Difference between revisions of "FlyBase:ScRNA-Seq"

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=Popular Resource Categories=
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= Popular Resource Categories =
  
 
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|style="text-align: center; padding: 15px;"| <big>[[FlyBase:External_Resources|All Resources]]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase:CRISPR|CRISPR]]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase:ScRNA-Seq|ScRNA-Seq]]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase:RNAi|RNAi]]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase:Stocks|Stocks]]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase: Antibodies|Antibodies]]</big>
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|style="text-align: center; padding: 15px;"| <big>[[FlyBase:External_Resources|All Resources]]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase:CRISPR|CRISPR]]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase:ScRNA-Seq|ScRNA-Seq]]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase:RNAi|RNAi]]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase:Stocks|Stocks]]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase: Antibodies|Antibodies]]</big>||style="text-align: center; padding: 20px;"| <big>[[FlyBase:Neuroscience|Neuroscience]]</big>
 
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{|class="wikitable"
 
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|style="text-align: center; padding: 20px;"| <big>[[FlyBase:Model_Organism_Databases|Model Organism<br/> Databases]]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase:Neuroscience|Neuroscience]]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase:Images|Images]]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase:Maps|Maps]]</big> ||style="text-align: center; padding: 20px;"| <big>[http://www.flyrnai.org/tools/protocols/web/ Protocols]</big>
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|style="text-align: center; padding: 20px;"| <big>[[FlyBase:Model_Organism_Databases|Model Organism<br/> Databases]]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase:Images|Images]]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase:Maps|Maps]]</big> ||style="text-align: center; padding: 20px;"| <big>[http://www.flyrnai.org/tools/protocols/web/ Protocols]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase:Papers_with_technical_advances|Papers with<br/> Technical Advances]]</big> ||style="text-align: center; padding: 20px;"| <big>[[FlyBase:GSEA|Gene Set<br/> Enrichment Tools]]</big>
 +
 
 
|}
 
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__TOC__
 
__TOC__
 
= Single Cell RNA-seq Data Portals =
 
 
  
 
= Single Cell RNA-seq Data Portals =
 
= Single Cell RNA-seq Data Portals =
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| [https://shiny.mdc-berlin.de/DVEX/ 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.  ||style="white-space: nowrap;"| MDC<br /> BIMSB<br /> Berlin, Germany<br /> [https://science.sciencemag.org/content/358/6360/194.long Karaiskos et al.]
 
| [https://shiny.mdc-berlin.de/DVEX/ 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.  ||style="white-space: nowrap;"| MDC<br /> BIMSB<br /> Berlin, Germany<br /> [https://science.sciencemag.org/content/358/6360/194.long Karaiskos et al.]
 
|-
 
|-
|style="background: #efefef;"| [https://www.flyrnai.org/tools/single_cell// DRscDB] ||style="background: #efefef;"| 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. ||style="background: #efefef;"<br/> DRSC, Harvard Medical School<br /> Boston, MA, USA
+
|style="background: #efefef;"| [https://www.flyrnai.org/tools/single_cell// DRscDB] ||style="background: #efefef;"| 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. ||style="background: #efefef;" | DRSC, Harvard Medical School<br /> Boston, MA, USA
 +
|-
 +
| [https://flycellatlas.org/ Fly Cell Atlas] || The Fly Cell Atlas (FCA) is a consortium that brings together Drosophila researchers interested in single-cell genomics, transcriptomics, and epigenomics, to build comprehensive cell atlases during different developmental stages and disease models. During 2020 and 2021, the FCA consortium ran a collaborative effort with CZ Biohub, Genentech, and NIH, to sequence all cells of the adult fly. Driven by Hongjie Li and Liqun Luo, along with dozens of Drosophila labs in the Bay area, 15 tissues were dissected for single-nucleus RNA-seq, alongside the whole head and body. Data analysis teams in Leuven (Aerts) and EPFL (Deplancke) analyzed all data, and through >20 online jamborees with >40 Drosophila labs around the world, more than 250 single-cell clusters were annotated with FlyBase FBbt terms. The data is now available via three portals, namely SCope, ASAP, and CellxGene, and can be downloaded as loomX and h5ad files to be further analyzed in R or Python. ||style="white-space: nowrap;"| Founders<br /> Stein Aerts, Leuven, Belgium <br /> Bart Deplancke, Lausanne, Switzerland <br /> Robert Zinzen, Berlin, Germany <br /> Contact - fca@flycellatlas.org
 +
|-
 +
|style="background: #efefef;"| [https://www.ebi.ac.uk/gxa/sc SCEA] ||style="background: #efefef;"| 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 [https://www.ebi.ac.uk/gxa/sc/experiments?species=%22drosophila%20melanogaster%22]  ||style="background: #efefef;white-space: nowrap;"| SCEA<br/> EMBL-EBI, Wellcome Genome Campus<br/> Hinxton, UK
 
