DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis
Yang Y., Walker TM., Walker AS., Wilson DJ., Peto TEA., Crook DW., Shamout F., Arandjelovic I., Comas I., Farhat MR., Gao Q., Sintchenko V., van Soolingen D., Hoosdally S., Gibertoni Cruz AL., Carter J., Grazian C., Earle SG., Kouchaki S., Yang Y., Walker TM., Fowler PW., Clifton DA., Iqbal Z., Hunt M., Smith EG., Rathod P., Jarrett L., Matias D., Cirillo DM., Borroni E., Battaglia S., Ghodousi A., Spitaleri A., Cabibbe A., Tahseen S., Nilgiriwala K., Shah S., Rodrigues C., Kambli P., Surve U., Khot R., Niemann S., Kohl T., Merker M., Hoffmann H., Molodtsov N., Plesnik S., Ismail N., Thwaites G., Thuy Thuong TN., Ngoc NH., Srinivasan V., Moore D., Coronel DJ., Solano W., Gao GF., He G., Zhao Y., Ma A., Liu C., Zhu B., Laurenson I., Claxton P., Koch A., Wilkinson R., Lalvani A., Posey J., Gardy JJ., Werngren J., Paton N., Jou R., Wu M-H., Lin W-H., Ferrazoli L., de Oliveira RS., Paulo S., Zhu T., Clifton DA.
Abstract Motivation Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. Results We used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR_cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR_cluster captures lineage-related clusters in the latent space. Availability and implementation The details of source code are provided at http://www.robots.ox.ac.uk/∼davidc/code.php. Supplementary information Supplementary data are available at Bioinformatics online.