Comparison of methods to detect differentially expressed genes between single-cell populations
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We compared five statistical methods to detect differentially expressed genes between two distinct single-cell populations. Currently, it remains unclear whether differential expression methods developed originally for conventional bulk RNA-seq data can also be applied to single-cell RNA-seq data analysis. Our results in three diverse comparison settings showed marked differences between the different methods in terms of the number of detections as well as their sensitivity and specificity. They, however, did not reveal systematic benefits of the currently available single-cell-specific methods. Instead, our previously introduced reproducibility-optimization method showed good performance in all comparison settings without any single-cell-specific modifications.
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Tools built specifically to handle single-cell data do not appear to perform better than tools created for bulk data analysis.
“The result is reduced reproducibility with real data, leading to the conclusion that tools built specifically to handle single-cell data do not appear to perform better than tools created for bulk data analysis22–24.”
From A practical solution to pseudoreplication bias in single-cell studies (2021) · cites this paper as [24]
The Conclusions say the comparison found no systematic benefit for single-cell-specific methods overall. But they also say the two methods originally developed for bulk RNA-seq, DESeq and Limma, were unsuitable for unmodified single-cell analysis; the general-purpose method ROTS performed well. The claim therefore overstates the finding as a comparison specifically against bulk-data tools. (LLM, conf=0.87)
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Cites
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
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829 · 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
Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., Slichter, C. K., Miller, H. W., McElrath, M. J., Prlic, M., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0844-5 · Cited by 3,159 · see citing works
De novo assembly of bacterial transcriptomes from RNA-seq data
Tjaden, B. C. (2015) · Genome biology · doi:10.1186/s13059-014-0572-2 · Cited by 1,695 · see citing works
Bayesian approach to single-cell differential expression analysis
Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348 · see citing works
RNA-Seq gene expression estimation with read mapping uncertainty
Li, B., Ruotti, V., Stewart, R. M., Thomson, J. A., & Dewey, C. N. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp692 · Cited by 1,198 · see citing works
Single-cell sequencing-based technologies will revolutionize whole-organism science
Shapiro, E., Biezuner, T., & Linnarsson, S. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3542 · Cited by 1,177 · see citing works
Computational and analytical challenges in single-cell transcriptomics
Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170 · see citing works
Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq
Islam, S., Kjällquist, U., Moliner, A., Zajac, P., Fan, J.-B., Lönnerberg, P., & Linnarsson, S. (2011) · Genome Research · doi:10.1101/gr.110882.110 · Cited by 949 · see citing works
Kowalczyk, M. S., Tirosh, I., Heckl, D., Rao, T. N., Dixit, A., Haas, B. J., Schneider, R. K., Wagers, A. J., Ebert, B. L., & Regev, A. (2015) · Genome Research · doi:10.1101/gr.192237.115 · Cited by 778 · 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
From RNA-seq reads to differential expression results
Oshlack, A., Robinson, M. D., & Young, M. D. (2010) · Genome biology · doi:10.1186/gb-2010-11-12-220 · Cited by 697 · see citing works
The heterogeneity of human CD127+ innate lymphoid cells revealed by single-cell RNA sequencing
