ROTS: reproducible RNA-seq biomarker detector—prognostic markers for clear cell renal cell cancer
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Recent comprehensive assessments of RNA-seq technology support its utility in quantifying gene expression in various samples. The next step of rigorously quantifying differences between sample groups, however, still lacks well-defined best practices. Although a number of advanced statistical methods have been developed, several studies demonstrate that their performance depends strongly on the data under analysis, which compromises practical utility in real biomedical studies. As a solution, we propose to use a data-adaptive procedure that selects an optimal statistic capable of maximizing reproducibility of detections. After demonstrating its improved sensitivity and specificity in a controlled spike-in study, the utility of the procedure is confirmed in a real biomedical study by identifying prognostic markers for clear cell renal cell carcinoma (ccRCC). In addition to identifying several genes previously associated with ccRCC prognosis, several potential new biomarkers among genes regulating cell growth, metabolism and solute transport were detected.
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Because simulations rely on specific distributions or limited datasets and spike-in datasets have only technical replicates with minimal variation, comparisons have been complemented with real datasets with true biological replicates.
“As simulations typically rely on specific statistical distributions or on limited experimental datasets and as spike-in datasets represent only technical replicates with minimal variation, comparisons using simulated datasets have been complemented with more practical comparisons in real datasets with true biological replicates [64, 210, 211].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [211]
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Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364 · see citing works
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464 · see citing works
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581 · see citing works
Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r36 · Cited by 12,668 · see citing works
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829 · see citing works
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785 · see citing works
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568 · see citing works
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r29 · Cited by 6,155 · see citing works
McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks042 · Cited by 5,195 · see citing works
Differential analysis of gene regulation at transcript resolution with RNA-seq
Trapnell, C., Hendrickson, D. G., Sauvageau, M., Goff, L., Rinn, J. L., & Pachter, L. (2013) · Nature Biotechnology · doi:10.1038/nbt.2450 · Cited by 3,492 · see citing works
Comprehensive molecular characterization of clear cell renal cell carcinoma
The Cancer Genome Atlas Research Network (2013) · Nature · doi:10.1038/nature12222 · Cited by 3,485 · see citing works
Bullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-94 · Cited by 1,680 · cited 1× · see citing works
“The necessity of normalization has been proved in RNAseq studies (20,21).”
BackgroundRini, B. I., Campbell, S. C., & Escudier, B. J. (2009) · The Lancet · doi:10.1016/s0140-6736(09)60229-4 · Cited by 1,634 · see citing works
Differential expression in RNA-seq: A matter of depth
Tarazona, S., García-Alcalde, F., Dopazo, J., Ferrer, A., & Conesa, A. (2011) · Genome Research · doi:10.1101/gr.124321.111 · Cited by 1,632 · see citing works
