STATISTICAL correlationCOVARIANCE matricesCANONICAL correlation (Statistics)NUISANCESWe consider asymptotically exact inference on the leading canonical correlation directions and strengths between two high-dimensional vectors under sparsity restrictions. In this regard, our main contribution is de...
reducedDimNames(13): PCA UMAP ... TSNE.CCA TSNE.HARMONY mainExpName: RNA altExpNames(0): garnett_markers A tibble: 34 × 10 Groups: gene_short_name [34] gene_id gene_short_name cell_group marker_score mean_expression fraction_expressing specificity ...
however, a prior knowledge of group structure is needed and only the group effect of one type of data is discussed. Motivated by this, in this paper we develop a more general group sparse CCA method. This model has the following advantages: 1) feature selection will be ...
[Lecture Notes in Computer Science] Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 Volume 9900 || Species Preserved and Exclusive Structural Connections Revealed by Sparse CCA 来自 onAcademic 喜欢 0 阅读量: 20 作者:
Chen, X. & Liu, H. An efficient optimization algorithm for structured sparse cca, with applications to eqtl mapping.Statistics in Biosciences4, 3–26 (2012). ArticleGoogle Scholar Yan, J. et al. Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm...
Santucciu C, Maestrale C, Madau L, Attene S, Cancedda MG, Demontis F, Tilocca MG, Saba M, Macciocu S, Carta A, et al: Association of N176K and L141F dimorphisms of the PRNP gene with lack of pathological prion protein deposition in placentas of naturally and experimentally scrap...
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After projecting the fNIRS dataset to MNI space for each subject, we used the 12 regions of interest identified in AtlasViewer (see preprocessing, Sect. 2.4.3). Hence, the final fNIRS data matrix was ∈ Rsamples×ROIs R300×12 . Since CCA requires the same number of data samples (...
transformations for imaging and genetic features so that the correlation between imaging and genetic can be maximized. In practice, the sparse CCA (SCCA) method is adopted to find a sparse (i.e., only a few components) set of features through regularization, such as with least absolute ...
GC-reverse: CCACATAGCGTAAAAGGAGCA This paper N/A ChR-forward: TCTGCCTGGGTCTGTGTTATGGTGC This paper N/A ChR-reverse: TGGTCTTGCGAATGTCGCCGTGG This paper N/A Halo-forward: TGGATGTTCCATCTGCTTCTG This paper N/A YFP-reverse: TTGCCGGTGGTGCAGATGAA This paper N/A td-forward: CCGCCAAGCTGAA...