Björklund, Å. K., Forkel, M., Picelli, S., Kónya, V., Theorell, J., Friberg, D., Sandberg, R., & Mjösberg, J. (2016) · Nature Immunology · doi:10.1038/ni.3368 · Cited by 491 · 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-citationZhang, W., Chen, J., Yang, Y., Tang, Y., Shang, J., & Shen, B. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017915 · Cited by 250 · see citing works
Entering the era of single-cell transcriptomics in biology and medicine
Sandberg, R. (2013) · Nature Methods · doi:10.1038/nmeth.2764 · Cited by 242 · see citing works
ROTS: reproducible RNA-seq biomarker detector—prognostic markers for clear cell renal cell cancer
Seyednasrollah, F., Rantanen, K., Jaakkola, P., & Elo, L. L. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv806 · Cited by 75 · see citing works
Self-citationOptimization of Statistical Methods Impact on Quantitative Proteomics Data
Pursiheimo, A., Vehmas, A. P., Afzal, S., Suomi, T., Chand, T., Strauss, L., POUTANEN, M. H., Rokka, A., Corthals, G. L., & Elo, L. L. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00183 · Cited by 72 · see citing works
Self-citationReproducibility-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-citationMouse Embryonic Stem Cell-Derived Spheres with Distinct Neurogenic Potentials
Moliner, A., Ernfors, P., Ibáñez, C. F., & Andäng, M. (2008) · Stem Cells and Development · doi:10.1089/scd.2007.0211 · Cited by 37 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Scenic: single-cell regulatory network inference and clustering.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520 · see citing works
Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing
Kim, C. R., Gao, R., Sei, E., Brandt, R., Hartman, J., Hatschek, T., Crosetto, N., Foukakis, T., & Navin, N. E. (2018) · Cell · doi:10.1016/j.cell.2018.03.041 · Cited by 1,091 · see citing works
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071 · see citing works
Orchestrating single-cell analysis with Bioconductor
Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945 · 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
Crowell, H. L., Soneson, C., Germain, P.-L., Calini, D., Collin, L., Raposo, C., Malhotra, D., & Robinson, M. D. (2020) · Nature Communications · doi:10.1038/s41467-020-19894-4 · Cited by 515 · see citing works
A practical solution to pseudoreplication bias in single-cell studies
Zimmerman, K. D., Espeland, M. A., & Langefeld, C. D. (2021) · Nature Communications · doi:10.1038/s41467-021-21038-1 · Cited by 362 · see citing works
Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database
Zappia, L., Phipson, B., & Oshlack, A. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006245 · Cited by 304 · see citing works
Wang, T., Li, B., Nelson, C. E., & Nabavi, S. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2599-6 · Cited by 286 · see citing works
“To assess TPs, we used the already-published top 1000 DE genes that are validated through qRT-PCR experiments [47] as a gold standard gene set [21, 40, 42].”
MethodChen, S., & Mar, J. C. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2217-z · Cited by 235 · see citing works
Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications
Van den Berge, K., Perraudeau, F., Soneson, C., Love, M. I., Risso, D., Vert, J.-P., Robinson, M. D., Dudoit, S., & Clement, L. (2018) · Genome Biology · doi:10.1186/s13059-018-1406-4 · Cited by 228 · 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 144 · see citing works
Self-citationGermain, P., Sonrel, A., & Robinson, M. D. (2020) · Genome biology · doi:10.1186/s13059-020-02136-7 · Cited by 139 · see citing works
Mosquera, J. V., Auguste, G., Wong, D., Turner, A. W., Hodonsky, C. J., Alvarez-Yela, A. C., Song, Y., Cheng, Q., Lino Cardenas, C. L., Theofilatos, K., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113380 · Cited by 123 · see citing works
Comparative analysis of differential gene expression tools for RNA sequencing time course data
Spies, D., Renz, P. F., Beyer, T. A., & Ciaudo, C. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx115 · Cited by 118 · see citing works
Tang, W., Bertaux, F., Thomas, P., Stefanelli, C., Saint, M., Marguerat, S., & Shahrezaei, V. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz726 · Cited by 113 · see citing works
