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Differential expression in RNA-seq: A matter of depth
Open accessAbstract
Next-generation sequencing (NGS) technologies are revolutionizing genome research, and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established, and additional research is needed for understanding how these data respond to differential expression analysis. In this work, we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level, and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach—NOISeq—that differs from existing methods in that it is data-adaptive and nonparametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data, and the issue of replication.
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In the citing paper
“Previous studies demonstrated that inferences about DE are generally more accurate for highly expressed genes19,20.”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [20]
Experimental — accuracy not yet measuredIn the citing paper
“Experimental results suggest that deep sequencing improves quantification and identification but might also result in the detection of transcriptional noise and off-target transcripts [10].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
In the citing paper
“Saturation curves can be used to assess the improvement in transcriptome coverage to be expected at a given sequencing depth [10].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
In the citing paper
“For example, rRNA and small RNAs should not be present in regular polyA longRNA preparations [10, 19].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
In the citing paper
“In such cases, simpler methods based on the Poisson distribution, such as DEGseq [62], or on empirical distributions (NOISeq [10]) can be an alternative”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
In the citing paper
“Increasing sequencing depth also can improve statistical power for lowly expressed genes [10, 194], and for any given sample there exists a level of sequencing at which power improvement is best achieved by increasing the number of replicates [195].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
In the citing paper
“NOISeq and NOISeqBIO (the adaptation of NOISeq for biological replication) are more efficient in avoiding false positive calls at the cost of some sensitivity but perform well with different numbers of replicates [10, 20, 212].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
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Cites
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
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
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,682
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877
A scaling normalization method for differential expression analysis of RNA-seq data
Robinson, M. D., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r25 · Cited by 7,876
Gene ontology analysis for RNA-seq: accounting for selection bias
Young, M. D., Wakefield, M. J., Smyth, G. K., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-2-r14 · Cited by 7,107
The relationship between Precision-Recall and ROC curves
Davis, J., & Goadrich, M. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143874 · Cited by 6,559
The Transcriptional Landscape of the Mammalian Genome
Carninci, P., Kasukawa, T., Katayama, S., Gough, J., Frith, M. C., Maeda, N., Oyama, R., Ravasi, T., Lenhard, B., Wells, C. A., et al. (2005) · Science · doi:10.1126/science.1112014 · Cited by 3,492
Cited by
Sorted by citations
Items 901–1,000 of 1,000
Froussios, K., Schurch, N., Mackinnon, K., Gierliński, M., Duc, C., Simpson, G. G., & Barton, G. J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz089 · Cited by 12
Yan, K., Mei, Z., Zhao, J., Prodhan, M. A. I., Obal, D., Katragadda, K., Doelling, B., Hoetker, D., Posa, D. K., He, L., et al. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.121.023868 · Cited by 11
Liu, G., Li, ., Ming, Y., Xiang, B., Zhou, X., Chen, Y., Chen, N., Abudupataer, M., Zhu, S., Sun, X., et al. (2023) · Lab on a Chip · doi:10.1039/d2lc01158a · Cited by 11
