Data

mirrorCheck results for 4 public datasets

Monash University
Katherine Scull (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.26180/27289017.v1&rft.title=mirrorCheck results for 4 public datasets&rft.identifier=https://doi.org/10.26180/27289017.v1&rft.publisher=Monash University&rft.description=Each zipped folder contains results files from reanalysis of public data in our publication, mirrorCheck: an R package facilitating informed use of DESeq2’s lfcShrink() function for differential gene expression analysis of clinical samples (see also the Collection description).These files were produced by rendering the Quarto documents provided in the supplementary data with the publication (one per dataset). The Quarto codes for the 3 main analyses (COVID, BRCA and Cell line datasets) performed differential gene expression (DGE) analysis using both DESeq2 with lfcShrink() via our R package mirrorCheck, and also edgeR. Each zipped folder here contains 2 folders, one for each DGE analysis. Since DESeq2 was run on data without prior data cleaning, with prefiltering or after Surrogate Variable Analysis, the 'mirrorCheck output' folders themselves contain 3 sub-folders titled 'DESeq_noclean', 'DESeq_prefilt' and 'DESeq_sva. The COVID dataset also has a folder with results from Gene Set Enrichment Analysis. Finally, the fourth folder contains results from a tutorial/vignette-style supplementary file using the Bioconductor parathyroidSE dataset. This analysis only utilised DESeq2, with both data cleaning methods and testing two different design formulae, resulting in 5 sub-folders in the zipped folder.&rft.creator=Katherine Scull&rft.date=2025&rft_rights=CC-BY-4.0&rft_subject=RNA-Seq&rft_subject=Differential gene expression analysis&rft_subject=quality control&rft_subject=Bioinformatic methods development&rft_subject=Genomics and transcriptomics&rft.type=dataset&rft.language=English Access the data

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Each zipped folder contains results files from reanalysis of public data in our publication, "mirrorCheck: an R package facilitating informed use of DESeq2’s lfcShrink() function for differential gene expression analysis of clinical samples" (see also the Collection description).

These files were produced by rendering the Quarto documents provided in the supplementary data with the publication (one per dataset). The Quarto codes for the 3 main analyses (COVID, BRCA and Cell line datasets) performed differential gene expression (DGE) analysis using both DESeq2 with lfcShrink() via our R package mirrorCheck, and also edgeR. Each zipped folder here contains 2 folders, one for each DGE analysis. Since DESeq2 was run on data without prior data cleaning, with prefiltering or after Surrogate Variable Analysis, the 'mirrorCheck output' folders themselves contain 3 sub-folders titled 'DESeq_noclean', 'DESeq_prefilt' and 'DESeq_sva". The COVID dataset also has a folder with results from Gene Set Enrichment Analysis. Finally, the fourth folder contains results from a tutorial/vignette-style supplementary file using the Bioconductor "parathyroidSE" dataset. This analysis only utilised DESeq2, with both data cleaning methods and testing two different design formulae, resulting in 5 sub-folders in the zipped folder.

Issued: 2025-03-27

Created: 2025-03-27

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