UMI-count modeling and differential expression analysis for single-cell RNA sequencing
Chen, W., Li, Y., Easton, J., Finkelstein, D., Wu, G., & Chen, X. (2018) · Genome Biology · doi:10.1186/s13059-018-1438-9 · Cited by 113 · see citing works
Junttila, S., Smolander, J., & Elo, L. L. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac286 · Cited by 109 · see citing works
Self-citationscPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
Schmid, K. T., Höllbacher, B., Cruceanu, C., Böttcher, A., Lickert, H., Binder, E. B., Theis, F. J., & Heinig, M. (2021) · Nature Communications · doi:10.1038/s41467-021-26779-7 · Cited by 108 · see citing works
Single-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
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
Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges
Liu, J., Fan, Z., Zhao, W., & Zhou, X. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.655536 · Cited by 84 · see citing works
bigSCale: an analytical framework for big-scale single-cell data
Iacono, G., Mereu, E., Guillaumet-Adkins, A., Corominas, R., Cuscó, I., Rodríguez-Esteban, G., Gut, M., Pérez-Jurado, L. A., Gut, I., & Heyn, H. (2018) · Genome Research · doi:10.1101/gr.230771.117 · Cited by 79 · see citing works
Reproducibility of Methods to Detect Differentially Expressed Genes from Single-Cell RNA Sequencing
Mou, T., Deng, W., Gu, F., Yudi Pawitan, Y., & Vu, T. N. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2019.01331 · Cited by 73 · see citing works
Saint, M., Bertaux, F., Tang, W., Sun, X.-M., Gamé, L., Köferle, A., Bähler, J., Shahrezaei, V., & Marguerat, S. (2019) · Nature Microbiology · doi:10.1038/s41564-018-0330-4 · Cited by 63 · 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
Wang, T., & Nabavi, S. (2018) · Methods · doi:10.1016/j.ymeth.2018.04.017 · Cited by 56 · see citing works
Li, X., Cooper, N. G. F., O’Toole, T. E., & Rouchka, E. C. (2020) · BMC Genomics · doi:10.1186/s12864-020-6502-7 · Cited by 56 · see citing works
Das, S., Rai, A. K., & Rai, S. N. (2022) · Entropy · doi:10.3390/e24070995 · Cited by 45 · see citing works
Silva, J. C., Domingues, D. S., Menotti, D., Hungría, M., & Lopes, F. M. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.051 · Cited by 42 · 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
Dusart, P., Hallström, B. M., Renné, T., Odeberg, J., Uhlén, M., & Butler, L. M. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.09.088 · Cited by 34 · see citing works
Fast identification of differential distributions in single-cell RNA-sequencing data with waddR
Schefzik, R., Flesch, J., & Gonçalves, Â. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab226 · Cited by 34 · 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
Gagnon, J., Pi, L., Ryals, M. M., Wan, Q., Hu, W., Ouyang, Z., Zhang, B., & Li, K. (2022) · Life · doi:10.3390/life12060850 · Cited by 28 · see citing works
Wang, R., Zheng, X., Wang, J., Wan, S., Song, F., Wong, M. H., Leung, K. S., & Cheng, L. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac002 · Cited by 34 · see citing works
Jabato, F. M., Caballero, J., Rojano, E., Mateo, C. R., Sanz, P., Pérez, B., Gallego, D., Seoane, P., Ranea, J. A. G., & Perkins, J. R. (2021) · Scientific Reports · doi:10.1038/s41598-021-94343-w · Cited by 27 · see citing works
Orrapin, S., Thongkumkoon, P., Udomruk, S., Moonmuang, S., Sutthitthasakul, S., Yongpitakwattana, P., Pruksakorn, D., & Chaiyawat, P. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241512337 · Cited by 28 · see citing works
Yoon, S., & Nam, D. (2017) · BMC Genomics · doi:10.1186/s12864-017-3809-0 · Cited by 23 · see citing works
Mallik, S., & Zhao, Z. (2019) · Genes · doi:10.3390/genes10080611 · Cited by 23 · see citing works
Postmortem Interval Leads to Loss of Disease-Specific Signatures in Brain Tissue
Olney, K. C., Gibson, K. A., Cadiz, M. P., Rahimzadeh, N., Swarup, V., & Fryer, J. D. (2025) · eneuro · doi:10.1523/eneuro.0505-24.2025 · Cited by 22 · see citing works
