Description Usage Arguments Value Examples

This function is the implementation of the iteratively re-weighted least squares
approach for estimating surrogate variables. As a by product, this function
produces estimates of the probability of being an empirical control. This function first
applies a moderated log transform as described in Leek 2014 before calculating the surrogate
variables. See the function `empirical.controls`

for a direct estimate of the empirical controls.

1 2 3 |

`dat` |
The transformed data matrix with the variables in rows and samples in columns |

`mod` |
The model matrix being used to fit the data |

`mod0` |
The null model being compared when fitting the data |

`n.sv` |
The number of surogate variables to estimate |

`controls` |
A vector of probabilities (between 0 and 1, inclusive) that each gene is a control. A value of 1 means the gene is certainly a control and a value of 0 means the gene is certainly not a control. |

`method` |
For empirical estimation of control probes use "irw". If control probes are known use "supervised" |

`vfilter` |
You may choose to filter to the vfilter most variable rows before performing the analysis. vfilter must be NULL if method is "supervised" |

`B` |
The number of iterations of the irwsva algorithm to perform |

`numSVmethod` |
If n.sv is NULL, sva will attempt to estimate the number of needed surrogate variables. This should not be adapted by the user unless they are an expert. |

`constant` |
The function takes log(dat + constant) before performing sva. By default constant = 1, all values of dat + constant should be positive. |

sv The estimated surrogate variables, one in each column

pprob.gam: A vector of the posterior probabilities each gene is affected by heterogeneity

pprob.b A vector of the posterior probabilities each gene is affected by mod

n.sv The number of significant surrogate variables

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
library(zebrafishRNASeq)
data(zfGenes)
filter = apply(zfGenes, 1, function(x) length(x[x>5])>=2)
filtered = zfGenes[filter,]
genes = rownames(filtered)[grep("^ENS", rownames(filtered))]
controls = grepl("^ERCC", rownames(filtered))
group = as.factor(rep(c("Ctl", "Trt"), each=3))
dat0 = as.matrix(filtered)
mod1 = model.matrix(~group)
mod0 = cbind(mod1[,1])
svseq = svaseq(dat0,mod1,mod0,n.sv=1)$sv
plot(svseq,pch=19,col="blue")
``` |

sva documentation built on May 2, 2018, 2:54 a.m.

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