SavedAdd your journal

Journals / Black Sea Journal of Agriculture / 2026 / Cilt: 9 Sayı: 5

Benchmarking Dimensionality Reduction Methods for Livestock Transcriptomic Data: A Comparative Analysis of Visualization, Clustering, and Classification Performance

Lütfi Bayyurt(TOKAT GAZIOSMANPASA UNIVERSITY FACULTY OF AGRICULTURE DEPARTMENT OF ZOOTECHNICS)0000-0003-2613-9302
Pages
679–688
Publication Date
2026-09-15
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

For the GSE24560 dataset, t-SNE was the most successful method in both clustering and classification analyses (Accuracy=80.59%, AUC=0.9165, F1-score=0.8057), ranking first in overall performance.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Dimensionality reduction methods play a critical role in the analysis and visualization of high-dimensional transcriptomic datasets.
📊
Key Findings & Data:In this study, the clustering and classification performances of Principal Component Analysis (PCA), Kernel Principal Component Analysis (Kernel PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) were compared using two independent livestock microarray datasets (GSE20552 and GSE24560) obtained from the GEO database.
💡
Conclusion & Impact:For the GSE24560 dataset, t-SNE was the most successful method in both clustering and classification analyses (Accuracy=80.59%, AUC=0.9165, F1-score=0.8057), ranking first in overall performance.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Transcriptomics · Livestock genomics · PCA

Abstract

Dimensionality reduction methods play a critical role in the analysis and visualization of high-dimensional transcriptomic datasets. In this study, the clustering and classification performances of Principal Component Analysis (PCA), Kernel Principal Component Analysis (Kernel PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) were compared using two independent livestock microarray datasets (GSE20552 and GSE24560) obtained from the GEO database. The performance of the methods was evaluated using Silhouette score, Davies–Bouldin index (DBI), Calinski–Harabasz index(CHI), accuracy, area under the receiver operating characteristic curve (AUC), and F1-score. Examination of the results revealed that t-SNE exhibited the highest clustering performance on the GSE20552 dataset, whereas the highest classification success was achieved by PCA (Accuracy=92.5%, AUC=0.9750, F1-score=0.9278). For the GSE24560 dataset, t-SNE was the most successful method in both clustering and classification analyses (Accuracy=80.59%, AUC=0.9165, F1-score=0.8057), ranking first in overall performance. Based on average rank values, the methods were ordered as t-SNE, PCA, Kernel PCA, and UMAP. The findings indicate that the choice of dimensionality reduction method significantly influences downstream analytical outcomes in livestock transcriptomic data. While t-SNE generally demonstrated the strongest performance, PCA was found to provide high classification accuracy in certain datasets. These results offer a comparative and application-oriented methodological framework for selecting appropriate dimensionality reduction techniques in livestock transcriptomic studies.

Özet

Dimensionality reduction methods play a critical role in the analysis and visualization of high-dimensional transcriptomic datasets. In this study, the clustering and classification performances of Principal Component Analysis (PCA), Kernel Principal Component Analysis (Kernel PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) were compared using two independent livestock microarray datasets (GSE20552 and GSE24560) obtained from the GEO database. The performance of the methods was evaluated using Silhouette score, Davies–Bouldin index (DBI), Calinski–Harabasz index(CHI), accuracy, area under the receiver operating characteristic curve (AUC), and F1-score. Examination of the results revealed that t-SNE exhibited the highest clustering performance on the GSE20552 dataset, whereas the highest classification success was achieved by PCA (Accuracy=92.5%, AUC=0.9750, F1-score=0.9278). For the GSE24560 dataset, t-SNE was the most successful method in both clustering and classification analyses (Accuracy=80.59%, AUC=0.9165, F1-score=0.8057), ranking first in overall performance. Based on average rank values, the methods were ordered as t-SNE, PCA, Kernel PCA, and UMAP. The findings indicate that the choice of dimensionality reduction method significantly influences downstream analytical outcomes in livestock transcriptomic data. While t-SNE generally demonstrated the strongest performance, PCA was found to provide high classification accuracy in certain datasets. These results offer a comparative and application-oriented methodological framework for selecting appropriate dimensionality reduction techniques in livestock transcriptomic studies.