Dillies, M., Rau, A., Aubert, J., Hennequet‐Antier, C., Jeanmougin, M., Servant, N., Keime, C., Marot, G., Castel, D., Estellé, J., et al. (2012) · Briefings in Bioinformatics · doi:10.1093/bib/bbs046 · Cited by 1,281 · see citing works
Integrated molecular analysis of clear-cell renal cell carcinoma
Sato, Y., Yoshizato, T., Shiraishi, Y., Maekawa, S., Okuno, Y., Kamura, T., Shimamura, T., Sato‐Otsubo, A., Nagae, G., Suzuki, H., et al. (2013) · Nature Genetics · doi:10.1038/ng.2699 · Cited by 1,223 · see citing works
SEQC/MAQC-III Consortium (2014) · Nature Biotechnology · doi:10.1038/nbt.2957 · Cited by 983 · see citing works
Raval, R. R., Lau, K. W., Tran, M. G. B., Sowter, H. M., Mandriota, S. J., Li, J.-L., Pugh, C. W., Maxwell, P. H., Harris, A. L., & Ratcliffe, P. J. (2005) · Molecular and Cellular Biology · doi:10.1128/mcb.25.13.5675-5686.2005 · Cited by 940 · see citing works
Shaughnessy, J. D., Zhan, F., Burington, B. E., Huang, Y., Colla, S., Hanamura, I., Stewart, P., Kordsmeier, B., Randolph, C. E., Williams, D. R., et al. (2006) · Blood · doi:10.1182/blood-2006-07-038430 · Cited by 916 · see citing works
A comparison of methods for differential expression analysis of RNA-seq data
Soneson, C., & Delorenzi, M. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-91 · Cited by 887 · see citing works
baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data
Hardcastle, T. J., & Kelly, K. A. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-422 · Cited by 755 · see citing works
Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
Rapaport, F., Khanin, R., Liang, Y., Pirun, M., Krek, A., Zumbo, P., Mason, C. E., Socci, N. D., & Betel, D. (2013) · Genome Biology · doi:10.1186/gb-2013-14-9-r95 · Cited by 733 · see citing works
Synthetic spike-in standards for RNA-seq experiments
Jiang, L., Schlesinger, F., Davis, C. A., Zhang, Y., Li, R., Salit, M., Gingeras, T. R., & Oliver, B. (2011) · Genome Research · doi:10.1101/gr.121095.111 · Cited by 653 · see citing works
Wang, C., Gong, B., Bushel, P. R., Thierry-Mieg, J., Thierry-Mieg, D., Xu, J., Fang, H., Hong, H., Shen, J., Su, Z., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.3001 · Cited by 512 · see citing works
Kapur, P., Peña‐Llopis, S., Christie, A. L., Zhrebker, L., Pavía-Jiménez, A., Rathmell, W. K., Xie, X., & Brugarolas, J. (2013) · The Lancet Oncology · doi:10.1016/s1470-2045(12)70584-3 · Cited by 466 · see citing works
Comparison of software packages for detecting differential expression in RNA-seq studies
Seyednasrollah, F., Laiho, A., & Elo, L. L. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbt086 · Cited by 421 · see citing works
Self-citationGenetic and Functional Studies Implicate <i>HIF1</i> α as a 14q Kidney Cancer Suppressor Gene
Shen, C., Beroukhim, R., Schumacher, S. E., Zhou, J., Chang, M., Signoretti, S., & Kaelin, W. G. (2011) · Cancer Discovery · doi:10.1158/2159-8290.cd-11-0098 · Cited by 413 · see citing works
Normalization, testing, and false discovery rate estimation for RNA-sequencing data
Li, J., Witten, D. M., Johnstone, I. M., & Tibshirani, R. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr031 · Cited by 369 · see citing works
Brannon, A. R., Reddy, A., Seiler, M., Arreola, A., Moore, D. T., Pruthi, R. S., Wallen, E., Nielsen, M. E., Liu, H., Nathanson, K. L., et al. (2010) · Genes & Cancer · doi:10.1177/1947601909359929 · Cited by 332 · see citing works
Yao, M. (2002) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/94.20.1569 · Cited by 244 · see citing works
Li, S., Tighe, S. W., Nicolet, C. M., Grove, D., Levy, S., Farmerie, W., Viale, A., Wright, C., Schweitzer, P. A., Gao, Y., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2972 · Cited by 240 · see citing works
Surveillance strategies for renal cell carcinoma patients following nephrectomy.
Chin, A. I., Lam, J. S., Figlin, R. A., & Belldegrun, A. S. (2006) · Europe PMC (PubMed Central) · Cited by 199 · see citing works
