NATMI

Network Analysis Toolkit for the Multicellular Interactions

Recent development of high throughput single-cell sequencing technologies has made it cost-effective to profile thousands of cells from a complex sample. Examining ligand and receptor expression patterns in the cell types identified from these datasets allows prediction of cell-to-cell communication at the level of niches, tissues and organism-wide. Here, we developed NATMI (Network Analysis Toolkit for Multicellular Interactions), a Python-based toolkit for multi-cellular communication network construction and network analysis of multispecies single-cell and bulk gene expression and proteomic data.

scMatch

A single-cell gene expression profile annotation tool using reference datasets

Massively multiplexed single-cell profiling has enabled large-scale transcriptional analyses of thousands of cells in complex tissues. In most cases, the true identity of individual cells is unknown and needs to be inferred from the transcriptomic data. Existing methods typically cluster (group) cells based on similarities of their gene expression profiles and assign the same identity to all cells within each cluster using the averaged expression levels. However, scRNA-seq experiments typically produce low-coverage sequencing data for each cell, which hinders the clustering process. We introduce scMatch, which directly annotates single cells by identifying their closest match in large reference datasets. We used this strategy to annotate various single-cell datasets and evaluated the impacts of sequencing depth, similarity metric and reference datasets. We found that scMatch can rapidly and robustly annotate single cells with comparable accuracy to another recent cell annotation tool (SingleR), but that it is quicker and can handle considerably larger reference datasets. We demonstrate how scMatch can handle large customized reference gene expression profiles that combine data from multiple sources, thus empowering researchers to identify cell populations in any complex tissue with the desired precision.

connectome

Web tool for exploring cell-cell ligand-receptor mediated communication networks

In Ramilowski et al. ‘A draft network of ligand-receptor mediated multicellular signaling in human’ 2015 we present the first large-scale map of cell-to-cell communication between 144 human primary cell types using 2,422 putative and literature supported ligand-receptor pairs. With up to hundreds of potential interactions between any two of these 144 primary cell types, there are millions of possible cell-cell communication paths across the entire network. Static visualization of such complex networks not only can be obscure and impractical but also difficult. With that, and to benefit the research community, we provide an online resource that visualizes, on demand, our cell-cell communication network for any given subset of the ligand-receptor pairs and profiled primary cells. An online version of the resource is located at: Ramilowski_et_al_2015 and mirrored at forrest-lab.github.io/connectome. We developed the online connectome visualization application using various open source and custom tools. The vector graphic visualization is generated using the D3.js visualization library. The application interface was developed using the AngularJS web application framework and the twitter bootstrap front-end framework. The visualization interface takes the the expression files generated in this study along with other metadata in tabular format Ramilowski_et_al_2015 to generate the network/hive visualization as shown in figure 5 in the paper.