|-
 
|-
| [https://www.ebi.ac.uk/gxa/sc 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 [https://www.ebi.ac.uk/gxa/sc/experiments?species=%22drosophila%20melanogaster%22]  ||style="white-space: nowrap;"| SCEA<br/> EMBL-EBI, Wellcome Genome Campus<br/> Hinxton, UK
+
| [https://cells.ucsc.edu/? UCSC Cell Browser] || The UCSC Cell Browser is an interactive viewer for single-cell expression for a variety of species, including D. melanogaster. || UCSC
 +
 
 
|-
 
|-
 
|}
 
|}
 
<br/>
 
<br/>
 
= Single Cell RNA-seq Data Analysis Tools =
 
 
  
 
= Single Cell RNA-seq Data Analysis Tools =
 
= Single Cell RNA-seq Data Analysis Tools =
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!style="background: #efefef;"| Resource !!style="background: #efefef;"| Description !!style="background: #efefef;"| Source/Reference
 
!style="background: #efefef;"| Resource !!style="background: #efefef;"| Description !!style="background: #efefef;"| Source/Reference
 
|-
 
|-
| [https://asap.epfl.ch/ 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.  ||style="white-space: nowrap;"| EPFL, Swiss Institute of Bioinformatics <br /> Lausanne, Switzerland
+
| [https://asap.epfl.ch/ 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.  ||style="white-space: nowrap;"| EPFL<br /> Swiss Institute of Bioinformatics <br /> Lausanne, Switzerland
 
|-
 
|-
|style="background: #efefef;"| [https://github.com/aertslab/cisTopic cisTopic] ||style="background: #efefef;"| cisTopic is an R package to simultaneously identify cell states and cis-regulatory topics from single cell epigenomics data. ||style="background: #efefef;"| SCEA<br/> Institution<br/> Location|-
+
|style="background: #efefef;"| [https://github.com/aertslab/cisTopic cisTopic] ||style="background: #efefef;"| cisTopic is an R package to simultaneously identify cell states and cis-regulatory topics from single cell epigenomics data. ||style="background: #efefef;"| [https://europepmc.org/article/MED/30962623 González-Blas et al.]
 
|-
 
|-
 
| [https://github.com/rajewsky-lab/distmap Distmap] || DistMap can be used to spatially map single cell RNA sequencing data by using an existing reference database of in situs.  ||style="white-space: nowrap;"| [https://science.sciencemag.org/content/358/6360/194 Karaiskos et al.]
 
| [https://github.com/rajewsky-lab/distmap Distmap] || DistMap can be used to spatially map single cell RNA sequencing data by using an existing reference database of in situs.  ||style="white-space: nowrap;"| [https://science.sciencemag.org/content/358/6360/194 Karaiskos et al.]
 
|-
 
|-
|style="background: #efefef;"| [https://github.com/rajewsky-lab/novosparc_novoSpaRc] ||style="background: #efefef;"| 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. ||style="background: #efefef;"<br/> [https://www.nature.com/articles/s41586-019-1773-3 Nitzan et al.]
+
|style="background: #efefef;"| [https://github.com/TJU-CMC-Org/SingleCell-DREAM/ Lasso.TopX]||style="background: #efefef;"| 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). ||style="background: #efefef;" |<br/> Thomas Jefferson University<br/> Philadelphia, PA<br/>  [https://www.frontiersin.org/articles/10.3389/fgene.2020.612840/full Loher et al.]
 +
|-
 +
| [https://github.com/TJU-CMC-Org/SingleCell-DREAM/ 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).  ||style="white-space: nowrap;"| Thomas Jefferson University<br/> Philadelphia, PA<br/>  [https://www.frontiersin.org/articles/10.3389/fgene.2020.612840/full Loher et al.]
 +
|-
 +
|style="background: #efefef;"| [https://github.com/rajewsky-lab/novosparc novoSpaRc] ||style="background: #efefef;"| 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. ||style="background: #efefef;" | [https://www.nature.com/articles/s41586-019-1773-3 Nitzan et al.]
 
