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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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Integration of single-cell RNA-sequencing data across tissues and cancer types towards immune cell characterization

Authors: Bhavesh Soni; Schwalie, Petra;

Integration of single-cell RNA-sequencing data across tissues and cancer types towards immune cell characterization

Abstract

To better understand dendritic cell states and subtypes, we collected individual single-cell RNAseq datasets from various studies and further integrated, batch corrected, and reprocessed the data using Besca (https://github.com/bedapub/besca). The following files are included: 1) study_table_integrated_DCs.xlsx - contains a list of studies from where the datasets were gathered. 2) int_dcs.raw.h5ad - An anndata object file containing the combined raw single-cell counts for DCs from individual studies. The datasets were joined based on the union of variables. 3) intersection_genes_integrated_dcs.tsv - List of genes if the datasets were joined based on the intersection of variables. These variables were used in the subsequent analyses. 4) int_dcs.annotated.h5ad - An anndata object file containing single-cell logarithmized counts for DCs data that have been integrated and reprocessed. The rows of the file contain cells, and the columns contain highly variable genes. A sparse matrix containing the logarithmized counts from all the genes (from the intersection genes integrated dcs.tsv file) can also be found (adata.raw.X) in the object. In the observations, cell-type annotation is available at three different hierarchal levels. This data was further used to produce results for the publication (https://jitc.bmj.com/content/10/6/e004268) on the effects of Toll-like receptor 8 agonists on conventional DCs.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Cancer Research