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.
Install this package via pip:
pip install ciclops
or clone this program to your local directory:
https://github.com/GuanLab/ciclops.git
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
- 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: Protocol for using Ciclops to build models trained on cross-platform transcriptome data for clinical outcome prediction
- 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