Table2_iPoLNG—An unsupervised model for the integrative analysis of single-cell multiomics data.XLS

Single-cell multiomics technologies, where the transcriptomic and epigenomic profiles are simultaneously measured in the same set of single cells, pose significant challenges for effective integrative analysis. Here, we propose an unsupervised generative model, iPoLNG, for the effective and scalable integration of single-cell multiomics data. iPoLNG reconstructs low-dimensional representations of the cells and features using computationally efficient stochastic variational inference by modelling the discrete counts in single-cell multiomics data with latent factors. The low-dimensional representation of cells enables the identification of distinct cell types, and the feature by factor loading matrices help characterize cell-type specific markers and provide rich biological insights on the functional pathway enrichment analysis. iPoLNG is also able to handle the setting of partial information where certain modality of the cells is missing. Taking advantage of GPU and probabilistic programming, iPoLNG is scalable to large datasets and it takes less than 15 min to implement on datasets with 20,000 cells.</p

DataSheet2_iPoLNG—An unsupervised model for the integrative analysis of single-cell multiomics data.PDF

Single-cell multiomics technologies, where the transcriptomic and epigenomic profiles are simultaneously measured in the same set of single cells, pose significant challenges for effective integrative analysis. Here, we propose an unsupervised generative model, iPoLNG, for the effective and scalable integration of single-cell multiomics data. iPoLNG reconstructs low-dimensional representations of the cells and features using computationally efficient stochastic variational inference by modelling the discrete counts in single-cell multiomics data with latent factors. The low-dimensional representation of cells enables the identification of distinct cell types, and the feature by factor loading matrices help characterize cell-type specific markers and provide rich biological insights on the functional pathway enrichment analysis. iPoLNG is also able to handle the setting of partial information where certain modality of the cells is missing. Taking advantage of GPU and probabilistic programming, iPoLNG is scalable to large datasets and it takes less than 15 min to implement on datasets with 20,000 cells.</p

DataSheet1_iPoLNG—An unsupervised model for the integrative analysis of single-cell multiomics data.ZIP

Single-cell multiomics technologies, where the transcriptomic and epigenomic profiles are simultaneously measured in the same set of single cells, pose significant challenges for effective integrative analysis. Here, we propose an unsupervised generative model, iPoLNG, for the effective and scalable integration of single-cell multiomics data. iPoLNG reconstructs low-dimensional representations of the cells and features using computationally efficient stochastic variational inference by modelling the discrete counts in single-cell multiomics data with latent factors. The low-dimensional representation of cells enables the identification of distinct cell types, and the feature by factor loading matrices help characterize cell-type specific markers and provide rich biological insights on the functional pathway enrichment analysis. iPoLNG is also able to handle the setting of partial information where certain modality of the cells is missing. Taking advantage of GPU and probabilistic programming, iPoLNG is scalable to large datasets and it takes less than 15 min to implement on datasets with 20,000 cells.</p

Table1_iPoLNG—An unsupervised model for the integrative analysis of single-cell multiomics data.XLSX

Single-cell multiomics technologies, where the transcriptomic and epigenomic profiles are simultaneously measured in the same set of single cells, pose significant challenges for effective integrative analysis. Here, we propose an unsupervised generative model, iPoLNG, for the effective and scalable integration of single-cell multiomics data. iPoLNG reconstructs low-dimensional representations of the cells and features using computationally efficient stochastic variational inference by modelling the discrete counts in single-cell multiomics data with latent factors. The low-dimensional representation of cells enables the identification of distinct cell types, and the feature by factor loading matrices help characterize cell-type specific markers and provide rich biological insights on the functional pathway enrichment analysis. iPoLNG is also able to handle the setting of partial information where certain modality of the cells is missing. Taking advantage of GPU and probabilistic programming, iPoLNG is scalable to large datasets and it takes less than 15 min to implement on datasets with 20,000 cells.</p

A novel location-routing problem in electric vehicle transportation with stochastic demands

This is the experimental data of the manuscript entitled "A novel location-routing problem in electric vehicle transportation with stochastic demands". All data are saved in the file with txt format. <br

Electric vehicle battery swap station location-routing problem with stochastic demands using hybrid variable neighborhood search

This is the experimental data of the manuscript entitled "Electric vehicle battery swap station location-routing problem with stochastic demands using hybrid variable neighborhood search". All data are saved in the file with txt format. </div

A fuzzy optimization model for the electric vehicle routing problem with time windows and recharging stations

This is the experimental data of the manuscript entitled "A fuzzy optimization model for the electric vehicle routing problem with time windows and recharging stations". All data are saved in the file with txt format

A novel location-routing problem in electric vehicle transportation with stochastic demands

This is the experimental data of the manuscript entitled "A novel location-routing problem in electric vehicle transportation with stochastic demands". All data are saved in the file with txt format

A novel location-routing problem in electric vehicle transportation with stochastic demands

This is the experimental data of the manuscript entitled "A novel location-routing problem in electric vehicle transportation with stochastic demands". All data are saved in the file with txt format

Fuzzy optimization model for electric vehicle routing problem with time windows and recharging stations

This is the experimental data of the manuscript entitled "fuzzy optimization model for electric vehicle routing problem with time windows and recharging stations". All data are saved in the file with txt format