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Dimension reduction methods are commonly applied to high-throughput biological datasets. However, the results can be hindered by confounding factors, either biological or technical in origin. In this study, we extend principal component analysis (PCA) to propose AC-PCA for simultaneous dimension reduction and adjustment for confounding (AC) variation. We(More)
Microarray and RNA-sequencing technologies have enabled rapid quantification of the transcriptomes in a large number of samples. Although dimension reduction methods are commonly applied to transcriptome datasets for visualization and interpretation of the sample variations, the results can be hindered by confounding factors, either biological or technical.(More)
In our recent paper, we showed that in exponential family, contrastive divergence (CD) with fixed learning rate will give asymptotically consistent estimates [11]. In this paper, we establish consistency and convergence rate of CD with annealed learning rate ηt. Specifically, suppose CD-m generates the sequence of parameters {θt}t≥0 using an i.i.d. data(More)
Dimension reduction methods are commonly applied to highthroughput biological datasets. However, the results can be hindered by confounding factors, either biologically or technically originated. In this study, we propose a Principal Component Analysis-based approach to Adjust for Confounding variation (AC-PCA). We show that AC-PCA can adjust for variations(More)
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