Motivation: The human microbiome, which is linked to various diseases by growing evidence, has a profound impact on human health. Since changes in the composition of the microbiome across time are associated with disease and clinical outcomes, microbiome analysis should be performed in a longitudinal study. However, due to limited sample sizes and differing numbers of timepoints for different subjects, a significant amount of data cannot be utilized, directly affecting the quality of analysis results. Deep generative models have been proposed to address this lack of data issue. Specifically, a generative adversarial network (GAN) has been successfully utilized for data augmentation to improve prediction tasks. Recent studies have also shown improved performance of GAN-based models for missing value imputation in a multivariate time series dataset compared with traditional imputation methods. Results: This work proposes DeepMicroGen, a bidirectional recurrent neural network-based GAN model, trained on the temporal relationship between the observations, to impute the missing microbiome samples in longitudinal studies. DeepMicroGen outperforms standard baseline imputation methods, showing the lowest mean absolute error for both simulated and real datasets. Finally, the proposed model improved the predicted clinical outcome for allergies, by providing imputation for an incomplete longitudinal dataset used to train the classifier. Availability and implementation: DeepMicroGen is publicly available at https://github.com/joungmin-choi/DeepMicroGen.

Identification of single-cell subtypes is one of the fundamental processes required to understand a heterogeneous population composed of multiple cells, based on single-cell RNA sequencing data. Previously, cell subtype identification was mainly carried out by dimension reduction and clustering approaches that grouped cells with similar expressed profiles together. However, for high robustness to noises and systematic annotation of the subtype in each cell, supervised classification approaches have been widely used. Recently, deep neural network (DNN) models have been widely presented in various fields, including biology. By capturing the composite relationship between sample features and target outcomes, a DNN model enables significant performance improvements in biological data mining analyses. In this paper, we constructed a DNN model, called scDAE for single-cell subtype identification combined with representative feature extraction using a multilayer denoising autoencoder (DAE). The feature sets were learned by the DAE and were further tuned by fully connected layers using a softmax classifier. The model was compared against four state-of-the-art cell subtype identification methods and two conventional machine learning algorithms. From multiple tests, scDAE significantly outperformed competing methods especially on data sets having a large number of cell subtypes and noises. Extracted cell features from the proposed model were clearly clustered with respect to subtype. The results of the experiments indicated that our proposed model is effective in identifying single-cell subtypes and molecular signatures representative of each cell subtype. scDAE is publicly available at https://github.com/cbi-bioinfo/scDAE.

ML Framework for PPCPs fate in WWTPs.

Background The global spread of antibiotic resistance presents a significant threat to human, animal, and plant health. Metagenomic sequencing is increasingly being utilized to profile antibiotic resistance genes (ARGs) in various environments, but presently a mechanism for predicting future trends in ARG occurrence patterns is lacking. Capability of forecasting ARG abundance trends could be extremely valuable towards informing policy and practice aimed at mitigating the evolution and spread of ARGs. Results Here we propose ARGfore, a multivariate forecasting model for predicting ARG abundances from time-series metagenomic data. ARGfore extracts features that capture inherent relationships among ARGs and is trained to recognize patterns in ARG trends and seasonality. Conclusion ARGfore outperformed standard time-series forecasting methods, such as N-HiTS, LSTM, and ARIMA, exhibiting the lowest mean absolute percentage error when applied to different wastewater datasets. Additionally, ARGfore demonstrated enhanced computational efficiency, making it a promising candidate for a variety of ARG surveillance applications. The rapid prediction of future trends can facilitate early detection and deployment of mitigation efforts if necessary. ARGfore is publicly available at https://github.com/joungmin-choi/ARGfore.

The development of single-cell RNA sequencing (scRNA-seq) has enabled gene expression to be quantified at single-cell resolution. Such advancement is expected to solve important issues that bulk RNA sequencing could not fully answer, such as inferring cell population heterogeneity, genetic variability of cells, detecting rare cell types, accurately predicting cell states and their localization. However, analyzing such large scale data, especially when they are sampled at multiple time points, brings new challenges in data mining informative genes, compared to single snapshot samples. It becomes even more complicated when gene expression patterns are to be mined from time-series scRNA-seq datasets generated from multiple conditions, which will constitute a data with gene, condition and time dimensions. Here, we focused on detecting gene expression patterns that well capture the underlying biological differences between time-series scRNA-seq datasets of three different types of stem cells. The gene expression profile of 2,128 time-series scRNA-seq samples from long-term hematopoietic stem cells (LTHSC) and two of its progenitor cell types were analyzed using our framework.We have successfully detected condition specific feature genes that were able to achieve 90.03% classification accuracy between the three cell types. Investigating the genes and clusters detected by our framework, we found that cell cycle related genes showed significantly high variance between the three cell types. Such results and transcriptomic characters detected from our analysis were consistent with the original study. Collectively, the framework was able to successfully detect biological meaningful gene sets and expression patterns from multi-condition time-series scRNA-seq samples.

Accurate breast cancer subtype prediction is critical for precise diagnosis, treatment planning, and prognosis evaluation. Recent studies highlight the important role of epigenetic modifications in breast tumor, especially the potential of abnormal DNA methylation patterns as markers for distinct subtypes. However, developing a reliable model for subtype prediction based on DNA methylation profiles is challenging due to the scarcity of annotated dataset. This work proposes BCtypeFinder, a breast cancer subtype prediction framework that utilizes a domain adaptation network combined with semi-supervised learning to address batch effects. Our model leverages both labeled and unlabeled DNA methylation data to extract domain-invariant features while aligning subtype distributions across various datasets. BCtypeFinder outperforms current methods, showcasing superior classification performance across multiple test cases. Furthermore, we explored the effects of batch correction in BCtypeFinder, demonstrating its ability to remove batch-specific variations among patients of the same subtype, thus improving the robustness of the classifier. BCtypeFinder is publicly available at https://github.com/joungmin-choi/BCtypeFinder .