Ruiz‐Cantero, M. C., Entrena, J. M., Artacho-Cordón, A., Huerta, M. Á., Portillo-Salido, E. F., Nieto, F. R., Baeyens, J. M., Costigan, M., González‐Cano, R., & Cobos, E. J. (2024) · Journal of Neuroimmune Pharmacology · doi:10.1007/s11481-024-10144-8 · Cited by 11
Comprehensive mutational profiling identifies new driver events in cutaneous leiomyosarcoma
van der Weyden, L., Velasco‐Herrera, M. D. C., Cheema, S., Wong, K., Boccacino, J. M., Offord, V., Droop, A., Jones, D. R. A., Vermes, I., Anderson, E. L., et al. (2024) · British Journal of Dermatology · doi:10.1093/bjd/ljae386 · Cited by 11
A Note on an Exon-Based Strategy to Identify Differentially Expressed Genes in RNA-Seq Experiments
Laiho, A., & Elo, L. L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0115964 · Cited by 12
Comparative transcriptome analysis of PBMC from HIV patients pre- and post-antiretroviral therapy
Zhao, F., Ma, J., Huang, L., Deng, Y., Li, L., Zhou, Y., Li, J., Li, S., Jiang, H., Yang, H., et al. (2017) · Meta Gene · doi:10.1016/j.mgene.2017.01.004 · Cited by 10
Ye, X., Fu, M., Liu, Y., An, D., Zheng, X., Tan, B., Li, J., Cheng, J., Wang, W., & Feng, J. (2018) · Journal of Plant Physiology · doi:10.1016/j.jplph.2018.04.015 · Cited by 11
Exploring the effect of library preparation on RNA sequencing experiments
Wang, L., Felts, S. J., Keulen, V. P. V., Pease, L. R., & Zhang, Y. (2018) · Genomics · doi:10.1016/j.ygeno.2018.11.030 · Cited by 11
Yu, P., Liu, M., Zhang, B., Yu, Y., Su, E., Xie, S., Zhang, L., Yang, X., Jiang, H., Chen, R., et al. (2020) · Cell Death Discovery · doi:10.1038/s41420-020-00340-9 · Cited by 11
MUREN: a robust and multi-reference approach of RNA-seq transcript normalization
Feng, Y., & Li, L. M. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04288-0 · Cited by 10
Powerful eQTL mapping through low-coverage RNA sequencing
Schwarz, T., Boltz, T. A., Hou, K., Bot, M. H. M., Duan, C., Loohuis, L. M. O., Boks, M. P., Kahn, R. S., Ophoff, R. A., & Paşaniuc, B. (2022) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2022.100103 · Cited by 13
Riccio, G., Martínez, K. A., Ianora, A., & Lauritano, C. (2022) · Biology · doi:10.3390/biology11050771 · Cited by 11
Correia, C. N., McHugo, G. P., Browne, J. A., McLoughlin, K. E., Nalpas, N., Magee, D. A., Whelan, A. O., Villarreal‐Ramos, B., Vordermeier, H. M., Gormley, E. P., et al. (2022) · Tuberculosis · doi:10.1016/j.tube.2022.102235 · Cited by 11
Feng, Y., Liu, L., Yu, J., Chen, R., Hu, C., Wang, H., Li, D., Wang, Z., & Zhao, Z. (2023) · LWT · doi:10.1016/j.lwt.2023.114680 · Cited by 11
Laine, A., Wang, X., Ni, K., Smith, S. E. B., Najjar, R., Whitmore, L. S., Yacoub, M., Bays, A. M., Gale, M., & Mustelin, T. (2023) · Microorganisms · doi:10.3390/microorganisms11051310 · Cited by 12
Membrane gene ontology bias in sequencing and microarray obtained by housekeeping-gene analysis
Zhang, Y., Akintola, O. S., Liu, K. J. A., & Sun, B. (2015) · Gene · doi:10.1016/j.gene.2015.09.041 · Cited by 11
Li, Y., Fan, G., Dong, Y., Zhao, Z., Deng, M., Cao, X., Xu, E., & Niu, S. (2014) · Gene · doi:10.1016/j.gene.2014.09.057 · Cited by 11
Ding, J., & Lu, S. (2016) · Genetics and Molecular Research · doi:10.4238/gmr.15048735 · Cited by 11
Kim, B.-B., Kim, M., Park, Y., Ko, Y., & Park, J. (2017) · Journal of International Medical Research · doi:10.1177/0300060517701035 · Cited by 12
PPARD is an Inhibitor of Cartilage Growth in External Ears
Zhang, Z., Duan, Y., Wu, Z., Zhang, H., Ren, J., & Huang, L. (2017) · International Journal of Biological Sciences · doi:10.7150/ijbs.19714 · Cited by 12
RNA-sequence analysis of samples from patients with idiopathic adhesive capsulitis
Cui, J., Zhang, T., Xiong, J., Lü, W., Duan, L. N., Zhu, W., & Wang, D. (2017) · Molecular Medicine Reports · doi:10.3892/mmr.2017.7579 · Cited by 11
Physiological RNA dynamics in RNA-Seq analysis
Xu, Z., & Asakawa, S. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby045 · Cited by 11
Hovhannisyan, H., & Gabaldón, T. (2018) · Current topics in microbiology and immunology · doi:10.1007/82_2018_122 · Cited by 11