Zigeuner, R. E., Hutterer, G. C., Chromecki, T. F., Imamović, A., Kampel‐Kettner, K., Rehak, P., Langner, C., & Pummer, K. (2008) · European Urology · doi:10.1016/j.eururo.2008.11.033 · Cited by 180 · see citing works
Detecting and correcting systematic variation in large-scale RNA sequencing data
Li, S., Łabaj, P. P., Zumbo, P., Sykacek, P., Shi, W., Shi, L., Phan, J., Wu, P.-Y., Wang, M., Wang, C., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.3000 · Cited by 175 · see citing works
Patard, J.-J., Rioux-Leclercq, N. C., Masson, D., Zerrouki, S., Jouan, F., Collet, N., Dubourg, C., Lobel, B., Denis, M. G., & Fergelot, P. (2009) · British Journal of Cancer · doi:10.1038/sj.bjc.6605298 · Cited by 98 · see citing works
Reproducibility-Optimized Test Statistic for Ranking Genes in Microarray Studies
Elo, L. L., Filén, S., Lahesmaa, R., & Aittokallio, T. A. (2008) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2007.1078 · Cited by 68 · see citing works
Self-citationvan Laar, R. K., Flinchum, R., Brown, N., Ramsey, J., Riccitelli, S., Heuck, C. J., Barlogie, B., & Shaughnessy, J. D. (2014) · BMC Medical Genomics · doi:10.1186/1755-8794-7-25 · Cited by 44 · see citing works
Elo, L. L., Hiissa, J., Tuimala, J., Kallio, A., Korpelainen, E., & Aittokallio, T. A. (2009) · Briefings in Bioinformatics · doi:10.1093/bib/bbp033 · Cited by 30 · see citing works
Self-citation
Flagged first, then by citations
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A survey of best practices for RNA-seq data analysis
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794 · see citing works
Self-citationSingle-Cell RNA-Seq Technologies and Related Computational Data Analysis
Chen, G., Ning, B., & Shi, T. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00317 · Cited by 913 · see citing works
Bartoschek, M., Oskolkov, N., Bocci, M., Lövrot, J., Larsson, C., Sommarin, M., Madsen, C. D., Lindgren, D., Pekar, G., Karlsson, G., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07582-3 · Cited by 826 · see citing works
Bias, robustness and scalability in single-cell differential expression analysis
Soneson, C., & Robinson, M. D. (2018) · Nature Methods · doi:10.1038/nmeth.4612 · Cited by 770 · see citing works
Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq
Bagnoli, J., Ziegenhain, C., Janjic, A., Wange, L. E., Vieth, B., Parekh, S., Geuder, J., Hellmann, I., & Enard, W. (2018) · Nature Communications · doi:10.1038/s41467-018-05347-6 · Cited by 242 · see citing works
Optimization of metabolomic data processing using NOREVA
Fu, J., Zhang, Y., Wang, Y., Zhang, H., Liu, J., Tang, J., Yang, Q., Sun, H., Qiu, W., Ma, Y., et al. (2021) · Nature Protocols · doi:10.1038/s41596-021-00636-9 · Cited by 182 · see citing works
powsimR: power analysis for bulk and single cell RNA-seq experiments
Vieth, B., Ziegenhain, C., Parekh, S., Enard, W., & Hellmann, I. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx435 · Cited by 155 · see citing works
ROTS: An R package for reproducibility-optimized statistical testing
Suomi, T., Seyednasrollah, F. S., Jaakkola, M. K., Faux, T., & Elo, L. L. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005562 · Cited by 149 · see citing works
Self-citationSingle-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods
Dal Molin, A., Baruzzo, G., & Di Camillo, B. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00062 · Cited by 107 · see citing works
Synthetic biology approaches to biological containment: pre-emptively tackling potential risks
Torres, L. L., Krüger, A., Csibra, E., Gianni, E., & Pinheiro, V. B. (2016) · Essays in Biochemistry · doi:10.1042/ebc20160013 · Cited by 103 · see citing works
scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells
