Field-wide assessment of differential HT-seq from NCBI GEO database

We analysed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository. - This release includes GEO series published up to Dec-31, 2020; geo-htseq.tar.gz archive contains following files: - output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0). - output/document_summaries.csv, document summaries of NCBI GEO series. - output/suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions. - output/suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO. - output/publications.csv, publication info of NCBI GEO series. - output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series - output/spots.csv, NCBI SRA sequencing run metadata. - output/cancer.csv, cancer related experiment accessions. - output/transcription_factor.csv, TF related experiment accessions. - output/single-cell.csv, single cell experiment accessions. - blacklist.txt, list of supplementary files that were either too large to import or were causing computing environment crash during import. Workflow to produce this dataset is available on Github at rstats-tartu/geo-htseq. geo-htseq-updates.tar.gz archive contains files: - results/detools_from_pmc.csv, differential expression analysis programs inferred from published articles - results/n_data.csv, manually curated sample size info for NCBI GEO HT-seq series - results/simres_df_parsed.csv, pi0 values estimated from differential expression results obtained from simulated RNA-seq data - results/data/parsed_suppfiles_rerun.csv, pi0 values estimated using smoother method from anti-conservative p-value sets