Hersh, C. P., Adcock, I. M., Celedon, J. C., Cho, M. H., Christiani, D. C., Himes, B. E., Kaminski, N., Mathias, R. A., Meyers, D. A., Quackenbush, J. F., et al. (2018) · Annals of the American Thoracic Society · doi:10.1513/annalsats.201810-716ws · Cited by 11
Arroyo-Pérez, E. E., González-Cerón, G., Soberón‐Chávez, G., Georgellis, D., & Servín‐González, L. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.01568 · Cited by 11
Khan, A., George, K., Jasrotia, R. S., Aravind, S., Angadi, U. B., Iquebal, M. A., Manju, K., Jaiswal, S., Umadevi, P., Rai, A. K., et al. (2019) · Genomics · doi:10.1016/j.ygeno.2019.11.017 · Cited by 11
Darr, J., Tomar, A., Lassi, M., Gerlini, R., Berti, L., Hering, A., Scheid, F., de Angelis, M. H., Witting, M. A., & Teperino, R. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.02.020 · Cited by 12
Identification of Immune Response to Sacbrood Virus Infection in Apis cerana Under Natural Condition
Deng, Y., Zhao, H., Shen, S., Yang, S., Yang, D., Deng, S., & Hou, C. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.587509 · Cited by 11
Neglected wardens: T lymphocyte ryanodine receptors
Fomina, A. F. (2021) · The Journal of Physiology · doi:10.1113/jp281722 · Cited by 11
Using RNA-seq Data to Detect Differentially Expressed Genes
Lorenz, D. J., Gill, R. S., Mitra, R., & Datta, S. (2014) · Springer eBooks · doi:10.1007/978-3-319-07212-8_2 · Cited by 11
Wang, Y., Yang, X., Yang, Y., Wang, W., Zhao, M., Liu, H., Li, D., & Hao, M. (2015) · Journal of Perinatology · doi:10.1038/jp.2015.192 · Cited by 11
First feed affects the expressions of microRNA and their targets in Atlantic cod
Bizuayehu, T. T., Furmanek, T., Karlsen, Ø., van der Meeren, T., Edvardsen, R. B., Rønnestad, I., Hamre, K., Johansen, S., & Babiak, I. (2016) · British Journal Of Nutrition · doi:10.1017/s0007114516000155 · Cited by 11
Ubrihien, R. P., Ezaz, T., Taylor, A. M., Stevens, M. M., Krikowa, F., Foster, S. D., & Maher, W. A. (2017) · Aquatic Toxicology · doi:10.1016/j.aquatox.2017.02.014 · Cited by 11
Bellés, X., & Ylla, G. (2015) · Insect Biochemistry and Molecular Biology · doi:10.1016/j.ibmb.2015.06.008 · Cited by 11
DFI: gene feature discovery in RNA-seq experiments from multiple sources
Özer, H. G., Parvin, J. D., & Huang, K. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-s8-s11 · Cited by 11
Lee, V. V., Judd, L. M., Jex, A. R., Holt, K. E., Tonkin, C. J., & Ralph, S. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.16.946699 · Cited by 11
Kalantari-Dehaghi, M., Esfahani, N. G., & Emadi‐Baygi, M. (2025) · Human Genomics · doi:10.1186/s40246-025-00884-w · Cited by 13
A practical guide to targeted single-cell RNA sequencing technologies
Moro, G., Brunner, E., & Basler, K. (2026) · Communications Biology · doi:10.1038/s42003-026-09675-y · Cited by 10
Shen, N.-X., Luo, M., Gu, W.-M., Gong, M., Lei, H.-M., Bi, L., Wang, C., Zhang, M.-C., Zhuang, G., Xu, L., et al. (2024) · Oncogene · doi:10.1038/s41388-024-03096-z · Cited by 10
van Lingen, H. J., Diez, M. S., & Saccenti, E. (2024) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2024.195058 · Cited by 14
Bessenay, A., Bisio, H., Belmudes, L., Couté, Y., Bertaux, L., Claverie, J., Abergel, C., Jeudy, S., & Legendre, M. (2024) · Nature Communications · doi:10.1038/s41467-024-52906-1 · Cited by 10
Suvanasuthi, R., Therasakvichya, S., Kanchanapiboon, P., Promptmas, C., & Chimnaronk, S. (2025) · Scientific Reports · doi:10.1038/s41598-024-84080-1 · Cited by 10
Wang, P., Liu, F., Wang, Y., Chen, H., Liu, T., Li, M., Chen, S., & Wang, D. (2025) · Plant Physiology and Biochemistry · doi:10.1016/j.plaphy.2025.109568 · Cited by 10
Darragh, K., Kay, K. M., & Ramírez, S. R. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msaf027 · Cited by 11
Differential expression analysis for sequence count data via mixtures of negative binomials
Bonafede, E. (2015) · AMS Dottorato Institutional Doctoral Theses Repository (University of Bologna) · doi:10.6092/unibo/amsdottorato/6741 · Cited by 9
Filée, J., Farhat, S., Higuet, D., Teysset, L., Marie, D., Thomas–Bulle, C., Hourdez, S., Jollivet, D., & Bonnivard, É. (2021) · Mobile DNA · doi:10.1186/s13100-021-00252-0 · Cited by 10
Efficacy of Kushen decoction on high-fat-diet-induced hyperlipidemia in rats.