Cao, Y., Zhu, J., Han, G., Jia, P., & Zhao, Z. (2017) · Genes · doi:10.3390/genes8120368 · Cited by 104 · see citing works
Ninomiya, T., Noguchi, E., Haruna, T., Hasegawa, M., Yoshida, T., Yamashita, Y., Okano, M., Yoshida, N., Haruna, S., Sakuma, Y., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-29612-2 · Cited by 91 · see citing works
Comparison of methods to detect differentially expressed genes between single-cell populations
Jaakkola, M. K., Seyednasrollah, F., Mehmood, A., & Elo, L. L. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw057 · Cited by 86 · see citing works
Self-citationChen, P., Wang, Y., Li, J., Bo, X., Wang, J., Nan, L., Wang, C., Ba, Q., Liu, H., & Wang, H. (2021) · Clinical and Translational Medicine · doi:10.1002/ctm2.462 · Cited by 79 · see citing works
A coding and non-coding transcriptomic perspective on the genomics of human metabolic disease
Timmons, J. A., Atherton, P. J., Larsson, O. S., Sood, S., Blokhin, I. O., Brogan, R. J., Volmar, C., Josse, A. R., Slentz, C. A., Wahlestedt, C., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky570 · Cited by 68 · see citing works
Schrödter, S., Braun, M., Syring, I., Klümper, N., Deng, M., Schmidt, D. H., Perner, S., Müller, S. C., & Ellinger, J. (2016) · Molecular Cancer · doi:10.1186/s12943-016-0495-5 · Cited by 68 · see citing works
Quantitative single-cell transcriptomics
Ziegenhain, C., Vieth, B., Parekh, S., Hellmann, I., & Enard, W. (2018) · Briefings in Functional Genomics · doi:10.1093/bfgp/ely009 · Cited by 58 · see citing works
The evolution of enzyme function in the isomerases
Cuesta, S. M., Furnham, N., Rahman, S. A., Sillitoe, I., & Thornton, J. M. (2014) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2014.06.002 · Cited by 55 · see citing works
Das, S., Rai, A. K., & Rai, S. N. (2022) · Entropy · doi:10.3390/e24070995 · Cited by 45 · see citing works
Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data
Baik, B., Yoon, S., & Nam, D. (2020) · PLoS ONE · doi:10.1371/journal.pone.0232271 · Cited by 47 · see citing works
Lim, S. D., Yim, W. C., Liu, D., Hu, R., Yang, X., & Cushman, J. C. (2018) · Plant Biotechnology Journal · doi:10.1111/pbi.12898 · Cited by 44 · see citing works
Porse, A., Jahn, L. J., Ellabaan, M. M. H., & Sommer, M. O. A. (2020) · Nature Communications · doi:10.1038/s41467-020-15080-8 · Cited by 42 · see citing works
Giri, A. K., Bharadwaj, S., Banerjee, P., Chakraborty, S., Parekatt, V., Rajashekar, D., Tomar, A., Ravindran, A., Basu, A., Tandon, N., et al. (2017) · Molecular Genetics and Genomics · doi:10.1007/s00438-017-1298-0 · Cited by 39 · see citing works
Guajardo‐Leiva, S., Pedrós‐Alió, C., Salgado, O., Pinto, F., & Dı́ez, B. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.02039 · Cited by 36 · see citing works
Das, S., Rai, A., Merchant, M. L., Cave, M. C., & Rai, S. N. (2021) · Genes · doi:10.3390/genes12121947 · Cited by 35 · see citing works
Koehn, L. M., Huang, Y., Habgood, M. D., Kysenius, K., Crouch, P. J., Dzięgielewska, K. M., & Saunders, N. R. (2020) · F1000Research · doi:10.12688/f1000research.24119.1 · Cited by 34 · see citing works
Reichardt, S., Budahn, H., Lamprecht, D., Riewe, D., Ulrich, D., Dunemann, F., & Kopertekh, L. (2020) · Horticulture Research · doi:10.1038/s41438-020-00412-y · Cited by 33 · see citing works
Methyllysine binding domains: Structural insight and small molecule probe development
Teske, K. A., & Hadden, M. K. (2017) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2017.04.047 · Cited by 32 · see citing works
Sinkevičiūtė, D., Aspberg, A., He, Y., Bay‐Jensen, A., & Önnerfjord, P. (2020) · BMC Rheumatology · doi:10.1186/s41927-020-00122-x · Cited by 32 · see citing works
An integrative approach to the evolution of shrimps of the genus Palaemon (Decapoda, Palaemonidae)
