Perform JIVE Decompositions for Multi-Source Data
Performs the JIVE decompositions on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data. It provides two methods of rank selection when the rank is unknown, a permutation test and a BIC selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.
Michael J. O'Connell and Eric F. Lock
Maintainer: Michael J. O'Connell <firstname.lastname@example.org>
Lock, E. F., Hoadley, K. A., Marron, J. S., & Nobel, A. B. (2013). Joint and individual variation explained (JIVE) for integrated analysis of multiple data types. The Annals of Applied Statistics, 7(1), 523-542.
O'Connell, M. J., & Lock, E.F. (2016). R.JIVE for Exploration of Multi-Source Molecular Data. Bioinformatics advance access: 10.1093/bioinformatics/btw324.
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## Not run: set.seed(10) ##Load data that were simulated as in Section 2.4 of Lock et al., 2013, ##with rank 1 joint structure, and rank 1 individual structure for each dataset data(SimData) # Using default method ("perm") Results <- jive(SimData) summary(Results) # Using BIC rank selection BIC_result <- jive(SimData, method="bic") summary(BIC_result) ## End(Not run) ###Load the permutation results data(SimResults) # Visualize results showVarExplained(Results) # showVarExplained is also called by the "jive" S3 class default plot method #show heatmaps showHeatmaps(Results) #show PCA plots showPCA(Results,1,c(1,1))
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