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

cor.unbalance estimates correlation from replicated data of unequal number of replicates.
different from `cor.balance`

, `cor.unbalance`

takes a pair of variables
at a time because of unequal number of replicates. the variance of each row of the data MUST equal to 1 (see example below)

1 | ```
cor.unbalance(x, m1, m2)
``` |

`x` |
data matrix, column represents samples (conditions), and row represents variables (genes), see example below for format information |

`m1` |
number of replicates for one variable (gene) |

`m2` |
number of replicates for another variable (gene) |

The multivariate correlation estimator assumes replicated omics data are iid samples from the multivariate normal distribution. It is derived by maximizing the likelihood function. Note that the off-diagonal elements in the returned correlation matrix (G by G) is the average of off-diagonals of MLE of correlation matrix of a pair of variables (m1+m2 by m1+m2).

A correlation matrix containing only one distinct correlation coefficient for the pair of variables (genes)

Dongxiao Zhu and Youjuan Li

Zhu, D and Li Y. 2007. Multivariate Correlation Estimator for Inferring Functional Relationships from Replicated 'OMICS' data. Submitted.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
library("CORREP")
d0 <- NULL
for(l in 1:10)
d0 <- rbind(d0, rnorm(8))
## The simulated data corresponds to the real-world data of 2 genes and 10 conditions, gene expression
## profiles were replicated 3 and 5 times.
## Note this function can only take calculate correlation matrix between two genes at a time.
d0<- t(d0)
## This step is to make the standard deviation of each replicate equal to 1
## so that we can model the covariance matrix as correlation matrix.
d0.std <- apply(d0, 1, function(x) x/sd(x))
M <- cor.unbalance(t(d0.std), m1=3, m2=5)
``` |

CORREP documentation built on Nov. 8, 2020, 5:09 p.m.

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