giLikelihood | R Documentation |

This function computes the genomic instability likelihood

giLikelihood( inferCNV, recompute = TRUE, distros = c(1, 3), tumor = NULL, normal = NULL )

`inferCNV` |
InferCNV-class object |

`recompute` |
Logical, whether the model fits should be re-computed |

`distros` |
Vector of 2 integers indicating the minimum and maximum number of Gaussian models to fit |

`tumor` |
Optional vector of integers indicating the Gaussians considered as tumors |

`normal` |
Optional vector of integers indicating the Gaussians considered as normal. This is only useful when no null model has been provided for the analysis |

Updated inferCNV-class object with gi_likelihood slot

[genomicInstabilityScore()] to estimate the genomic instability score for each cell in the dataset, and [inferCNV()] to infer the enrichment of loci-blocks in the gene expression data.

eh <- ExperimentHub::ExperimentHub() dset <- eh[["EH5419"]] tpm_matrix <- SummarizedExperiment::assays(dset)$TPM set.seed(1) tpm_matrix <- tpm_matrix[, sample(ncol(tpm_matrix), 500)] cnv <- inferCNV(tpm_matrix) cnv <- genomicInstabilityScore(cnv) cnv <- giLikelihood(cnv, distros=c(3, 3), tumor=2:3) print(cnv$gi_fit) plot(density(cnv$gi_likelihood, from=0, to=1))

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