This note is for Boyeau, P., Bates, S., Ergen, C., Jordan, M. I., & Yosef, N. (2023). Calibrated Identification of Feature Dependencies in Single-cell Multiomics.

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This note is for Chen, Y. T., & Gao, L. L. (2023). Testing for a difference in means of a single feature after clustering (arXiv:2311.16375). arXiv.

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##### April 12, 2024

This note is for Chen, Y. T., & Witten, D. M. (2022). Selective inference for k-means clustering (arXiv:2203.15267). arXiv.

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##### March 26, 2024

This post is for Ahlmann-Eltze, C., & Huber, W. (2023). Comparison of transformations for single-cell RNA-seq data. Nature Methods, 20(5), 665–672.

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##### February 24, 2024 (Update: )

The note is for Hafemeister, C., & Satija, R. (2019). Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biology, 20(1), 296.

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##### February 09, 2024

The note is for Spector, A., & Janson, L. (2023). Controlled Discovery and Localization of Signals via Bayesian Linear Programming (arXiv:2203.17208). arXiv.

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##### February 08, 2024

This post is for González-Delgado, J., Cortés, J., & Neuvial, P. (2023). Post-clustering Inference under Dependency (arXiv:2310.11822). arXiv.

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##### February 08, 2024

This post is for Gaynor, S. M., Fagny, M., Lin, X., Platig, J., & Quackenbush, J. (2022). Connectivity in eQTL networks dictates reproducibility and genomic properties. Cell Reports Methods, 2(5), 100218.

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##### February 08, 2024

This note is for Gao, L. L., Bien, J., & Witten, D. (2022). Selective Inference for Hierarchical Clustering (arXiv:2012.02936). arXiv.

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##### February 07, 2024

This note is for Loh, P.-R., Bhatia, G., Gusev, A., Finucane, H. K., Bulik-Sullivan, B. K., Pollack, S. J., de Candia, T. R., Lee, S. H., Wray, N. R., Kendler, K. S., O’Donovan, M. C., Neale, B. M., Patterson, N., & Price, A. L. (2015). Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance components analysis. Nature Genetics, 47(12), 1385–1392.

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##### January 31, 2024 (Update: )

This post is for Liu, M., Sun, J., Herazo-Maya, J. D., Kaminski, N., & Zhao, H. (2019). Joint Models for Time-to-Event Data and Longitudinal Biomarkers of High Dimension. Statistics in Biosciences, 11(3), 614–629.

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##### January 31, 2024 (Update: )

This post is for Köhler, M., Umlauf, N., Beyerlein, A., Winkler, C., Ziegler, A.-G., & Greven, S. (2017). Flexible Bayesian additive joint models with an application to type 1 diabetes research. Biometrical Journal, 59(6), 1144–1165.

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##### January 26, 2024 (Update: )

This note is for Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R., Grabska-Barwinska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., & Kelley, D. R. (2021). Effective gene expression prediction from sequence by integrating long-range interactions. Nature Methods, 18(10), 1196–1203.

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##### January 24, 2024

This note is based on Shao, J. (2003). Mathematical statistics (2nd ed). Springer. and Hwang, J. (2019). Note on Edgeworth Expansions and Asymptotic Reﬁnements of Percentile t-Bootstrap Methods. Bootstrap Methods.

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##### January 23, 2024

The post is for Lee, A. F. S., & Gurland, J. (1977). One-Sample t-Test When Sampling from a Mixture of Normal Distributions. The Annals of Statistics, 5(4), 803–807.

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##### January 22, 2024

This post is for Zou, Y., Carbonetto, P., Wang, G., & Stephens, M. (2022). Fine-mapping from summary data with the “Sum of Single Effects” model. PLOS Genetics, 18(7), e1010299.

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##### January 22, 2024

This note is for Wang, G., Sarkar, A., Carbonetto, P., & Stephens, M. (2020). A Simple New Approach to Variable Selection in Regression, with Application to Genetic Fine Mapping. Journal of the Royal Statistical Society Series B: Statistical Methodology, 82(5), 1273–1300.

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##### January 19, 2024

This post is for Taylor, J., & Tibshirani, R. J. (2015). Statistical learning and selective inference. Proceedings of the National Academy of Sciences of the United States of America, 112(25), 7629–7634.

