RSIR: regularized sliced inverse regression for motif discovery.
BACKGROUND
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.


BACKGROUND
Matlab programs are available upon request from the authors.
Zhong
Wenxuan
W
Department of Statistics, Harvard University, Cambridge, MA 02138, USA.
Zeng
Peng
P
Ma
Ping
P
Liu
Jun S
JS
Zhu
Yu
Y
D000465:Algorithms; D020816:Amino Acid Motifs; D002453:Cell Cycle; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004247:DNA / Q000378:metabolism; D005189:False Positive Reactions; D005656:Fungal Proteins / Q000378:metabolism; D015966:Gene Expression Regulation, Fungal; D005800:Genes, Fungal / Q000235:genetics; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D012044:Regression Analysis; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D012680:Sensitivity and Specificity; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
D005656:Fungal Proteins; D004247:DNA