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added several datasets #11

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added several datasets #11

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added several datasets
igordot Oct 15, 2019
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Update README.md
federicomarini Oct 22, 2019
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Expand Up @@ -34,6 +34,7 @@ A curated list of software and resources for exploring and visualizing (browsing
- [FASTGenomics](https://fastgenomics.org), an online platform to share single-cell RNA sequencing data and analyses using reproducible workflows. Users can upload their own data and generate reproducible workflows ([Scholz et al. 2018](https://doi.org/10.1101/272476)). GitHub: [@fastgenomics](https://github.com/fastgenomics). A free demo instance is reachable at https://prod.fastgenomics.org/ (allows anonymous login)
- [Fate Bias Inference in Lymphoid progenitors](http://hematopoietic-progenitors.ie-freiburg.mpg.de), from the lab of Dominic Grün - single-cell RNA-seq data of murine hematopoietic progenitors
- [FireBrowse](http://firebrowse.org), developed by the Broad Institute on top of Firehose
- [Gene and protein expression in adult haematopoiesis](http://blood.stemcells.cam.ac.uk/single_cell_atlas.html), visualization of gene expression in 1,656 single HSPCs, accompanying https://doi.org/10.1182/blood-2016-05-716480
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Is there a more defined name for this resource? I had in my radar as well but did not insert it yet.
Otherwise, we can just leave it as it is...

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I agree the name is not ideal, but I don't know what else to change it to. There is nothing on the website and in the original publication, they say "we therefore developed a user-friendly Web site (http://blood.stemcells.cam.ac.uk/single_cell_atlas.html) where users can explore the three-dimensional structure of the diffusion map graph as well as visualize expression profiles for any gene of interest and surface marker expression".

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Gotta admit that "single_cell_atlas.html" is somewhat... generic

- [GenePattern](http://genepattern-notebook.org), Gene Pattern Notebook Environment (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5572818/)
- [GEPIA](http://gepia.cancer-pku.cn/) Gene Expression Profiling Interactive Analysis, a web server for cancer and normal gene expression profiling and interactive analyses. (https://academic.oup.com/nar/article/45/W1/W98/3605636)
- [Giotto](http://spatial.rc.fas.harvard.edu), a complete pipeline for integrative analysis and visualization of single-cell spatial transcriptomic data. Publication about it is https://doi.org/10.1101/701680 - Giotto Viewer is http://spatial.rc.fas.harvard.edu/giotto-viewer/, while the pipeline is available at https://github.com/RubD/Giotto
Expand All @@ -44,6 +45,7 @@ A curated list of software and resources for exploring and visualizing (browsing
- [Human Cell Atlas Data Portals](https://developmentcellatlas.ncl.ac.uk/datasets/hca_liver/), for exploring gene expression and developmental trajectories, showcased in this publication https://doi.org/10.1038/s41586-019-1652-y (HCA Liver instances: https://developmentcellatlas.ncl.ac.uk/datasets/hca_liver/). Source code is available at https://github.com/DoruMP/Fast-data-portals-for-scRNAseq-data
- [Human Liver Cell Atlas](http://human-liver-cell-atlas.ie-freiburg.mpg.de), related to https://dx.doi.org/10.1038/s41586-019-1373-2 - *currently not working?*
- [iMAC](https://www.stemformatics.org/atlas), an interactive atlas to explore phenotypic differences between macrophage cells. Puiblication available at https://www.biorxiv.org/content/10.1101/719237v1
- [indeXplorer](http://steinmetzlab.embl.de/shiny/indexplorer/), a web-based app for the explorative analysis of the hematopoietic stem cell dataset, accompanying https://doi.org/10.1038/ncb3493, with source code available at https://git.embl.de/velten/indeXplorer
- [Innate T cell gradient](https://immunogenomics.io/itc), low-input and single-cell RNA-seq for human T cell subsets, visualized with Shiny. [Nature Communications](https://doi.org/10.1038/s41467-019-08604-4)
- [iS-CellR](https://github.com/immcore/iS-CellR), for analysing and visualising scRNA-seq data. R Shiny app. Manuscript available at https://academic.oup.com/bioinformatics/article/34/24/4305/5048937
- [iSEE](https://bioconductor.org/packages/release/bioc/html/iSEE.html) - Interactive SummarizedExperiment Explorer (dev version at https://github.com/csoneson/iSEE), with custom panels (https://github.com/kevinrue/iSEE_custom). Publication available at https://f1000research.com/articles/7-741/v1
Expand All @@ -66,6 +68,7 @@ A curated list of software and resources for exploring and visualizing (browsing
- [Multiple sclerosis gene atlas](http://msatlas.compbio.sdu.dk), companion to https://www.biorxiv.org/content/10.1101/584920v1
- [NCI Patient Derived Model Repository](https://pdmdb.cancer.gov/pls/apex/f?p=101:1:0::NO) expression and whole exome data on >350 patients xenografts and patient tissue samples.
- [Neuroexpresso](http://neuroexpresso.org), for the analysis of brain cell type expression profiles. Source available here, https://github.com/PavlidisLab/neuroexpresso, publication: https://www.eneuro.org/content/4/6/ENEURO.0212-17.2017
- [nichExplorer](http://aifantislab.com/niche), an interactive browser for bone marrow microenvironment, accompanying https://doi.org/10.1038/s41586-019-1104-8, with source code available at https://github.com/igordot/nichexplorer
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Would it make more sense to link directly to the resource here? https://compbio.nyumc.org/niche/

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I was trying to match the publication for consistency. I am personally okay with either version. From personal experience, the nyumc.org one may be less stable long-term. On the other hand, this page can be edited at any time to fix any issues.

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I'd propose then you just use both 👍 there is no restriction in the links number

- [PanglaoDB](https://panglaodb.se), a database for the exploration of single cell RNA sequencing experiments from mouse and human. Publication at https://academic.oup.com/database/article/doi/10.1093/database/baz046/5427041, GitHub repo: https://github.com/oscar-franzen/PanglaoDB (not the source code of the resource itself)
- [PAWER](https://biit.cs.ut.ee/pawer/), a software tool for analysing protein microarray data - described in publication: https://www.biorxiv.org/content/10.1101/692905v1
- [PEAC RNA-seq](https://peac.hpc.qmul.ac.uk), for exploring RNA-seq of early rheumatoid arthritis individuals and their clinical data. Linked to the publication https://doi.org/10.1016/j.celrep.2019.07.091
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