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##### January 19, 2024

This post is for Tibshirani, R. J., Taylor, J., Lockhart, R., & Tibshirani, R. (2016). Exact Post-Selection Inference for Sequential Regression Procedures. Journal of the American Statistical Association, 111(514), 600–620.

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##### January 15, 2024 (Update: )

This post is for Dai, C., Lin, B., Xing, X., & Liu, J. S. (2023). A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models. Journal of the American Statistical Association, 118(543), 1551–1565.

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##### January 10, 2024 (Update: )

This post is based on vignettes of MMRM R package: https://openpharma.github.io/mmrm/main/index.html

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##### January 06, 2024 (Update: )

This post is for Chen, Shuxiao, Sizun Jiang, Zongming Ma, Garry P. Nolan, and Bokai Zhu. “One-Way Matching of Datasets with Low Rank Signals.” arXiv, October 3, 2022.

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##### December 08, 2023 (Update: )

The post is for Neufeld, Anna, Lucy L Gao, Joshua Popp, Alexis Battle, and Daniela Witten. “Inference after Latent Variable Estimation for Single-cell RNA Sequencing Data.” Biostatistics, December 13, 2022, kxac047.

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##### December 04, 2023

This post is for Tenha, Lovemore, and Mingzhou Song. “Statistical Evidence for the Presence of Trajectory in Single-cell Data.” BMC Bioinformatics 23, no. Suppl 8 (August 16, 2022): 340.

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##### December 04, 2023

This post is for Song, Dongyuan, Kexin Li, Xinzhou Ge, and Jingyi Jessica Li. “ClusterDE: A Post-Clustering Differential Expression (DE) Method Robust to False-Positive Inflation Caused by Double Dipping,” 2023

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##### November 26, 2023

This post is for Pinheiro, José C., and Douglas M. Bates. “Approximations to the Log-Likelihood Function in the Nonlinear Mixed-Effects Model.” Journal of Computational and Graphical Statistics 4, no. 1 (1995): 12–35.

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##### November 25, 2023

This post is for Zhang, Shu, Ran Xu, Caiming Xiong, and Chetan Ramaiah. “Use All the Labels: A Hierarchical Multi-Label Contrastive Learning Framework,” 16660–69, 2022.

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##### November 20, 2023

This post is for two papers on Hierarchical multi-label classification (HMC), which imposes a hierarchy constraint on the classes.

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##### November 16, 2023

This note is for Obozinski, Guillaume, Gert Lanckriet, Charles Grant, Michael I. Jordan, and William Stafford Noble. “Consistent Probabilistic Outputs for Protein Function Prediction.” Genome Biology 9 Suppl 1, no. Suppl 1 (2008): S6.

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##### October 13, 2023

This note is for Ghazanfar, Shila, Yingxin Lin, Xianbin Su, David Ming Lin, Ellis Patrick, Ze-Guang Han, John C. Marioni, and Jean Yee Hwa Yang. “Investigating Higher-Order Interactions in Single-cell Data with scHOT.” Nature Methods 17, no. 8 (August 2020): 799–806.

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##### September 22, 2023

This note is for Navarro-García, M., Guerrero, V., & Durban, M. (2023). On constrained smoothing and out-of-range prediction using P-splines: A conic optimization approach. Applied Mathematics and Computation, 441, 127679.

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##### September 21, 2023

This note is for Turlach, B. A. (2005). Shape constrained smoothing using smoothing splines. Computational Statistics, 20(1), 81–104.

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##### September 21, 2023

This note is for Papp, D., & Alizadeh, F. (2014). Shape-Constrained Estimation Using Nonnegative Splines. Journal of Computational and Graphical Statistics, 23(1), 211–231.

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##### September 21, 2023

This note is for Murray, K., Müller, S., & Turlach, B. (2016). Fast and flexible methods for monotone polynomial fitting. Journal of Statistical Computation and Simulation, 86, 1–21.

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##### September 21, 2023

This note is for Groeneboom, P., & Jongbloed, G. (2023). Confidence intervals in monotone regression (arXiv:2303.17988). arXiv.

