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
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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June 11, 2023 (Update: )
This post is for Ko, S., German, C. A., Jensen, A., Shen, J., Wang, A., Mehrotra, D. V., Sun, Y. V., Sinsheimer, J. S., Zhou, H., & Zhou, J. J. (2022). GWAS of longitudinal trajectories at biobank scale. The American Journal of Human Genetics, 109(3), 433–445.
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May 05, 2023
This post is for Gandy, A., & Matcham, T. J. (2022). On concordance indices for models with time-varying risk (arXiv:2208.03213). arXiv.
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April 21, 2023
This note is for Schaid, D. J., Sinnwell, J. P., Batzler, A., & McDonnell, S. K. (2022). Polygenic risk for prostate cancer: Decreasing relative risk with age but little impact on absolute risk. American Journal of Human Genetics, 109(5), 900–908.
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March 28, 2023
This note is for Zhang, Z., Reinikainen, J., Adeleke, K. A., Pieterse, M. E., & Groothuis-Oudshoorn, C. G. M. (2018). Time-varying covariates and coefficients in Cox regression models. Annals of Translational Medicine, 6(7), 121.
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March 28, 2023
This note is for Yan, J., & Huang, J. (2012). Model Selection for Cox Models with Time-Varying Coefficients. Biometrics, 68(2), 419–428.
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February 10, 2023
This note is for Luan, B., Lee, Y., & Zhu, Y. (2021). Predictive Model Degrees of Freedom in Linear Regression. ArXiv:2106.15682 [Math].
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January 24, 2023
This note is based on Choi, S. W., Mak, T. S.-H., & O’Reilly, P. F. (2020). Tutorial: A guide to performing polygenic risk score analyses. Nature Protocols, 15(9), Article 9.
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December 28, 2022 (Update: )
This post is for Wang, B., Mezlini, A. M., Demir, F., Fiume, M., Tu, Z., Brudno, M., Haibe-Kains, B., & Goldenberg, A. (2014). Similarity network fusion for aggregating data types on a genomic scale. Nature Methods, 11(3), Article 3. and a related paper Ruan, P., Wang, Y., Shen, R., & Wang, S. (2019). Using association signal annotations to boost similarity network fusion. Bioinformatics, 35(19), 3718–3726.
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December 15, 2022 (Update: )
This note is for Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015). LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics, 47(3), 291–295.
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November 21, 2022
This post is based on
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November 12, 2022 (Update: )
This note is for Jiang, W., & Yu, W. (2017). Controlling the joint local false discovery rate is more powerful than meta-analysis methods in joint analysis of summary statistics from multiple genome-wide association studies. Bioinformatics, 33(4), 500–507.
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November 04, 2022 (Update: )
This note is for Blondel, M., Teboul, O., Berthet, Q., & Djolonga, J. (2020). Fast Differentiable Sorting and Ranking (arXiv:2002.08871). arXiv.
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October 31, 2022
This note is for Cao, X., & Lee, K. (2021). Joint Bayesian Variable and DAG Selection Consistency for High-dimensional Regression Models with Network-structured Covariates. Statistica Sinica.
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October 30, 2022
This note is for Wang, W., Baladandayuthapani, V., Morris, J. S., Broom, B. M., Manyam, G., & Do, K.-A. (2013). iBAG: Integrative Bayesian analysis of high-dimensional multiplatform genomics data. Bioinformatics, 29(2), 149–159.
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October 29, 2022
This note is for Ni, Y., Stingo, F. C., Ha, M. J., Akbani, R., & Baladandayuthapani, V. (2019). Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics. Journal of the American Statistical Association, 114(525), 48–60.
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October 12, 2022
This note is for Tang, D., Park, S., & Zhao, H. (2022). SCADIE: Simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedure. Genome Biology, 23(1), 129.
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October 10, 2022
This note is based on Jingyi Jessica Li’s talk on Song, D., Wang, Q., Yan, G., Liu, T., & Li, J. J. (2022). A unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics (p. 2022.09.20.508796). bioRxiv.
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October 09, 2022
This post is based on
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October 08, 2022
This note is for Prof. Dong Xu’s talk on Wang, J., Ma, A., Chang, Y., Gong, J., Jiang, Y., Qi, R., Wang, C., Fu, H., Ma, Q., & Xu, D. (2021). ScGNN is a novel graph neural network framework for single-cell RNA-Seq analyses. Nature Communications, 12(1), Article 1.
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October 06, 2022
This note is based on
- Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations (arXiv:2002.05709). arXiv.
- Ji, W., Deng, Z., Nakada, R., Zou, J., & Zhang, L. (2021). The Power of Contrast for Feature Learning: A Theoretical Analysis (arXiv:2110.02473). arXiv.
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October 02, 2022 (Update: )
This post is based on Rizopoulos, D. (2017). An Introduction to the Joint Modeling of Longitudinal and Survival Data, with Applications in R. 235.
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September 20, 2022
This post is for the talk at Yale given by Prof. Ting Ye based on the paper Ye, T., Shao, J., & Kang, H. (2020). Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization (arXiv:1911.09802). arXiv.
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August 25, 2022 (Update: )
This note is for Todd, J. L., Vinisko, R., Liu, Y., Neely, M. L., Overton, R., Flaherty, K. R., Noth, I., Newby, L. K., Lasky, J. A., Olman, M. A., Hesslinger, C., Leonard, T. B., Palmer, S. M., & Belperio, J. A. (2020). Circulating matrix metalloproteinases and tissue metalloproteinase inhibitors in patients with idiopathic pulmonary fibrosis in the multicenter IPF-PRO Registry cohort. BMC Pulmonary Medicine, 20(1), 64.
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July 21, 2022
This note is for Jiang, Y., & Liu, C. (2022). Estimation of Over-parameterized Models via Fitting to Future Observations (arXiv:2206.01824). arXiv.
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