Function reduces the processing power and memory needed to calculate modified liquid association (MLA) values for a genome by using a pre-screening method to reduce the candidate pool to triplets likely to have a high MLA value. It does this using matrix algebra to create an approximation to the direct MLA estimate for all possible pairs of X1X2|X3.

1 | ```
fastMLA(data, topn = 2000, nvec = 1, rvalue = 0.5, cut = 4, threads = detectCores())
``` |

`data` |
Matrix of numeric data, with columns representing genes and rows representing observations. |

`topn` |
Number of results to return, ordered from highest |MLA| value descending. |

`nvec` |
Numeric vector of the gene(s) to use in the X3 position of the X1X2|X3 screening. This should be a numeric vector representing the column #(s) of the gene. |

`rvalue` |
Tolerance value for LA approximation. Lower values of rvalue will cause a more thorough search, but take longer. |

`cut` |
Value passed to the GLA function to create buckets (equal to number of buckets+1). Values placing between 15-30 samples per bucket are optimal. Must be a positive integer>1. See GLA. |

`threads` |
Number of cores to use for multi-threading in correlation calculation (enableWGCNAThreads argument). See WGCNA. |

Choosing the number of bins: For example, assume that our data has 100 observations. Since values between 15-30 observations per bin are optimal, good values to choose for cut would be 5-7.

A data frame with 5 variables: the genes in positions X1, X2 and X3; the rhodiff value of the triplet; and the GLA value of the triplet. A more comprehensive discussion of these values is available in the vignette.

The data matrix must be numeric.

While this is intended to significantly reduce processing time for identifying high MLA values (and in our estimates did so by >90

Tina Gunderson

[1] Yen-Yi Ho, Giovanni Parmigiani, Thomas A Louis, and Leslie M Cope. Modeling liquid association. Biometrics, 67(1):133-141, 2011.

`LiquidAssociation`

, `parallel`

, `WGCNA`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
#to view function code
selectMethod("fastMLA", "matrix")
#
library(fastLiquidAssociation)
library(yeastCC)
data(spYCCES)
lae <- spYCCES[,-(1:4)]
### get rid of samples with high % NA elements
lae <- lae[apply(is.na(exprs(lae)),1,sum) < ncol(lae)*0.3,]
data <- t(exprs(lae))
data <- data[,1:50]
example <- fastMLA(data=data, topn=25, nvec=1:10, rvalue=1.0, cut=4)
example[1:5,]
closeAllConnections()
``` |

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