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https://github.com/alleninstitute/tasic2016data

Single cell transcriptomic data from Tasic, et al. (2016)

https://github.com/alleninstitute/tasic2016data

Science Score: 54.0%

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Keywords

scrattch scrna-seq-data transcriptomics
Last synced: 5 months ago · JSON representation

Repository

Single cell transcriptomic data from Tasic, et al. (2016)

Basic Info
  • Host: GitHub
  • Owner: AllenInstitute
  • License: other
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 100 MB
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  • Watchers: 14
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scrattch scrna-seq-data transcriptomics
Created about 8 years ago · Last pushed about 7 years ago
Metadata Files
Readme License

README.md

tasic2016data

Data from Tasic, et al., Nature Neuroscience (2016).

Overview

This package is intended as a convenient way to load the scRNA-seq dataset published in Tasic, et al. (2016) in R for analysis.

It contains data for all cells released on GEO for this article, deposited at accession GSE71585

There are only 3 data objects in this package, and no functions. They are:
tasic_2016_anno: A data.frame with annotations for each sample. See ?tasic_2016_anno for descriptions of each column.
tasic_2016_counts: A matrix of read counts from RSEM analysis for each gene used in Tasic, et al. (2016).
tasic_2016_fpkm: A matrix of FPKM values for each gene used in Tasic, et al. (2016).

Installation

The package can be installed from Github using the devtools package:

r devtools::install_github("AllenInstitute/tasic2016data")

Once installed, the dataset will be placed in the user's R LazyLoad database, and can be easily retrieved:

```r library(tasic2016data)

annotations <- tasic2016anno counts <- tasic2016counts ```

Getting clustered samples

Note that this dataset has all cells from GSE71585, including those that failed QC, those from retrograde virus injections, and those that fell into outlier clusters.

To obtain the set of cells that was used for clustering in Tasic, et al. (2016), you can filter annotations on the coreintermediate and credriver_1 columns

r clustered_samples <- tasic_2016_anno$core_intermediate != "unclassified" & tasic_2016_anno$cre_driver_1 != "CAV2-Cre" clustered_anno <- tasic_2016_anno[clustered_samples, ] clustered_counts <- tasic_2016_counts[, clustered_anno$sample_name]

Citation

If you use this package, please cite the original publication, Tasic, et al. (2016). Details can be found using

r citation("tasic2016data")

License

The license for this package is available on Github at: https://github.com/AllenInstitute/tasic2016data/blob/master/LICENSE

Support

We are not currently supporting this code, but simply releasing it to the community AS IS but are not able to provide any guarantees of support. The community is welcome to submit issues, but you should not expect an active response.

Owner

  • Name: Allen Institute
  • Login: AllenInstitute
  • Kind: organization
  • Location: Seattle, WA

Please visit http://alleninstitute.github.io/ for more information.

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