Modeling group heteroscedasticity in single-cell RNA-seq pseudo-bulk data
You, Y., Dong, X., Wee, Y. K., Maxwell, M. J., Alhamdoosh, M., Smyth, G. K., Hickey, P. F., Ritchie, M. E., & Law, C. W. (2023) · Genome biology · doi:10.1186/s13059-023-02949-2 · Cited by 23 · see citing works
Nault, R., Saha, S., Bhattacharya, S., Dodson, J. P., Sinha, S., Maiti, T., & Zacharewski, T. R. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac019 · Cited by 20 · see citing works
Zou, J., Deng, F., Wang, M., Zhang, Z., Liu, Z., Zhang, X., Hua, R., Chen, K., Zou, X., & Hao, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac180 · Cited by 20 · see citing works
Yan, J., Zeng, Q., & Wang, X. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05889-1 · Cited by 17 · see citing works
zingeR: unlocking RNA-seq tools for zero-inflation and single cell applicationsPreprint
den Berge, K. V., Soneson, C., Love, M. I., Robinson, M. D., & Clement, L. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/157982 · Cited by 17 · see citing works
Das, S., & Rai, S. N. (2021) · Genomics · doi:10.1016/j.ygeno.2021.02.014 · Cited by 15 · see citing works
SCRIP: an accurate simulator for single-cell RNA sequencing data
Qin, F., Luo, X., Xiao, F., & Cai, G. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab824 · Cited by 12 · see citing works
Revolutionizing Cancer Immunology: The Power of Next-Generation Sequencing Technologies
Singer, M., & Anderson, A. C. (2019) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-18-0281 · Cited by 13 · see citing works
Zhang, Z., Zhang, Y., Evans, P., Chinwalla, A., & Taylor, D. M. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/122747 · Cited by 11 · see citing works
iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects
Liu, Y., Zhao, J., Adams, T. S., Wang, N., Schupp, J. C., Wu, W., McDonough, J. E., Chupp, G. L., Kaminski, N., Wang, Z., et al. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05432-8 · Cited by 11 · see citing works
matchSCore: Matching Single-Cell Phenotypes Across Tools and ExperimentsPreprint
Mereu, E., Iacono, G. L., Guillaumet-Adkins, A., Moutinho, C., Lunazzi, G., Santos, C. P., Miguel-Escalada, I., Ferrer, J. P., Real, F. X., Gut, I. G., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/314831 · Cited by 9 · see citing works
Zhang, L., Thapa, I., Haas, C. S., & Bastola, D. K. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3140-7 · Cited by 10 · see citing works
Lessons from single-cell transcriptome analysis of oxygen-sensing cells
Zhou, T., & Matsunami, H. (2017) · Cell and Tissue Research · doi:10.1007/s00441-017-2682-0 · Cited by 8 · see citing works
Zhang, S., Xie, L., Cui, Y., Carone, B. R., & Chen, Y. (2022) · Biomolecules · doi:10.3390/biom12081130 · Cited by 9 · see citing works
Xiong, Y.-X., & Zhang, X.-F. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae072 · Cited by 9 · see citing works
Fan, J. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9057-3_7 · Cited by 5 · see citing works
Differential gene expression analysis in single-cell RNA sequencing data
Wang, T., & Nabavi, S. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217650 · Cited by 5 · see citing works
Das, S., Pradhan, U. K., & Rai, S. N. (2022) · Current Bioinformatics · doi:10.2174/1574893617666220823114108 · Cited by 5 · see citing works
Dhapola, P., Eldeeb, M., Ugale, A. S., Olofzon, R., Erlandsson, E., Soneji, S., Bryder, D., & Göran, K. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.30.321216 · 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
Cell annotation using scRNA-seq data: A protein-protein interaction network approach
Senra, D., Guisoni, N., & Diambra, L. A. (2023) · MethodsX · doi:10.1016/j.mex.2023.102179 · Cited by 4 · see citing works
Alaqeeli, O., & Alturki, R. (2023) · Applied Sciences · doi:10.3390/app132011512 · Cited by 4 · see citing works
Biasini, A., Smith, A., Abdulkarim, B., da Silva, M. F., Tan, J. Y., & Marques, A. C. (2020) · iScience · doi:10.1016/j.isci.2020.101291 · Cited by 4 · see citing works
