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How to cite

CytePool has no paper yet. Cite the software with the version you ran, which cytepool.__version__ gives and every result’s provenance record keeps:

CytePool, version <version>, https://github.com/NygenAnalytics/CytePool

Cite also the papers that define the methods you used. CytePool reimplements them, and the credit for the methods belongs to their authors.

FunctionsCite
limma_voom, voomvoom Law et al., 2014; limma Ritchie et al., 2015; empirical Bayes moderation Smyth, 2004
limma_trendlimma-trend Law et al., 2014; limma Ritchie et al., 2015Smyth, 2004
robust=True in the functions abovePhipson et al., 2016
quality_weights=True, sample_quality_weights, voom_with_quality_weightsRitchie et al., 2006Liu et al., 2015
prior_weights="n_cells"dreamlet Hoffman et al., 2026
dream_voom, voom_with_dream_weights, moderated_fixed_effect_testsdream Hoffman & Roussos, 2021
fit_mixed_model, fixed_effect_tests, fixed_effect_f_testlme4 Bates et al., 2015; Satterthwaite’s tests as lmerTest computes them Kuznetsova et al., 2017; Kenward and Roger’s Kenward & Roger, 1997 as pbkrtest computes them Halekoh & Højsgaard, 2014
treatMcCarthy & Smyth, 2009
fit_quasi_likelihood, quasi_likelihood_f_testedgeR 4 Chen et al., 2025; quasi-likelihood Lund et al., 2012
fit_negative_binomial_model, common_dispersionMcCarthy et al., 2012
fit_deseq2, deseq2_wald_test, deseq2_likelihood_ratio_testDESeq2 Love et al., 2014
tmm_factorsRobinson & Oshlack, 2010
median_ratio_size_factors, rle_factorsAnders & Huber, 2010
upper_quartile_factorsBullard et al., 2010
filter_by_expressionChen et al., 2016
cameraWu & Smyth, 2012
fryGiner & Smyth, 2016
roast, mroastWu et al., 2010
romerMajewski et al., 2010
over_representationlimma’s kegga and goana Young et al., 2010; the hypergeometric test Tavazoie et al., 1999
read_gmtthe collection you read, as its provider asks; for MSigDB, Subramanian et al., 2005Liberzon et al., 2011
propellerPhipson et al., 2022
meta_analysis, differential_expression_meta_analysismetafor Viechtbauer, 2010; the estimator of the between-study variance you chose: REML Viechtbauer, 2005, DerSimonian-Laird DerSimonian & Laird, 1986 or Paule-Mandel Paule & Mandel, 1982; Knapp-Hartung Knapp & Hartung, 2003
adjust_p_valuesthe method you chose: Benjamini & Hochberg, 1995, Benjamini & Yekutieli, 2001, Holm, 1979, Hochberg, 1988 or Hommel, 1988

Each module’s documentation lists, under References, every work it implements; the Bibliography collects them. For the data of the documentation, cite Kang et al. (2018) and, for the Hallmark gene set, Liberzon et al. (2015) (Datasets).

References
  1. Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014). voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, 15(2), R29. 10.1186/gb-2014-15-2-r29
  2. Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47. 10.1093/nar/gkv007
  3. Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology, 3(1), Article 3. 10.2202/1544-6115.1027
  4. Phipson, B., Lee, S., Majewski, I. J., Alexander, W. S., & Smyth, G. K. (2016). Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. The Annals of Applied Statistics, 10(2), 946–963. 10.1214/16-AOAS920
  5. Ritchie, M. E., Diyagama, D., Neilson, J., van Laar, R., Dobrovic, A., Holloway, A., & Smyth, G. K. (2006). Empirical array quality weights in the analysis of microarray data. BMC Bioinformatics, 7(1), 261. 10.1186/1471-2105-7-261
  6. Liu, R., Holik, A. Z., Su, S., Jansz, N., Chen, K., Leong, H. S., Blewitt, M. E., Asselin-Labat, M.-L., Smyth, G. K., & Ritchie, M. E. (2015). Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses. Nucleic Acids Research, 43(15), e97. 10.1093/nar/gkv412
  7. Hoffman, G. E., Lee, D., Bendl, J., Prashant, N. M., Hong, A., Casey, C., Alvia, M., Shao, Z., Argyriou, S., Therrien, K., Venkatesh, S., Voloudakis, G., Haroutunian, V., Fullard, J. F., & Roussos, P. (2026). Efficient differential expression analysis of large-scale single-cell transcriptomics data using Dreamlet. Nature Communications, 17(1), 9597. 10.1038/s41467-026-75680-8
  8. Hoffman, G. E., & Roussos, P. (2021). Dream: powerful differential expression analysis for repeated measures designs. Bioinformatics, 37(2), 192–201. 10.1093/bioinformatics/btaa687
  9. Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. 10.18637/jss.v067.i01
  10. Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest package: tests in linear mixed effects models. Journal of Statistical Software, 82(13), 1–26. 10.18637/jss.v082.i13
  11. Kenward, M. G., & Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 53(3), 983–997. 10.2307/2533558
  12. Halekoh, U., & Højsgaard, S. (2014). A Kenward-Roger approximation and parametric bootstrap methods for tests in linear mixed models: the R package pbkrtest. Journal of Statistical Software, 59(9), 1–32. 10.18637/jss.v059.i09
  13. McCarthy, D. J., & Smyth, G. K. (2009). Testing significance relative to a fold-change threshold is a TREAT. Bioinformatics, 25(6), 765–771. 10.1093/bioinformatics/btp053
  14. Chen, Y., Chen, L., Lun, A. T. L., Baldoni, P. L., & Smyth, G. K. (2025). edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets. Nucleic Acids Research, 53(2), gkaf018. 10.1093/nar/gkaf018
  15. Lund, S. P., Nettleton, D., McCarthy, D. J., & Smyth, G. K. (2012). Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates. Statistical Applications in Genetics and Molecular Biology, 11(5), Article 8. 10.1515/1544-6115.1826
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