Carvalho, F. L., Grave, S. D., & Mantelatto, F. L. M. (2016) · Zoologica Scripta · doi:10.1111/zsc.12228 · Cited by 31 · see citing works
Improved Genome Assembly and Annotation of the Soybean Aphid ( Aphis glycines Matsumura)
Mathers, T. C. (2020) · G3 Genes Genomes Genetics · doi:10.1534/g3.119.400954 · Cited by 31 · see citing works
BCseq: accurate single cell RNA-seq quantification with bias correction
Chen, L., & Zheng, S. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky308 · Cited by 30 · see citing works
Liu, H.-M., Yang, D., Liu, Z.-F., Hu, S., Shen-hai, Y., & He, X.-W. (2019) · PLoS ONE · doi:10.1371/journal.pone.0219551 · Cited by 27 · see citing works
Intratumoral androgen levels are linked to TMPRSS2-ERG fusion in prostate cancer
Knuuttila, M., Mehmood, A., Mäki-Jouppila, J. H., Ryberg, H., Taimen, P., Knaapila, J., Ettala, O. O., Boström, P. J., Ohlsson, C., Venäläinen, M. S., et al. (2018) · Endocrine Related Cancer · doi:10.1530/erc-18-0148 · Cited by 19 · see citing works
Self-citationKogaki, T., Ohshio, I., Ura, H., Iyama, S., Kitae, K., Morie, T., Fujii, S., Sato, S., Nagata, T., Harada-Takeda, A., et al. (2021) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2021.113943 · Cited by 20 · see citing works
2D transition metal dichalcogenide nanomaterial-based miRNA biosensors
Mitrevska, K., Milosavljević, V., Gagić, M., Richtera, L., & Adam, V. (2021) · Applied Materials Today · doi:10.1016/j.apmt.2021.101043 · Cited by 20 · see citing works
Gene expression signature predicts rate of type 1 diabetes progression
Suomi, T., Starskaia, I., Ullah, U., Rasool, O., Jaakkola, M. K., Grönroos, T., Välikangas, T., Brorsson, C., Mazzoni, G., Bruggraber, S. F. A., et al. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104625 · Cited by 20 · see citing works
Self-citationMutation-induced LZTR1 polymerization provokes cardiac pathology in recessive Noonan syndrome
Busley, A. V., Gutiérrez‐Gutiérrez, Ó., Hammer, E., Koitka, F., Mirzaiebadizi, A., Steinegger, M., Pape, C., Böhmer, L., Schroeder, H., Kleinsorge, M., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114448 · Cited by 19 · see citing works
Gene set analysis for interpreting genetic studies
Pers, T. H. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw249 · Cited by 19 · see citing works
Ferguson, K., Kursch-Metz, T., Verhulst, E. C., & Pannebakker, B. A. (2020) · G3 Genes Genomes Genetics · doi:10.1534/g3.120.401344 · Cited by 16 · see citing works
Patón, V., Flores, R. O. R., Gábor, A., Badia-i-Mompel, P., Tanevski, J., Garrido‐Rodríguez, M., & Sáez-Rodríguez, J. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae552 · Cited by 15 · see citing works
Välikangas, T., Suomi, T., Chandler, C. E., Scott, A. J., Tran, B. Q., Ernst, R. K., Goodlett, D. R., & Elo, L. L. (2022) · Nature Communications · doi:10.1038/s41467-022-35564-z · Cited by 13 · see citing works
Self-citationBuchacher, T., Honkimaa, A., Välikangas, T., Lietzén, N., Hirvonen, M. K., Laiho, J. E., Sioofy‐Khojine, A., Eskelinen, E., Hyöty, H., Elo, L. L., et al. (2021) · iScience · doi:10.1016/j.isci.2021.103653 · Cited by 13 · see citing works
Self-citationCOVID-19-specific transcriptomic signature detectable in blood across multiple cohorts
Välikangas, T., Junttila, S., Rytkönen, K. T., Kukkonen-Macchi, A., Suomi, T., & Elo, L. L. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.929887 · Cited by 12 · see citing works
Self-citationNetwork-based analysis of transcriptional profiles from chemical perturbations experiments
Mulas, F., Li, A., Sherr, D. H., & Monti, S. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1536-9 · Cited by 12 · see citing works
Li, Z.-L., Fang, F., Long, Y., Zhao, Q., Wang, X.-T., Ye, Z., Meng, T.-Q., Gu, X.-L., Xiang, W.-P., Xiong, C.-L., et al. (2022) · Cell & Bioscience · doi:10.1186/s13578-022-00917-0 · Cited by 9 · see citing works
