DrSim: Similarity learning for transcriptional phenotypic drug discovery

Introduction

DrSim is a learning-based framework that automatically infers similarity for drug discovery from transcriptional perturbation data. Traditionally, such similarity has been defined in an unsupervised way. But due to the high dimensionality and the existence of high noise in thses perturbation data, they lack robustness with limited performance. DrSim significantly outperforms the existing methods on publicly available in vitro and in vivo datasets in drug annotation and repositioning scenario.

Dependencies

Required Software:

• sklearn
• numpy
• pandas
• scipy

Installation

Install via docker, highly recommended

Docker image of DrSim is available at https://hub.docker.com/r/bm2lab/drsim/. if you have docker installed, you call pull the image:

docker pull bm2lab/drsim

Install from github

git clone https://github.com/bm2-lab/DrSim.git  

Usage

DrSim can be applied for:
Drug annotation:

python  DrugAno.py --help
python  DrugAno.py  -ref  DrugAnoRef.h5   -query  query.tsv

Drug repositioning

python  DrugRep.py  --help
python  DrugRep.py   -ref  DrugRepRef.h5  -query  query.tsv

User Manual

For detailed information about usage, data preparation, input, output files and example files, please refer to the DrSim User Manual. The reference signatures in LINCS are available at onedrive. The example query signatures generated in TCGA in vivo data are available at onedrive.

DrSim flowchart

DrSim comprises three steps: data preprocessing, model training and similarity calculation

• (i) In the first step, only signatures treated by compounds for 6H or 24H in the nine human cancer cell lines are retained. The retained signatures are then split into subsets according to the cell type and time point attributes.
• (ii) In the second step, DrSim automatically infers a similarity for query assignment based on the training reference signatures. First, PCA is applied to the reference signatures to denoise and reduce dimensionality. A transformation matrix P is obtained. Second, by applying LDA to the dimensionality-reduced signatures, a transformation matrix L is learned. The label of a signature is the compound that induced the signature. Finally, the transformed references denoted as TR belonging to the identical compound are median centered to derive the transformed median centered references (denoted as TMR). The transformed references TR is calculated using Eq. 1.
• (iii) In the third step, given a query signature, after transformation by P and L, its similarities to TMR are calculated by cosine similarity.

Citation:

Similarity learning for transcriptional phenotypic drug discovery, submitted, 2021.

Contact

Zhiting Wei 1632738@tongji.edu.cn
Qi Liu qiliu@tongji.edu.cn
Tongji University, Shanghai, China