|-
 
|-
| [https://github.com/aertslab/SCENIC SCENIC R Package]<br/> [https://github.com/aertslab/pySCENIC SCENIC Python package] || SCENIC infers Gene Regulatory Networks and cell types from single-cell RNA-seq data.  ||style="white-space: nowrap;"| [https://europepmc.org/article/MED/28991892 Aibar et al.]<br/> [https://europepmc.org/article/MED/32561888 Van de Sande et al.]
+
| [https://currentprotocols.onlinelibrary.wiley.com/doi/10.1002/cpz1.37 R Pipeline] || A custom multistage analysis pipeline which integrates modules contained indifferent R packages to ensure exible, high-quality RNA-seq data analysis.  ||style="white-space: nowrap;"| [https://currentprotocols.onlinelibrary.wiley.com/doi/10.1002/cpz1.37 Vicidomini et al.]
 
|-
 
|-
|style="background: #efefef;"| [https://scope.aertslab.org SCope] ||style="background: #efefef;"| SCope is a fast visualization tool for large-scale and high dimensional scRNA-seq and scATAC-seq datasets. ||style="background: #efefef;" | <br/> [https://europepmc.org/article/PPR/PPR21077 Janssens et al.]
+
|style="background: #efefef;"| [https://github.com/aertslab/SCENIC SCENIC R Package]<br/> [https://github.com/aertslab/pySCENIC SCENIC Python package] ||style="background: #efefef;"| SCENIC infers Gene Regulatory Networks and cell types from single-cell RNA-seq data. ||style="background: #efefef;"| [https://europepmc.org/article/MED/28991892 Aibar et al.]<br/> [https://europepmc.org/article/MED/32561888 Van de Sande et al.]
 
|-
 
|-
| [http://satijalab.org/seurat/ 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.  ||style="white-space: nowrap;"| Satija lab<br /> New York Genome Center<br />  Center for Genomics and Systems Biology<br /> NYU<br /> New York, NY<br /> [https://www.biorxiv.org/content/10.1101/2020.10.12.335331v1 Hao et al.]
+
| [https://scope.aertslab.org SCope] || SCope is a fast visualization tool for large-scale and high dimensional scRNA-seq and scATAC-seq datasets.  ||style="white-space: nowrap;"| [https://europepmc.org/article/PPR/PPR21077 Janssens et al.]
 
|-
 
|-
|style="background: #efefef;"| [https://https://github.com/vib-singlecell-nf/vsn-pipelines VSN] ||style="background: #efefef;"| 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. ||style="background: #efefef;" |
+
|style="background: #efefef;"| [http://satijalab.org/seurat/ Seurat] ||style="background: #efefef;"| 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. ||style="background: #efefef;"| Satija lab<br /> New York Genome Center<br />  Center for Genomics and Systems Biology<br /> NYU<br /> New York, NY<br /> [https://www.biorxiv.org/content/10.1101/2020.10.12.335331v1 Hao et al.]
 