Yang, Y., Liu, X., Zhang, N., & Jiang, W. (2021) · Talanta · doi:10.1016/j.talanta.2021.122680 · Cited by 10 · see citing works
StrucBreak: A Computational Framework for Structural Break Detection in DNA Sequences
Kamal, M. S., & Nimmy, S. F. (2016) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-016-0158-7 · Cited by 8 · see citing works
da Silva, D. T., da Silva, M. F., Lima, A. M., Matos, P., Sanches, O. D. C., Matos, E. R., & Hamoy, I. G. (2021) · Parasitology International · doi:10.1016/j.parint.2021.102472 · Cited by 7 · see citing works
Iacovelli, F., & Falconi, M. (2015) · FEBS Journal · doi:10.1111/febs.13315 · Cited by 7 · see citing works
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Wang, H., Simpson, J. H., Kotra, M. E., Zhu, Y., Wickramasinghe, S., Mills, D. A., & Chiu, N. H. L. (2021) · BMC Research Notes · doi:10.1186/s13104-021-05563-2 · Cited by 6 · see citing works
Välikangas, T., Lietzén, N., Jaakkola, M. K., Krogvold, L., Eike, M. C., Kallionpää, H., Tuomela, S., Mathews, C. E., Gerling, I., Oikarinen, S., et al. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.861985 · Cited by 6 · see citing works
Self-citationWang, J., Li, F., Xu, Y., Zheng, X., Zhang, C., Hu, C., Xu, Y., Mi, W., Li, X., & Zhang, Y. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-021-02962-8 · Cited by 6 · see citing works
Biswas, B., Kumar, N., Sugimoto, M., & Hoque, M. A. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108769 · Cited by 5 · see citing works
Yang, D., & Liu, H. (2019) · Technology and Health Care · doi:10.3233/thc-199024 · Cited by 5 · see citing works
Two‐sample test withg‐modeling and its applications
Zhai, J., & Jiang, H. (2022) · Statistics in Medicine · doi:10.1002/sim.9603 · Cited by 3 · see citing works
Alaqeeli, O., & Alturki, R. (2023) · Applied Sciences · doi:10.3390/app132011512 · Cited by 4 · see citing works
Reproducibility-optimized detection of differential DNA methylation
Suni, V., Seyednasrollah, F. S., Ghimire, B. R., Junttila, S., Laiho, A., & Elo, L. L. (2020) · Epigenomics · doi:10.2217/epi-2019-0289 · Cited by 3 · see citing works
Self-citationTölken, L. A., Paulikat, A. D., Jachmann, L. H., Reder, A., Salazar, M. G., Medina, L. M. P., Michalik, S., Völker, U., Svensson, M., Norrby‐Teglund, A., et al. (2024) · Journal of Biomedical Science · doi:10.1186/s12929-024-01014-9 · Cited by 3 · see citing works
Debandi, M., Carrica, M. D. C., Hentschker, C., Baroli, C. M., Völker, U., Rodrı́guez, M. E., Surmann, K., & Lamberti, Y. (2024) · Journal of Proteome Research · doi:10.1021/acs.jproteome.3c00817 · Cited by 3 · see citing works
Ang, T. F., Salleh, A. B., Normi, Y. M., & Leow, T. C. (2018) · 3 Biotech · doi:10.1007/s13205-018-1333-9 · Cited by 3 · see citing works
Alaqeeli, O., Xing, L., & Zhang, X. (2021) · Microbiology Research · doi:10.3390/microbiolres12020022 · Cited by 2 · see citing works
Välikangas, T., Suomi, T., Chandler, C. E., Scott, A. J., Tran, B. Q., Ernst, R. K., Goodlett, D. R., & Elo, L. L. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.19.440388 · Cited by 2 · see citing works
Self-citationRNA-Seq Analysis: Methods, Applications and Challenges
vari, A., Geraci, F., Saha, I., & Bianchini, M. (2020) · Frontiers research topics · doi:10.3389/978-2-88963-705-8 · Cited by 1 · see citing works
Integrative Ranking of Enhancer Networks Facilitates the Discovery of Epigenetic Markers in Cancer
Wang, Q., Wu, Y., Vorberg, T., Eils, R., & Herrmann, C. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.664654
Class Discovery, Comparison, and Prediction Methods for RNA-Seq Data
Cephe, A., Koçhan, N., Zararsız, G. E., Eldem, V., & Zararsız, G. (2023) · IGI Global eBooks · doi:10.4018/978-1-7998-9220-5.ch123 · Cited by 1 · see citing works