Nguyen, T. M., Jeevan, J. J., Xu, N., & Chen, J. Y. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.09.006 · Cited by 4 · see citing works
Pseudoreplication bias in single-cell studies; a practical solution
Zimmerman, K. D., Espeland, M. A., & Langefeld, C. D. (2020) · doi:10.1101/2020.01.15.906248 · Cited by 332 · 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-citationIdentification of Differential Gene Groups From Single-Cell Transcriptomes Using Network Entropy
Gan, Y., Liang, S., Wei, Q., & Zou, G. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.588041 · Cited by 2 · see citing works
Zhou, L., & Pan, Q. (2022) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2022.107733 · Cited by 2 · see citing works
Alaqeeli, O., Xing, L., & Zhang, X. (2021) · Microbiology Research · doi:10.3390/microbiolres12020022 · Cited by 2 · see citing works
Single-Cell Transcriptome Analysis in Melanoma Using Network Embedding
Wang, L., Liu, F., Du, L., & Qin, G. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.700036 · Cited by 2 · see citing works
Cell type identification from single-cell transcriptomes in melanoma
Huo, Q., Yin, Y., Liu, F., Ma, Y., Wang, L., & Qin, G. (2021) · BMC Medical Genomics · doi:10.1186/s12920-021-01118-3 · Cited by 1 · see citing works
Latent Variable Modelling and Variational Inference for scRNA-seq Differential Expression Analysis
Godinho, J. R. S., Carvalho, A. M., & Vinga, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-79290-9_6 · Cited by 1 · see citing works
RNA-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
Doulah, M. S. U. (2022) · Bioinformatics & Proteomics Open Access Journal · doi:10.23880/bpoj-16000150 · Cited by 1 · see citing works
High Spatial Resolution Profiling in Tree Species
Giacomello, S., Delhomme, N., Niittylä, T., Tuominen, H., & Street, N. R. (2019) · Wiley eBooks · doi:10.1002/9781119312994.apr0688 · Cited by 1 · see citing works
Decoding the heterogeneity of skin in homeostasis and regeneration at single-cell resolution
Joost, S. (2018) · Figshare
Netskar, H. (2019) · NORA - Norwegian Open Research Archives
Polar Gini Curve: a Technique to Discover Single-cell Biomarker Using 2D Visual InformationPreprint
Nguyen, T. M., Jeevan, J. J., Xu, N., & Chen, J. Y. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.04.977140
Zou, J., Wang, M., Zhang, Z., Liu, Z., Zhang, X., Hua, R., Chen, K., Zou, X., & Hao, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.18.469072
Zhao, H. C., Guo, Y., Ma, Y., Chen, Y., Sun, H., Sun, D., Wu, N., & Jin, Y. (2021) · Annals of Translational Medicine · doi:10.21037/atm-21-6401
Handling the Cellular Complex Systems in Alzheimer’s Disease Through a Graph Mining Approach
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Farhan, S. M. K., Fiorini, M. R., Li, J., Fon, E. A., & Thomas, R. A. (2024) · Research Square · doi:10.21203/rs.3.rs-5471740/v1
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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-citation
- Authors
- Jaakkola, M. K., Seyednasrollah, F., Mehmood, A., & Elo, L. L.
- Affiliations
- University of Turku (FI)
- Corresponding author
- Jaakkola, Maria K.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-07-03
- Volume
- 18
- Issue
- 5
- Pages
- bbw057
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC5862313
- OpenAlex
- W2468855952
- Cited by
- 86
- References
- 21
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 6 · 2022: 12 · 2021: 16 · 2020: 12 · 2019: 11 · 2018: 12 · 2017: 11
- FWCI
- 5.89
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Briefings in Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UTUPub (University of Turku) (submittedVersion)
- Funders
- Turun Yliopisto
- Turun yliopiston tutkijakoulu
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- 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
- Keywords
- differentially expressed genes, single-cell RNA sequencing, method comparison, sensitivity and specificity
- MeSH terms
- RNA, Reproducibility of Results, Gene Expression, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis
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