RSIR: regularized sliced inverse regression for motif discovery. MOTIVATION: Identification of transcription factor binding motifs (TFBMs) is a crucial first step towards the understanding of regulatory circuitries controlling the expression of genes. In this paper, we propose a novel procedure called regularized sliced inverse regression (RSIR) for identifying TFBMs. RSIR follows a recent trend to combine information contained in both gene expression measurements and genes’ promoter sequences. Compared with existing methods, RSIR is efficient in computation, very stable for data with high dimensionality and high collinearity, and improves motif detection sensitivities and specificities by avoiding inappropriate model specification. RESULTS: We compare RSIR with SIR and stepwise regression based on simulated data and find that RSIR has a lower false positive rate. We also demonstrate an excellent performance of RSIR by applying it to the yeast amino acid starvation data and cell cycle data. AVAILABILITY: Matlab programs are available upon request from the authors.

References in zbMATH (referenced in 17 articles )

Showing results 1 to 17 of 17.
Sorted by year (citations)

  1. Girard, Stéphane; Lorenzo, Hadrien; Saracco, Jérôme: Advanced topics in sliced inverse regression (2022)
  2. Jlassi, Ines; Saracco, Jérôme: Variable importance assessment in sliced inverse regression for variable selection (2019)
  3. Wang, Tao; Chen, Mengjie; Zhao, Hongyu; Zhu, Lixing: Estimating a sparse reduction for general regression in high dimensions (2018)
  4. Liu, Yang; Chiaromonte, Francesca; Li, Bing: Structured ordinary least squares: a sufficient dimension reduction approach for regressions with partitioned predictors and heterogeneous units (2017)
  5. Adragni, Kofi P.; Karmakar, Moumita: A sequential test for variable selection in high dimensional complex data (2015)
  6. Fan, Jianqing; Ke, Zheng Tracy; Liu, Han; Xia, Lucy: QUADRO: a supervised dimension reduction method via Rayleigh quotient optimization (2015)
  7. Lian, Heng: Functional sufficient dimension reduction: convergence rates and multiple functional case (2015)
  8. Chavent, Marie; Girard, Stéphane; Kuentz-Simonet, Vanessa; Liquet, Benoit; Nguyen, Thi Mong Ngoc; Saracco, Jérôme: A sliced inverse regression approach for data stream (2014)
  9. Jiang, Bo; Liu, Jun S.: Variable selection for general index models via sliced inverse regression (2014)
  10. Yu, Yue; Chen, Zhihong; Yang, Jie: Cluster-based regularized sliced inverse regression for forecasting macroeconomic variables (2014)
  11. Wu, Qiang; Liang, Feng; Mukherjee, Sayan: Kernel sliced inverse regression: regularization and consistency (2013)
  12. Yao, Wei-Ting; Wu, Han-Ming: Isometric sliced inverse regression for nonlinear manifold learning (2013)
  13. Li, Yehua; Hsing, Tailen: Deciding the dimension of effective dimension reduction space for functional and high-dimensional data (2010)
  14. Wen, Xuerong Meggie: On sufficient dimension reduction for proportional censorship model with covariates (2010)
  15. Yoo, Jae Keun; Patterson, Becky S.; Datta, Susmita: An OLS-based predictor test for a single-index model for predicting transcription rate from histone acetylation level (2009)
  16. Bernard-Michel, C.; Gardes, L.; Girard, S.: A note on sliced inverse regression with regularizations (2008)
  17. Li, Lexin; Yin, Xiangrong: Sliced inverse regression with regularizations (2008)