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Galaxy Training Material for single-cell RNA-seq tutorial with Plant Datasets

Authors: Tekman, Mehmet;

Galaxy Training Material for single-cell RNA-seq tutorial with Plant Datasets

Abstract

These are the raw expression matrices from Spatiotemporal Developmental Trajectories in the Arabidopsis Root Revealed Using High-Throughput Single-Cell RNA Sequencing (Denyer et al., 2019). In the paper they were processed using Seurat under a tSNE embedding, and in this tutorial we will replicate the analysis using ScanPy under a UMAP embedding. The initial datasets used generic Uniprot identifiers and required transposing. The method required to pull, annotate, unambiguously exchange names, and transpose these datasets to help with analysis are contained within the uniprot.R script included (with the remapping.complete.tsv.gz being generated by this). Original datasets from GSE123818: GSE123818_Root_single_cell_shr_datamatrix.csv GSE123818_Root_single_cell_wt_datamatrix.csv Updated using the scripts (for the GTN): GSE123818_Root_single_cell_shr_datamatrix.fixednames.transposed.csv.gz GSE123818_Root_single_cell_wt_datamatrix.fixednames.transposed.csv.gz

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Keywords

training, scRNA-seq, galaxy, single-cell, scRNA

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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.
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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
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