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##### September 14, 2023 (Update: )

This note is for Hou, W., Ji, Z., Chen, Z., Wherry, E. J., Hicks, S. C., & Ji, H. (2021). A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples (p. 2021.07.10.451910). bioRxiv.

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##### September 14, 2023

The note is for Van den Berge, K., Roux de Bézieux, H., Street, K., Saelens, W., Cannoodt, R., Saeys, Y., Dudoit, S., & Clement, L. (2020). Trajectory-based differential expression analysis for single-cell sequencing data. Nature Communications, 11(1), Article 1.

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##### September 14, 2023

This note is for Garg, S., Tsipras, D., Liang, P., & Valiant, G. (2023). What Can Transformers Learn In-Context? A Case Study of Simple Function Classes (arXiv:2208.01066). arXiv.

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##### August 28, 2023

This note is for Craiu, R. V., Gong, R., & Meng, X.-L. (2023). Six Statistical Senses. Annual Review of Statistics and Its Application, 10(1), 699–725.

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##### July 31, 2023

This post is for Van den Berge, K., Roux de Bézieux, H., Street, K., Saelens, W., Cannoodt, R., Saeys, Y., Dudoit, S., & Clement, L. (2020). Trajectory-based differential expression analysis for single-cell sequencing data. Nature Communications, 11(1), Article 1.

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##### July 27, 2023

The note is for Song, D., & Li, J. J. (2021). PseudotimeDE: Inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data. Genome Biology, 22(1), 124.

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##### July 27, 2023

This post is for Lin, X., Tian, T., Wei, Z., & Hakonarson, H. (2022). Clustering of single-cell multi-omics data with a multimodal deep learning method. Nature Communications, 13(1), Article 1.

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##### July 14, 2023 (Update: )

This note is for Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A., & Murali, T. M. (2020). Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nature Methods, 17(2), Article 2.

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##### July 13, 2023

This post is for Natri, H. M., Azodi, C. B. D., Peter, L., Taylor, C. J., Chugh, S., Kendle, R., Chung, M., Flaherty, D. K., Matlock, B. K., Calvi, C. L., Blackwell, T. S., Ware, L. B., Bacchetta, M., Walia, R., Shaver, C. M., Kropski, J. A., McCarthy, D. J., & Banovich, N. E. (2023). Cell type-specific and disease-associated eQTL in the human lung (p. 2023.03.17.533161). bioRxiv.

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##### July 10, 2023

Kraven, L. M., Taylor, A. R., Molyneaux, P. L., Maher, T. M., McDonough, J. E., Mura, M., Yang, I. V., Schwartz, D. A., Huang, Y., Noth, I., Ma, S. F., Yeo, A. J., Fahy, W. A., Jenkins, R. G., & Wain, L. V. (2023). Cluster analysis of transcriptomic datasets to identify endotypes of idiopathic pulmonary fibrosis. Thorax, 78(6), 551–558.

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##### July 10, 2023

This post is for Fanidis, D., Pezoulas, V. C., Fotiadis, D. Ι., & Aidinis, V. (2023). An explainable machine learning-driven proposal of pulmonary fibrosis biomarkers. Computational and Structural Biotechnology Journal, 21, 2305–2315.

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##### June 30, 2023

This post is for Cui, H., Wang, C., Maan, H., & Wang, B. (2023). scGPT: Towards Building a Foundation Model for Single-cell Multi-omics Using Generative AI (p. 2023.04.30.538439). bioRxiv.

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##### June 29, 2023

The post is for Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature Methods, 15(12), Article 12.

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##### June 11, 2023 (Update: )

This post is for Marchetti-Bowick, M., Yin, J., Howrylak, J. A., & Xing, E. P. (2016). A time-varying group sparse additive model for genome-wide association studies of dynamic complex traits. Bioinformatics, 32(19), 2903–2910.

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##### June 11, 2023 (Update: )

The note is for Das, K., Li, J., Wang, Z., Tong, C., Fu, G., Li, Y., Xu, M., Ahn, K., Mauger, D., Li, R., & Wu, R. (2011). A dynamic model for genome-wide association studies. Human Genetics, 129(6), 629–639.

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