Integrating single-cell datasets with ambiguous batch information by incorporating molecular network features

scapGNN: A graph neural network-based framework for active pathway and gene module inference from single-cell multi-omics data

Although advances in single-cell technologies have enabled the characterization of multiple omics profiles in individual cells, extracting functional and mechanistic insights from such information remains a major challenge. Here, we present scapGNN, a graph neural network (GNN)-based framework that creatively transforms sparse single-cell profile data into the stable gene-cell association network for inferring single-cell pathway activity scores and identifying cell phenotype-associated gene modules from single-cell multi-omics data. Systematic benchmarking demonstrated that scapGNN was more accurate, robust, and scalable than state-of-the-art methods in various downstream single-cell analyses such as cell denoising, batch effect removal, cell clustering, cell trajectory inference, and pathway or gene module identification. scapGNN was developed as a systematic R package that can be flexibly extended and enhanced for existing analysis processes. It provides a new analytical platform for studying single cells at the pathway and network levels.

spliceJAC: transition genes and state-specific gene regulation from single-cell transcriptome data

Extracting dynamical information from single-cell transcriptomics is a novel task with the promise to advance our understanding of cell state transition and interactions between genes. Yet, theory-oriented, bottom-up approaches that consider differences among cell states are largely lacking. Here, we present spliceJAC, a method to quantify the multivariate mRNA splicing from single-cell RNA sequencing (scRNA-seq). spliceJAC utilizes the unspliced and spliced mRNA count matrices to constructs cell state-specific gene-gene regulatory interactions and applies stability analysis to predict putative driver genes critical to the transitions between cell states. By applying spliceJAC to biological systems including pancreas endothelium development and epithelial-mesenchymal transition (EMT) in A549 lung cancer cells, we predict genes that serve specific signaling roles in different cell states, recover important differentially expressed genes in agreement with pre-existing analysis, and predict new transition genes that are either exclusive or shared between different cell state transitions.

Single-cell profiling reveals molecular basis of malignant phenotypes and tumor microenvironments in small bowel adenocarcinomas

Small bowel adenocarcinomas (SBAs) are rare malignant tumors with a high mortality rate, and their molecular characteristics are still largely unexplored. Here we performed single-cell RNA sequencing for tumor samples from 12 SBA patients and predicted drug candidates for SBA. We identified four prevalent subtypes of malignant cells with distinct signatures including cell cycle program, mitochondria program, metabolism program and epithelial–mesenchymal transition (EMT) program. The progression relationships of these four subtypes of malignant cells were also revealed, which started from the cell cycle program, through the mitochondria program and then progressing into either the metabolism program or the EMT program. Importantly, ligand–receptor interaction pairs were found to be specifically enriched in pairs of EMT-program malignant cells and highly exhausted CD8+ T cells, suggesting that cancer cell subpopulations with EMT features may contribute most to the exhaustion of T cells. We also showed that the duodenal subtype of SBA exhibited molecular features more similar to gastric cancer whereas jejunal subtype of SBA more similar to colorectal cancer. Especially, we predicted specific drugs for SBA based on differential gene expression signatures between malignant cells and normal epithelial cells of SBA, and verified more potent inhibitory effects of volasertib and tozasertib for SBA cancer cells than conventional drugs of SBA at the same concentration, which provides new clues for treatments of SBA. In summary, our study provides a blueprint of the molecular signatures of both tumor cells and tumor microenvironment cells in SBA and reveals potential targets and drug candidates for its clinical treatments.

Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis

Background: Biological processes are based on complex networks of cells and molecules. Single cell multi-omics is a new tool aiming to provide new incites in the complex network of events controlling the functionality of the cell. Methods: Since single cell technologies provide many sample measurements, they are the ideal environment for the application of Deep Learning and Machine Learning approaches. An autoencoder is composed of an encoder and a decoder sub-model. An autoencoder is a very powerful tool in data compression and noise removal. However, the decoder model remains a black box from which is impossible to depict the contribution of the single input elements. We have recently developed a new class of autoencoders, called Sparsely Connected Autoencoders (SCA), which have the advantage of providing a controlled association among the input layer and the decoder module. This new architecture has the benefit that the decoder model is not a black box anymore and can be used to depict new biologically interesting features from single cell data. Results: Here, we show that SCA hidden layer can grab new information usually hidden in single cell data, like providing clustering on meta-features difficult, i.e. transcription factors expression, or not technically not possible, i.e. miRNA expression, to depict in single cell RNAseq data. Furthermore, SCA representation of cell clusters has the advantage of simulating a conventional bulk RNAseq, which is a data transformation allowing the identification of similarity among independent experiments. Conclusions: In our opinion, SCA represents the bioinformatics version of a universal “Swiss-knife” for the extraction of hidden knowledgeable features from single cell omics data.