Mutu, Z. J., Shilong, L., Peng, N., Yong'an, L., Qinying, A. I., Xiaojun, Y., Hongning, L., Yanhua, J. I., & Zhijun, Z. (2022) · PubMed Central · doi:10.19852/j.cnki.jtcm.20220225.002 · Cited by 10
Salt Stress Tolerance in Casuarina glauca: Insights from the Branchlets Transcriptome
Fernandes, I., Paulo, O. S., Marques, I., Sarjkar, I., Sen, A., Graça, I., Pawlowski, K., Ramalho, J. C., & Ribeiro-Barros, A. I. (2022) · Plants · doi:10.3390/plants11212942 · Cited by 10
Sangket, U., Yodsawat, P., Nuanpirom, J., & Sathapondecha, P. (2022) · PeerJ · doi:10.7717/peerj.14344 · Cited by 10
Boot, J. R., Rosser, G., Kancheva, D., Vinel, C., Lim, Y. M., Pomella, N., Zhang, X., Guglielmi, L., Sheer, D., Barnes, M. R., et al. (2022) · eLife · doi:10.7554/elife.77335 · Cited by 10
Zaldívar‐López, S., Herrera-Uribe, J., Bautista, R., Jiménez, Á., Moreno, Á., Claros, M. G., & Garrido, J. J. (2022) · Developmental & Comparative Immunology · doi:10.1016/j.dci.2022.104597 · Cited by 10
Transcriptomic changes in single yeast cells under various stress conditions
Su, Y., Xu, C., Shea, J., DeStephanis, D., & Su, Z. (2023) · BMC Genomics · doi:10.1186/s12864-023-09184-w · Cited by 12
Rayford, K. J., Cooley, A., Strode, A. W., Osi, I., Arun, A., Lima, M. F., Misra, S., Pratap, S., & Nde, P. N. (2023) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2023.1083379 · Cited by 10
Padilla-Padilla, E. A., la Rosa, C. D., Aragón, W., Ávila-Sandoval, A. K., Torres, M., Dorantes-Acosta, A. E., Arteaga-Vazquez, M. A., Formey, D., & Serrano, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0304790 · Cited by 10
Transcriptome Profile in Response to Frost Tolerance in Eucalyptus globulus
Férnandez, M., Troncoso, V., & Valenzuela, S. (2015) · Plant Molecular Biology Reporter · doi:10.1007/s11105-014-0845-7 · Cited by 10
Survey of the transcriptome of Brevibacillus borstelensis exposed to low temperature shock
Tripathy, S., Sen, R., Padhi, S. K., Sahu, D. K., Nandi, S., Mohanty, S., & Maiti, N. K. (2014) · Gene · doi:10.1016/j.gene.2014.08.030 · Cited by 10
RNA-Seq and Expression Arrays: Selection Guidelines for Genome-Wide Expression Profiling
Minnier, J., Pennock, N. D., Guo, Q., Schedin, P. S., & Harrington, C. A. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7834-2_2 · Cited by 10
Wang, X. P., Qi, X., Yang, B., Chen, S. Y., & Wang, J. Y. (2018) · Poultry Science · doi:10.3382/ps/pey318 · Cited by 10
Wang, G.-J., Zhuo, X.-R., Wang, W., Liu, X., Wang, G., & Wang, J. (2019) · BMC Genomics · doi:10.1186/s12864-019-5544-1 · Cited by 10
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Statistical Methods for RNA Sequencing Data Analysis
Li, D. (2019) · Codon Publications eBooks · doi:10.15586/computationalbiology.2019.ch6 · Cited by 10
Li, Q., Wu, Z., Wu, H., Fang, W., Chen, F., & Teng, N. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20235865 · Cited by 10
Shan, Y., Guo, D. S., Gu, Q., Li, Y., Li, Y., Chen, Y., & Guan, W. (2019) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-019-10131-7 · Cited by 10
Bui, T. T., Lee, D. D., & Selvarajoo, K. (2020) · Scientific Reports · doi:10.1038/s41598-020-74564-1 · Cited by 10
Qin, D., Zhou, Y., Zhang, P., Liu, B., Zheng, Q., & Zhang, Z. (2020) · Pest Management Science · doi:10.1002/ps.6212 · Cited by 10
Han, Q., Song, H., Yang, C., Zhang, S., Korpelainen, H., & Li, C. (2022) · Tree Physiology · doi:10.1093/treephys/tpac013 · Cited by 10
Transcriptome responses to phosphate deficiency in Poncirus trifoliata (L.) Raf
Bai, F., Chen, C., An, J., Xiao, S., Deng, X., & Pan, Z. (2014) · Acta Physiologiae Plantarum · doi:10.1007/s11738-014-1687-5 · Cited by 10
Gene Expression Analysis: Current Methods
Teo, Z. L., Savas, P. S., & Loi, S. (2016) · Springer eBooks · doi:10.1007/978-1-4939-6643-1_6 · Cited by 9