Class Discovery, Comparison, and Prediction Methods for RNA-Seq Data
Cephe, A., Koçhan, N., Zararsız, G. E., Eldem, V., & Zararsız, G. (2023) · IGI Global eBooks · doi:10.4018/979-8-3693-3026-5.ch021
Sinkevičiūtė, D., Aspberg, A., He, Y., Bay‐Jensen, A., & Önnerfjord, P. (2020) · Figshare · doi:10.6084/m9.figshare.12285080
Wang, J., Li, F., Xu, Y., Zheng, X., Zhang, C., Hu, C., Xu, Y., Mi, W., Li, X., & Zhang, Y. (2021) · Figshare · doi:10.6084/m9.figshare.14936500
Wang, J., Li, F., Xu, Y., Zheng, X., Zhang, C., Hu, C., Xu, Y., Mi, W., Li, X., & Zhang, Y. (2021) · Figshare · doi:10.6084/m9.figshare.14936494
Wang, J., Li, F., Xu, Y., Zheng, X., Zhang, C., Hu, C., Xu, Y., Mi, W., Li, X., & Zhang, Y. (2021) · Figshare · doi:10.6084/m9.figshare.14936485
Wang, J., Li, F., Xu, Y., Zheng, X., Zhang, C., Hu, C., Xu, Y., Mi, W., Li, X., & Zhang, Y. (2021) · Figshare · doi:10.6084/m9.figshare.14936503
Wang, J., Li, F., Xu, Y., Zheng, X., Zhang, C., Hu, C., Xu, Y., Mi, W., Li, X., & Zhang, Y. (2021) · Figshare · doi:10.6084/m9.figshare.14936497
Wang, J., Li, F., Xu, Y., Zheng, X., Zhang, C., Hu, C., Xu, Y., Mi, W., Li, X., & Zhang, Y. (2021) · Figshare · doi:10.6084/m9.figshare.14936491
Wang, J., Li, F., Xu, Y., Zheng, X., Zhang, C., Hu, C., Xu, Y., Mi, W., Li, X., & Zhang, Y. (2021) · Figshare · doi:10.6084/m9.figshare.14936488
Wang, J., Li, F., Xu, Y., Zheng, X., Zhang, C., Hu, C., Xu, Y., Mi, W., Li, X., & Zhang, Y. (2021) · Figshare · doi:10.6084/m9.figshare.14936482
Sinkevičiūtė, D., Aspberg, A., He, Y., Bay-Jensen, A.-C., & Önnerfjord, P. (2020) · Figshare · doi:10.6084/m9.figshare.12285077
Wang, J., Li, F., Xu, Y., Zheng, X., Zhang, C., Hu, C., Xu, Y., Mi, W., Li, X., & Zhang, Y. (2021) · Figshare · doi:10.6084/m9.figshare.14936506
REACTOR: REgulon Activity analysis and Comparison Tool for single-cell transcriptOmics Research
Lindén, M., Norman, S. Z., Välikangas, T., Junttila, S., Suomi, T., Rytkönen, K. T., & Elo, L. L. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag203
Self-citationClarifying the scope and capabilities of ROTS in differential expression analysis
Suomi, T., Kettunen, J., Pusa, T., & Elo, L. L. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag335
Self-citationTranscriptomics of type 1 diabetes progression: a validation study in newly diagnosed patients
Suomi, T., Starskaia, I., Rasool, O., Ullah, U., Bruggraber, S. F. A., Marcovecchio, M. L., Hendricks, E., Overbergh, L., Peakman, M., Tree, T. I. M., et al. (2026) · EBioMedicine · doi:10.1016/j.ebiom.2026.106374
Self-citationRevisiting differential expression analysis: An updated six-dimensional comparative study
Wu, J., Lu, S., Yao, H., & Fang, Z. (2026) · PLoS ONE · doi:10.1371/journal.pone.0344709
- Authors
- Seyednasrollah, F., Rantanen, K., Jaakkola, P., & Elo, L. L.
- Affiliations
- Åbo Akademi University (FI)
- Corresponding author
- Seyednasrollah, Fatemeh
- Elo, Laura L.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-08-11
- Volume
- 44
- Issue
- 1
- Pages
- e1-e1
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC4705679
- OpenAlex
- W1932455826
- Cited by
- 75
- References
- 40
- Citations by year
- 2026: 4 · 2024: 5 · 2023: 5 · 2022: 6 · 2021: 14 · 2020: 11 · 2019: 3 · 2018: 11 · 2017: 7 · 2016: 7 · 2015: 1 · 2014: 1
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- reproducibility, clear cell renal cell carcinoma, prognostic factors, differential gene expression, gene expression quantification
- MeSH terms
- Algorithms, Carcinoma, Renal Cell, Humans, Kidney Neoplasms, Prognosis, ROC Curve, Software, Biomarkers, Tumor, Reproducibility of Results, Models, Statistical, Gene Expression Regulation, Neoplastic, Computational Biology, Internet, Kaplan-Meier Estimate, High-Throughput Nucleotide Sequencing, Datasets as Topic
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