene embeddings to train a model that predict target genes of compounds from L1000 perturbagen gene signatures. Example data are provided in the data folder. To do this, run the command python l1000_model.py within the src/ directory. Cross validation will be performed for a list of compounds that have target annotations. There are a number of arguments that need to be specified for the script. Run the command under the src directory to see these options:

python l1000_model.py -h

Input format

Before using the script, the directories of following files and parameters need to be specified.

1. Path to a file that lists the compounds for cross validation (--cpdlist_file)

2. Path to a file containing target annotations of compounds (--target_file)

   By default, we will use Broad's compound target annotations.

3. Path to a file containing L1000 gene signatures (--sig_file)

4. Perturbagen type of gene signatures to use (--perttype)

   Choose target gene signatures of shRNA perturbation or cDNA perturbation to use.

5. Gene embeddings learned from GO annotations (--emb_go)

   By default, this will be the gene embeddings already-trained from Gene Ontology annotations saved in the data folder.

6. Gene embeddings learned from archs4 gene expression experiments (--emb_archs4)

   By default, this will be the gene embeddings already-trained from gene lists derived from ARCHS4 gene expression experiments saved in the data folder.

7. Number of training epochs (--epochs)

8. Path to a directory to save the predicted target ranking lists (--outdir)

9. Path to a directory to save trained models (--modeldir)

Output

1. Learned model weights will be saved in the specified --modeldir

2. For each perturbagen type (shRNA or cDNA), a target ranking list will be saved in the specified --outdir for each compound in each cell line, named as compoundID@cellline_perturbagen.txt

   Each file contains three columns, the first column lists gene IDs, the second column indicates the rankings of genes, the last column indicates the predicted scores of the model for the compound gene pair. The higher the ranking, the more likely the gene will be targeted by the compound in the cell line.

Test pretrained models on other compounds

For each perturbagen type (shRNA or cDNA), three models trained on all the Broad's target annotations are provided in the saved_model directory. Run the following script under the src directory to reproduce the predictions for compounds without Broad's target annotations combining the three models.

python l1000_inference.py

Training gene embeddings and generate signature embeddings

This section describes how to train gene embeddings using your own data. To do this, run the gene_vec_model.py script. Again, example data are provided in the data folder. Run the command under the src directory to see the arguments that need to be specified:

python gene_vec_model.py -h

Input format

1. Type of data from which the gene embeddings learned (--datatype)

   Here we consider either go or archs4 data.

2. Path to a file containing GO term gene associations or ARCHS4 experiments gene associations (--association_file)

   A TAB separated file where each line starts with a GO/ARCHS4 category ID, following by the number of genes in the category, following by the genes associated with the category separated by comma.

3. Path to save the learned embeddings (--outfile)

Output

1. A gene embedding file will be saved as the specified --outfile.

   A comma separated file where each line indicates a gene embedding. The first item in the line is the gene ID, following by a list of float numbers which is the embedding representation of the gene.

Generate gene signature embeddings

Use the following command to run examples of generating gene signature embeddings from embeddings of individual genes:

python signature_embedding.py

Run logistic regression (LR) models for aggregating multiple predictions

The code for the LR model and the trained LR model for Model L, Q, LQ, NCI60, and PSP are provided in the LR_model directory.

Citation

If you find FRoGS is useful for your research, please cite the following paper:

Chen, H., King, F.J., Zhou, B. et al.
Drug target prediction through deep learning functional representation of gene signatures.
Nature Commun. 15, 1853 (2024). https://doi.org/10.1038/s41467-024-46089-y