Description Usage Arguments Details Value Note Author(s) References See Also Examples

Three approaches are supplied in this function, consensus PCA (CPCA), generalized CCA (GCCA) and multiple co-inertia analsyis (MCIA).

1 2 3 |

`x` |
A |

`ncomp` |
An integer; the number of components to calculate. To calculate more components requires longer computational time. |

`method` |
A character string could be one of c("globalScore", "blockScore", "blockLoading"). The "globalScore" approach equals consensus PCA; The "blockScore" approach equals generalized canonical correlation analysis (GCCA); The "blockLoading" approach equals multiple co-inertia anaysis (MCIA); |

`k` |
The absolute number (if k >= 1) or the proportion (if 0<k<1) of non-zero coefficients for the variable loading vectors. It could be a single value or a vector has the same length as x so the sparsity of individual matrix could be different. |

`center` |
Logical; if the variables should be centered |

`scale` |
Logical; if the variables should be scaled |

`option` |
A charater string could be one of c("lambda1", "inertia", "uniform") to indicate how the different matrices should be normalized. If "lambda1", the matrix is divided by its the first singular value, if "inertia", the matrix is divided by its total inertia (sum of square), if "uniform", none of them would be done. |

`maxiter` |
Integer; Maximum number of iterations in the algorithm |

`moa` |
Logical; whether the output should be converted to an object of class |

`verbose` |
Logical; whether the process (# of PC) should be printed |

`svd.solver` |
A charater string could be one of c("svd", "fast.svd", "propack"). The default "fast.svd " has a good compromise between the robustness and speed. "propack" is the fastest but may failed to converge in practice. |

details need to update

An object of class `moa-class`

(if `moa=TRUE`

) or
an `list`

object contains the following elements:

`tb`

- the block scores

`pb`

- the block loadings

`t`

- the global scores

`w`

- the wegihts of block scores to construct the global scor

no note now

Chen Meng

reference need to be updated

see `moa`

for non-iterative algorithms for multi-block PCA.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | ```
data("NCI60_4arrays")
tumorType <- sapply(strsplit(colnames(NCI60_4arrays$agilent), split="\\."), "[", 1)
colcode <- as.factor(tumorType)
levels(colcode) <- c("red", "green", "blue", "cyan", "orange",
"gray25", "brown", "gray75", "pink")
colcode <- as.character(colcode)
moa <- mbpca(NCI60_4arrays, ncomp = 10, k = "all", method = "globalScore", option = "lambda1",
center=TRUE, scale=FALSE)
plot(moa, value="eig", type=2)
r <- bootMbpca(moa, mc.cores = 1, B=6, replace = FALSE, resample = "sample")
moas <- mbpca(NCI60_4arrays, ncomp = 3, k = 0.1, method = "globalScore", option = "lambda1",
center=TRUE, scale=FALSE)
scr <- moaScore(moa)
scrs <- moaScore(moas)
diag(cor(scr[, 1:3], scrs))
layout(matrix(1:2, 1, 2))
plot(scrs[, 1:2], col=colcode, pch=20)
legend("topright", legend = unique(tumorType), col=unique(colcode), pch=20)
plot(scrs[, 2:3], col=colcode, pch=20)
gap <- moGap(moas, K.max = 12, cluster = "hcl")
gap$nClust
hcl <- hclust(dist(scrs))
cls <- cutree(hcl, k=4)
clsColor <- as.factor(cls)
levels(clsColor) <- c("red", "blue", "orange", "pink")
clsColor <- as.character((clsColor))
heatmap(t(scrs[hcl$order, ]), ColSideColors = colcode[hcl$order], Rowv = NA, Colv=NA)
heatmap(t(scrs[hcl$order, ]), ColSideColors = clsColor[hcl$order], Rowv = NA, Colv=NA)
genes <- moaCoef(moas)
genes$nonZeroCoef$agilent.V1.neg
``` |

mengchen18/mogsa documentation built on Dec. 1, 2018, 7:13 p.m.

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