Randomly permutes a subset of data, and calculates projected PCA scores for these 'random' genes. Then compares the PCA scores for the 'random' genes with the observed PCA scores to determine statistical signifance. End result is a p-value for each gene's association with each principal component.

JackStraw( object, reduction = "pca", assay = NULL, dims = 20, num.replicate = 100, prop.freq = 0.01, verbose = TRUE, maxit = 1000 )

object | Seurat object |
---|---|

reduction | DimReduc to use. ONLY PCA CURRENTLY SUPPORTED. |

assay | Assay used to calculate reduction. |

dims | Number of PCs to compute significance for |

num.replicate | Number of replicate samplings to perform |

prop.freq | Proportion of the data to randomly permute for each replicate |

verbose | Print progress bar showing the number of replicates that have been processed. |

maxit | maximum number of iterations to be performed by the irlba function of RunPCA |

Returns a Seurat object where JS(object = object[['pca']], slot = 'empirical') represents p-values for each gene in the PCA analysis. If ProjectPCA is subsequently run, JS(object = object[['pca']], slot = 'full') then represents p-values for all genes.

Inspired by Chung et al, Bioinformatics (2014)

if (FALSE) { data("pbmc_small") pbmc_small = suppressWarnings(JackStraw(pbmc_small)) head(JS(object = pbmc_small[['pca']], slot = 'empirical')) }