|}
 
|}
 
<br/>
 
<br/>
 +
 +
= Sharing scRNAseq Datasets with the Community =
 +
 +
FlyBase has an ongoing collaboration with the EBI’s [https://www.ebi.ac.uk/gxa/sc/home Single Cell Expression Atlas], a repository of scRNAseq datasets.
 +
 +
In order to get your scRNAseq data into the Single Cell Expression Atlas, you need to deposit your raw sequencing data files into a sequencing data repository such as the NCBI’s [https://www.ncbi.nlm.nih.gov/geo/ Gene Expression Omnibus] or the EBI’s [https://www.ebi.ac.uk/biostudies/arrayexpress ArrayExpress]. From there, once your paper has been published, your data will be picked up by FlyBase and SCEA curators to be added to the Single Cell Expression Atlas.
 +
 +
In the meantime, if you wish to upload your processed data somewhere else to share it as soon as possible, your options include:
 +
 +
* the EFPL/SIB’s [https://asap.epfl.ch/ ASAP platform];
 +
* the UCSC [https://cells.ucsc.edu/ Cell Browser] (there is no upload form from the website itself; you need to contact them at cells@ucsc.edu).
 +
 +
There is also CZI’s [cellxgene.cziscience.com/ Cell×Gene Discover], but currently they only accept data from primates and rodents. This may change in the future, though, and we’ll update this page if/when they start accepting datasets from _D. melanogaster_.
 +
<br/>
 +
 +
= Single Cell RNA-seq Community =
 +
 +
{|cellpadding=5
 +
|-
 +
!style="background: #efefef;"| Resource !!style="background: #efefef;"| Description !!style="background: #efefef;"| Source/Contact
 +
|-
 +
| [https://flycellatlas.org/ Fly Cell Atlas] || The Fly Cell Atlas (FCA) is a consortium that brings together Drosophila researchers interested in single-cell genomics, transcriptomics, and epigenomics, to build comprehensive cell atlases during different developmental stages and disease models.  ||style="white-space: nowrap;"| Founders<br /> Stein Aerts, Leuven, Belgium <br /> Bart Deplancke, Lausanne, Switzerland <br /> Robert Zinzen, Berlin, Germany <br /> Contact - fca@flycellatlas.org

Latest revision as of 20:24, 25 June 2024

Popular Resource Categories

All Resources CRISPR ScRNA-Seq RNAi Stocks Antibodies Neuroscience
Model Organism
Databases
Images Maps Protocols Papers with
Technical Advances
Gene Set
Enrichment Tools

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
Fly Cell Atlas The Fly Cell Atlas (FCA) is a consortium that brings together Drosophila researchers interested in single-cell genomics, transcriptomics, and epigenomics, to build comprehensive cell atlases during different developmental stages and disease models. During 2020 and 2021, the FCA consortium ran a collaborative effort with CZ Biohub, Genentech, and NIH, to sequence all cells of the adult fly. Driven by Hongjie Li and Liqun Luo, along with dozens of Drosophila labs in the Bay area, 15 tissues were dissected for single-nucleus RNA-seq, alongside the whole head and body. Data analysis teams in Leuven (Aerts) and EPFL (Deplancke) analyzed all data, and through >20 online jamborees with >40 Drosophila labs around the world, more than 250 single-cell clusters were annotated with FlyBase FBbt terms. The data is now available via three portals, namely SCope, ASAP, and CellxGene, and can be downloaded as loomX and h5ad files to be further analyzed in R or Python. Founders
Stein Aerts, Leuven, Belgium
Bart Deplancke, Lausanne, Switzerland
Robert Zinzen, Berlin, Germany
Contact - fca@flycellatlas.org
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
UCSC Cell Browser The UCSC Cell Browser is an interactive viewer for single-cell expression for a variety of species, including D. melanogaster. UCSC


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.
R Pipeline A custom multistage analysis pipeline which integrates modules contained indifferent R packages to ensure exible, high-quality RNA-seq data analysis. Vicidomini 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.


Sharing scRNAseq Datasets with the Community

FlyBase has an ongoing collaboration with the EBI’s Single Cell Expression Atlas, a repository of scRNAseq datasets.

In order to get your scRNAseq data into the Single Cell Expression Atlas, you need to deposit your raw sequencing data files into a sequencing data repository such as the NCBI’s Gene Expression Omnibus or the EBI’s ArrayExpress. From there, once your paper has been published, your data will be picked up by FlyBase and SCEA curators to be added to the Single Cell Expression Atlas.

In the meantime, if you wish to upload your processed data somewhere else to share it as soon as possible, your options include:

  • the EFPL/SIB’s ASAP platform;
  • the UCSC Cell Browser (there is no upload form from the website itself; you need to contact them at cells@ucsc.edu).

There is also CZI’s [cellxgene.cziscience.com/ Cell×Gene Discover], but currently they only accept data from primates and rodents. This may change in the future, though, and we’ll update this page if/when they start accepting datasets from _D. melanogaster_.

Single Cell RNA-seq Community

Resource Description Source/Contact
Fly Cell Atlas The Fly Cell Atlas (FCA) is a consortium that brings 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, Leuven, Belgium
Bart Deplancke, Lausanne, Switzerland
Robert Zinzen, Berlin, Germany
Contact - fca@flycellatlas.org