View source: R/logistic_reg_histology.R

histo_logit_cov | R Documentation |

These functions were developed for the genetic analysis of cancer subtypes, where "histology" is considered the phenotype, and copy number the genotype. The association is carried out using logistic regresion by 'glm' using 'family = "binary"', as this recreates the standard model of quantitative genetics P = G + E. Alternate arguments of 'family' have no been tested.

```
histo_logit_cov(
cnr,
trait,
pheno0,
pheno1,
covar,
exclude.cluster = "HC",
family = "binomial",
na.action = "na.exclude",
...
)
```

`cnr` |
a cnr bundle |

`trait` |
character, name of the trait of interest to analyze. Must be a column in the phenotype matrix (Y). e.g. "binary1" |

`pheno0` |
character, phenotype(s) to use as baseline, e.g. "0" |

`pheno1` |
character, phenotype(s) to use as alternate, e.g. "1" |

`covar` |
character, model covariates to include in the model, e.g. "category1" |

`exclude.cluster` |
character, list of clusters to exclude, e.g. hypersegmented, Stroma, etc. Default "HC" |

`family` |
character, description of the error distribution and link function to be used in the model. See glm for details. Default "binomial" |

`na.action` |
character, handling of NA. default is "na.exclude" |

`...` |
additional arguments passed to glm |

a CNR object with results from a logistic regression analysis (family = "binomial") with effect estimates, and p-values attached to the chromInfo and gene.index matrices.

Results columns are "Estimate", "Std.Error", "z.value", "p.value", and "q.value"; with the phenotype comparison pre-apended as <pheno0>.vs.<pheno1>.cv<covar>.lr.<value>. Using grade as an example the columns would be 0.vs.1..lr.Estimate, 0.vs.1.quantitative1.lr.Std.Error, 0.vs.1.quantitative1.lr.z.value, 0.vs.1.quantitative1.lr.p.value, and 0.vs.1.quantitative1.lr.q.value

```
data(cnr)
cnr <- histo_logit_cov(cnr, trait = "binary1",
pheno0 = 0, pheno1 = 1, covar = "category1")
cnr <- histo_logit_cov(cnr, trait = "category1",
pheno0 = "A", pheno1 = c("B", "C"), covar = "category2")
cnr <- histo_logit_cov(cnr, trait = "category2",
pheno0 = c("X", "Y"), pheno1 = "Z", covar = "category1")
```

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