🔖

Cite This Article

Bayyurt, L. (2026). Benchmarking Dimensionality Reduction Methods for Livestock Transcriptomic Data: A Comparative Analysis of Visualization, Clustering, and Classification Performance. Black Sea Journal of Agriculture, Cilt: 9 Sayı: 5, 679–688. https://doi.org/10.47115/bsagriculture.1968808

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (32)

  1. Aggarwal, C. C., Hinneburg, A., & Keim, D. A. (2001). On the surprising behavior of distance metrics in high dimensional space. In International Conference on Database Theory (pp. 420–434). Springer, Berlin, Heidelberg.
  2. Astuti, P. K., Ayoob, A., Strausz, P., Vakayil, B., Kumar, S. H., & Kusza, S. (2024). Climate change and dairy farming sustainability: A causal loop paradox and its mitigation scenario. Heliyon, 10(3).
  3. Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I. W. H., Ng, L. G., & Newell, E. W. (2019). Dimensionality reduction for visualizing single-cell data using UMAP. Nature Biotechnology, 37(1), 38–44.
  4. Bellman, R. E. (2015). Adaptive control processes: A guided tour. Princeton University Press.
  5. Caliński, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics - Theory and Methods, 3(1), 1–27.
  6. Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Mortazavi, A., & Ana, C. (2016). A survey of best practices for RNA-seq data analysis. Genome Biology, 17(1), 13.
  7. Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, (2), 224–227.
  8. Diaz-Papkovich, A., Anderson-Trocmé, L., Ben-Eghan, C., & Gravel, S. (2021). UMAP reveals cryptic population structure and phenotype heterogeneity in large genomic cohorts. PLOS Genetics, 17(7), e1008432.
  9. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.
  10. Hastie, T., Tibshirani, R., Friedman, J., & Franklin, J. (2005). The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer, 27(2), 83-85.
  11. Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). John Wiley & Sons.
  12. Huang, D., Jiang, F., Li, K., Tong, G., & Zhou, G. (2022). Scaled PCA: A new approach to dimension reduction. Management Science, 68(3), 1678–1695.
  13. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). Linear model selection and regularization. In An introduction to statistical learning: with applications in R (pp. 203-264). New York, NY: Springer New York.
  14. Jolliffe, I. (2025). Principal component analysis. In International Encyclopedia of Statistical Science (pp. 1945–1948). Springer, Berlin, Heidelberg.
  15. Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202.
  16. Kadarmideen, H. N. (2014). Genomics to systems biology in animal and veterinary sciences: Progress, lessons and opportunities. Livestock Science, 166, 232–248.
  17. Kobak, D., & Berens, P. (2019). The art of using t-SNE for single-cell transcriptomics. Nature Communications, 10(1), 5416.
  18. Lever, J., Krzywinski, M., & Altman, N. (2017). Principal component analysis. Nature Methods, 14(7), 641–643.
  19. McInnes, L., Healy, J., & Melville, J. (2018). UMAP: Uniform manifold approximation and projection for dimension reduction (arXiv:1802.03426). arXiv.
  20. Mika, S., Rätsch, G., Weston, J., Schölkopf, B., & Müller, K.-R. (1999). Fisher discriminant analysis with kernels. In Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (pp. 41–48). IEEE.
  21. Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559–572.
  22. Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.
  23. Schölkopf, B., Smola, A., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.
  24. Scholz, M., Kaplan, F., Guy, C. L., Kopka, J., & Selbig, J. (2005). Non-linear PCA: A missing data approach. Bioinformatics, 21(20), 3887–3895.
  25. Shashank, C. G., Sejian, V., Silpa, M. V., Devaraj, C., Madhusoodan, A. P., Rebez, E. B., & Dunshea, F. R. (2024). Climate resilience in farm animals: Transcriptomics-based alterations in differentially expressed genes and stress pathways. BioTech, 13(4), 49.
  26. Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427–437.
  27. Stark, R., Grzelak, M., & Hadfield, J. (2019). RNA sequencing: The teenage years. Nature Reviews Genetics, 20(11), 631–656.
  28. Sun, S., Zhu, J., Ma, Y., & Zhou, X. (2019). Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis. Genome Biology, 20(1), 269.
  29. van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.
  30. Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: A revolutionary tool for transcriptomics. Nature Reviews Genetics, 10(1), 57–63.
  31. Xiang, R., Wang, W., Yang, L., Wang, S., Xu, C., & Chen, X. (2021). A comparison for dimensionality reduction methods of single-cell RNA-seq data. Frontiers in Genetics, 12, 646936.
  32. Yan, Y., Zhu, S., Jia, M., Chen, X., Qi, W., Gu, F., & Sun, H. Z. (2024). Advances in single-cell transcriptomics in animal research. Journal of Animal Science and Biotechnology, 15(1), 102.