Begley CG, Ioannidis JP. Reproducibility in science: improving the standard for basic and preclinical research. Circ Res. 2015;116(1):116–26.

Article  CAS  PubMed  Google Scholar 

Gilmore RO, Diaz MT, Wyble BA, Yarkoni T. Progress toward openness, transparency, and reproducibility in cognitive neuroscience. Ann N Y Acad Sci. 2017;1396(1):5–18.

Article  PubMed  PubMed Central  Google Scholar 

Errington TM, Iorns E, Gunn W, Tan FE, Lomax J, Nosek BA. An open investigation of the reproducibility of cancer biology research. Elife. 2014;10(3):e04333.

Article  Google Scholar 

Prinz F, Schlange T, Asadullah K. Believe it or not: how much can we rely on published data on potential drug targets? Nat Rev Drug Discov. 2011;10(9):712.

Article  CAS  PubMed  Google Scholar 

Hutson M. Artificial intelligence faces reproducibility crisis. Science. 2018;359(6377):725–6.

Article  PubMed  Google Scholar 

Stodden V, McNutt M, Bailey DH, Deelman E, Gil Y, Hanson B, et al. Enhancing reproducibility for computational methods. Science. 2016;354(6317):1240–1.

Article  CAS  PubMed  Google Scholar 

Marcial LH, Hemminger BM. Scientific data repositories on the Web: An initial survey. J Am Soc Inform Sci Technol. 2010;61(10):2029–48.

Article  Google Scholar 

Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3: 160018.

Article  PubMed  PubMed Central  Google Scholar 

Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet. 2001;29(4):365–71.

Article  CAS  PubMed  Google Scholar 

Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res. 2013;41(Database issue):D991-5.

CAS  PubMed  Google Scholar 

Tonzani S, Fiorani S. The STAR Methods way towards reproducibility and open science. iScience. 2021;24(4).

Article  PubMed  PubMed Central  Google Scholar 

Sprang M, Krüger M, Andrade-Navarro MA, Fontaine JF. Statistical guidelines for quality control of next-generation sequencing techniques. Life Sci Alliance. 2021;4(11):e202101113.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Consortium EP. The ENCODE (ENCyclopedia Of DNA Elements) Project. Science. 2004;306(5696):636–40.

Article  Google Scholar 

Jacob L, Gagnon-Bartsch JA, Speed TP. Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed. Biostatistics. 2016;17(1):16–28.

Article  PubMed  Google Scholar 

Leek JT. svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res. 2014;42(21): e161.

Article  PubMed  PubMed Central  Google Scholar 

Zhang Y, Parmigiani G, Johnson WE. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom Bioinform. 2020;2(3):lqaa078.

Article  PubMed  PubMed Central  Google Scholar 

Murkin JT, Amos HE, Brough DW, Turley KD. In Silico Modeling Demonstrates that User Variability During Tumor Measurement Can Affect In Vivo Therapeutic Efficacy Outcomes. Cancer Inform. 2022;21:11769351221139256.

Article  PubMed  PubMed Central  Google Scholar 

Chao HP, Chen Y, Takata Y, Tomida MW, Lin K, Kirk JS, et al. Systematic evaluation of RNA-Seq preparation protocol performance. BMC Genomics. 2019;20(1):571.

Article  PubMed  PubMed Central  Google Scholar 

Simeon-Dubach D, Perren A. Better provenance for biobank samples. Nature. 2011;475(7357):454–5.

Article  CAS  PubMed  Google Scholar 

Soneson C, Gerster S, Delorenzi M. Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation. PLoS ONE. 2014;9(6): e100335.

Article  PubMed  PubMed Central  Google Scholar 

Hamilton DG, Page MJ, Finch S, Everitt S, Fidler F. How often do cancer researchers make their data and code available and what factors are associated with sharing? BMC Med. 2022;20(1):438.

Article  PubMed  PubMed Central  Google Scholar 

Simeon-Dubach D, Burt AD, Hall PA. Quality really matters: the need to improve specimen quality in biomedical research. J Pathol. 2012;228(4):431–3.

Article  PubMed  Google Scholar 

Subramanian A, Alperovich M, Yang Y, Li B. Biology-inspired data-driven quality control for scientific discovery in single-cell transcriptomics. Genome Biol. 2022;23(1):267.

Article  CAS  PubMed  PubMed Central  Google Scholar 

10x Genomics®. CG000130 Rev A Technical Note – Removal of Dead Cells from Single Cell Suspensions Improves Performance for 10xGenomics® Single Cell Applications. 2019. https://www.10xgenomics.com/support/single-cell-gene-expression/documentation/steps/sample-prep/removal-of-dead-cells-from-single-cell-suspensions-improves-performance-for-10-x-genomics-r-single-cell-applications. Accessed 01 Jun 2023.

Wilms R, Mäthner E, Winnen L, Lanwehr R. Omitted variable bias: A threat to estimating causal relationships. Methods in Psychology. 2021;5: 100075.

Article  Google Scholar 

Albrecht S, Sprang M, Andrade-Navarro MA, Fontaine JF. seqQscorer: automated quality control of next-generation sequencing data using machine learning. Genome Biol. 2021;22(1):75.

Article  PubMed  PubMed Central  Google Scholar 

Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Sci Rep. 2019;9(1):9354.

Article  PubMed  PubMed Central  Google Scholar 

Wang Z, Lachmann A, Ma’ayan A. Mining data and metadata from the gene expression omnibus. Biophys Rev. 2019;11(1):103–10.

Article  CAS  PubMed  Google Scholar 

Nicodemus-Johnson J, Myers RA, Sakabe NJ, Sobreira DR, Hogarth DK, Naureckas ET, et al. DNA methylation in lung cells is associated with asthma endotypes and genetic risk. JCI Insight. 2016;1(20):e90151.

Article  PubMed  PubMed Central  Google Scholar 

Li B, Tsoi LC, Swindell WR, Gudjonsson JE, Tejasvi T, Johnston A, et al. Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms. J Invest Dermatol. 2014;134(7):1828–38.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jin Y, Lee WY, Toh ST, Tennakoon C, Toh HC, Chow PK, et al. Comprehensive analysis of transcriptome profiles in hepatocellular carcinoma. J Transl Med. 2019;17(1):273.

Article  PubMed  PubMed Central  Google Scholar 

Cassetta L, Fragkogianni S, Sims AH, Swierczak A, Forrester LM, Zhang H, et al. Human Tumor-Associated Macrophage and Monocyte Transcriptional Landscapes Reveal Cancer-Specific Reprogramming, Biomarkers, and Therapeutic Targets. Cancer Cell. 2019;35(4):588-602 e10.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bondar G, Togashi R, Cadeiras M, Schaenman J, Cheng RK, Masukawa L, et al. Association between preoperative peripheral blood mononuclear cell gene expression profiles, early postoperative organ function recovery potential and long-term survival in advanced heart failure patients undergoing mechanical circulatory support. PLoS ONE. 2017;12(12):e0189420.

Article