BCScreen: A gene panel to test for breast carcinogenesis in chemical safety screening
Grashow, R. G., Rosa, V. Y. D. L., Watford, S., Ackerman, J. M., & Rudel, R. A. (2017) · Computational Toxicology · doi:10.1016/j.comtox.2017.11.003 · Cited by 10
Design, challenges, and the potential of transcriptomics to understand social behavior
Lim, W. K., & Mathuru, A. S. (2020) · Current Zoology · doi:10.1093/cz/zoaa007 · Cited by 11
Zheng, H., Zhang, Y., Li, J., He, L., Wang, F., Bi, Y.-P., & Gao, J. (2020) · Plant Signaling & Behavior · doi:10.1080/15592324.2020.1777373 · Cited by 10
Andrade, Á., Hernández-Eligio, A., Tirado, A. L., Vega‐Alvarado, L., Olvera, M., Morett, E., & Juárez, K. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.626443 · Cited by 9
Wu, T., Li, Y., Chang, K.-J., Fang, J.-C., Urano, D., & Liu, M. (2024) · Genome Research · doi:10.1101/gr.278100.123 · Cited by 8
Decsi, K., Ahmed, M., Abdul-Hamid, D., Rizk, R., & Tóth, Z. (2025) · Agronomy · doi:10.3390/agronomy15061365 · Cited by 9
Heat Stress Drives Rapid Viral and Antiviral Innate Immunity Activation in Hexacorallia
Sharoni, T., Jaimes‐Becerra, A., Lewandowska, M., Aharoni, R., Voolstra, C. R., Fine, M., & Moran, Y. (2025) · Molecular Ecology · doi:10.1111/mec.70098 · Cited by 9
Hur, B., Chae, H., & Kim, S. (2015) · BMC Medical Genomics · doi:10.1186/1755-8794-8-s2-s10 · Cited by 9
Turnbull, L. B., Siwo, G., Button-Simons, K. A., Tan, A., Checkley, L. A., Painter, H. J., Llinás, M., & Ferdig, M. T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0187595 · Cited by 9
Zhang, Y., Chen, H., Yu, J., Feng, R., Chen, Z., Zhang, X., Ren, Y., Yang, G., Huang, X., & Li, G. (2022) · Veterinary Microbiology · doi:10.1016/j.vetmic.2022.109540 · Cited by 9
Jardillier, R., Koça, D., Chatelain, F., & Guyon, L. (2022) · Genes · doi:10.3390/genes13122275 · Cited by 9
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Yang, R., Liu, P., Zhang, T., Dong, H., Jing, Y., Yang, Z., Tang, S., Zhang, Y., Lyu, M., Liu, J., et al. (2023) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiad353 · Cited by 9
CDK4/6i enhances the antitumor effect of PD1 antibody by promoting TLS formation in ovarian cancer
Feng, W., Jiang, D., Xu, Y., Li, Y., Chen, L., Zhao, M., Shen, Y., Liao, W., Yang, H., & Li, J. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e19760 · Cited by 9
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Metadata
- Authors
- Tarazona, S., García-Alcalde, F., Dopazo, J., Ferrer, A., & Conesa, A.
- Affiliations
- Centro de Investigacion Principe Felipe (ES)
- Universitat Politècnica de València (ES)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2011-09-08
- Volume
- 21
- Issue
- 12
- Pages
- 2213-2223
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC3227109
- OpenAlex
- W2161106528
- Cited by
- 1,632
- References
- 42
- Citations by year
- 2026: 45 · 2025: 61 · 2024: 90 · 2023: 70 · 2022: 104 · 2021: 171 · 2020: 156 · 2019: 162 · 2018: 151 · 2017: 129 · 2016: 131 · 2015: 128 · 2014: 123 · 2013: 71 · 2012: 37
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- RiuNet (Universitat Politècnica de València) (submittedVersion, cc-by-nc-nd)
- Funders
- Generalitat Valenciana
- Ministerio de Ciencia e Innovación (BIO2008-04212)
- Instituto de Salud Carlos III (RD06/0020)
- 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 › Single-cell and spatial transcriptomics
- Keywords
- RNA-seq, differential gene expression, sequencing depth, false discovery rate, nonparametric statistics, transcript detection, sequencing noise, replication, gene expression profiling
- MeSH terms
- Algorithms, Gene Expression Regulation, Humans, Models, Genetic, Expressed Sequence Tags, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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