Ciclops

Cross-platform training In CLinical Outcome PredictionS (ciclops) is the winning algorithm in 2019 Malaria DREAM Challenge SubChallenge 2.

Ciclops performs transfer learning from one transcriptomic platform's samples to another.

Installation

Install this package via pip:

pip install ciclops

or clone this program to your local directory:

https://github.com/GuanLab/ciclops.git

Usage

python ciclops [-h] [--train_path TRAIN_PATH] [--valid_path VALID_PATH]
               [-m MODEL_TYPE] [--no_quantile] [--shap] [-n TOP_GENES]

Pipeline for building clinical outcome prediction models on training dataset and transfer learning on validation datasets.

optional arguments:
 -h, --help            show this help message and exit
 --train_path TRAIN_PATH
                       Path to your training data, in .csv format; includes sample names as first column and labels as last column
 --valid_path VALID_PATH
                       Path to your transfer validation data, in .csv format; includes sample names as first column and labels as last column
 -m MODEL_TYPE, --model_type MODEL_TYPE
                       Machine learning models to use:
                                   lgb: LightGBM;
                                   xgb: XGBoost;
                                   rf: Random Forest;
                                   gpr: Gaussian Process Regression;
                                   lr: Linear Regression;
                                   default: lgb
 --no_quantile         If specified, do not use quantile normalization.
 --shap                Conduct SHAP analysis on the training and validation set.
                       Only for use with LightGBM, XGBoost, and Random Forest.
 -n TOP_GENES, --top_genes TOP_GENES
                       If --shap is specified, indicate number of top genes from both training and validation sets that will be compared in post-SHAP analysis.
                       Default is 20.

It will generate the following folders:

./training/: preprocessed training datasets for model training and 10-fold cross validation

./validation/: validation dataset for transferring test

./params/: trained machine learning model parameters

./performance/: model performance in 10-fold cross validation and transferring test

./SHAP/: SHAP analysis results

References

• For the original paper, please refer to the Guan Lab's 2022 iScience paper: Machine learning for artemisinin resistance in malaria treatment across in vivo-in vitro platforms.
• STAR Protocol (TBD)
• External data for testing/example purposes:
  • Shaw, P.J. et al. (2015) ‘Plasmodium parasites mount an arrest response to dihydroartemisinin, as revealed by whole transcriptome shotgun sequencing (RNA-seq) and microarray study’, BMC Genomics. doi:10.1186/s12864-015-2040-0.
  • GSE59098
  • GSE151189