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Single Cell RNA-seq Data Portals

Resource	Description	Source/Reference
DVEX	Drosophila Virtual Expression eXplorer (DVEX) is an online resource tool which offers an easy way to explore the transcriptome of the stage 6 Drosophila embryo at the single cell level.	MDC BIMSB Berlin, Germany Karaiskos et al.
DRscDB	DRscDB was built based on the information curated from Drosophila scRNA-seq publications and selected publications from other major model organisms (zebrafish, mouse and human) relevant to the tissue types that are common among the species chosen. Users can mine and compare the gene expression profiles at single-cell level across studies, tissues and species for any input gene. Users can also use DRscDB to analyze an input gene list, looking for marker genes enriched in tissues and cell types based in the same or other species. This makes it possible to compare different datasets across studies, tissues and species, and can facilitate cell type assignment for clusters identified from newly obtained scRNA-seq datasets.	DRSC, Harvard Medical School Boston, MA, USA
SCEA	Single Cell Expression Atlas (SCEA) is an open science bioinformatics resource that provides free access to gene expression data generated in experiments performed in different laboratories around the world at single cell resolution. The SCEA reprocesses raw scRNA-Seq data in a standardised way, in-house, and provides to the life sciences community uniform gene expression data across multiple species that are available both for download and for exploration via gene-oriented queries and visualisation online. The Atlas includes data from all popular single cell RNA-seq technologies, including SMART-like and Droplet. Fly datasets in SCEA can be accessed directly at [1]	SCEA EMBL-EBI, Wellcome Genome Campus Hinxton, UK

Single Cell RNA-seq Data Analysis Tools

Resource	Description	Source/Reference
ASAP	The web-based, collaborative portal ASAP (Automated Single-cell Analysis Portal) was developed with as primary goal to democratize complex single-cell omics data analyses (scRNA-seq and more recently scATAC-seq). By taking advantage of a Docker system to enhance reproducibility, and novel bioinformatics approaches that were recently developed for improving scalability, ASAP meets challenging requirements set by recent cell atlasing efforts such as the Human (HCA) and Fly (FCA) Cell Atlas Projects. Specifically, ASAP can now handle datasets containing millions of cells, integrating intuitive tools that allow researchers to collaborate on the same project synchronously. ASAP tools are versioned, and researchers can create unique access IDs for storing complete analyses that can be reproduced or completed by others. Finally, ASAP does not require any installation and provides a full and modular single-cell RNA-seq analysis pipeline.	EPFL Swiss Institute of Bioinformatics Lausanne, Switzerland
cisTopic	cisTopic is an R package to simultaneously identify cell states and cis-regulatory topics from single cell epigenomics data.	González-Blas et al.
Distmap	DistMap can be used to spatially map single cell RNA sequencing data by using an existing reference database of in situs.	Karaiskos et al.
Lasso. TopX	Lasso. TopX is an approach for identifying genes that contain spatial information. It utilizes the Lasso and ranking statistics and allows a user to define a specific number of features they are interested in. It was used to reconstruct the 3-D arrangement of the embryo using information from the identified genes employing Matthews correlation coefficients (MCC).	Thomas Jefferson University Philadelphia, PA Loher et al.
DeepCMC - Neural Networks	A Neural Networks (NN) based approach for identifying genes that contain spatial information. It utilizes weak supervision for linear regression to accommodate for uncertain or probabilistic training labels. It was used to reconstruct the 3-D arrangement of the embryo using information from the identified genes employing Matthews correlation coefficients (MCC).	Thomas Jefferson University Philadelphia, PA Loher et al.
novoSpaRc	novoSpaRc predicts locations of single cells in space by solely using single-cell RNA sequencing data. An existing reference database of marker genes is not required, but significantly enhances performance if available.	Nitzan et al.
SCENIC R Package SCENIC Python package	SCENIC infers Gene Regulatory Networks and cell types from single-cell RNA-seq data.	Aibar et al. Van de Sande et al.
SCope	SCope is a fast visualization tool for large-scale and high dimensional scRNA-seq and scATAC-seq datasets.	Janssens et al.
Seurat	Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data. Seurat aims to enable users to identify and interpret sources of heterogeneity from single-cell transcriptomic measurements, and to integrate diverse types of single-cell data.	Satija lab New York Genome Center Center for Genomics and Systems Biology NYU New York, NY Hao et al.
VSN	SN-Pipelines is a repository of pipelines for single-cell data analysis in Nextflow DSL2. It contains multiple workflows for analyzing single cell transcriptomics data, and was used for the automated analysis of the Fly Cell Atlas.

Single Cell RNA-seq Community

Resource	Description	Source/Contact
Fly Cell Atlas	The Fly Cell Atlas (FCA) is a consortium that will bring together Drosophila researchers interested in single-cell genomics, transcriptomics, and epigenomics, to build comprehensive cell atlases during different developmental stages and disease models.	Founders Stein Aerts, Belgium Bart Deplancke, Lausanne, Switzerland Robert Zinzen , Germany Contact - fca@flycellatlas.org