Description Usage Arguments Value Author(s) Examples

View source: R/FastMix.r View source: R/FastMix.R

A new analytic pipeline, dubbed as FastMix, that combines the deconvolution step with the downstream analyses based on linear mixed eﬀects regression (LMER) model and a moment-matching algorithm.

1 | ```
ols.eblup.trim (Des, Y, random = "all", independent = TRUE, trim = 0.5, robust = FALSE, trim.fix = FALSE)
``` |

`Des` |
the design matrix ordered by gene subject by subject. First column should be identification variable, e.g., ID or subject, and the rest columns are covariates. |

`Y` |
vectorized gene expression data. |

`random` |
'random' is an index vector that specifies which variable(s) requires random effects – by default, all covariates are paired with a random effect. |

`independent` |
specify the correlation structure among random effects. If TRUE, random effects are assumed to be independent. |

`trim` |
the trimming percentage when accounting for outliers. Default valie is 0.5 (50%). |

`test` |
the test method for DEGs. "1" is Gaussian mixture model, "2" is Anderson-darling normal test. Default valie is "1". |

`robust` |
Specifies whether robust covariance estimation is implemented and which method to use: "FALSE" for non-robust estimation; "mcd" for the MCD algorithm of Rousseeuw and Van Driessen; "weighted" for the Reweighted MCD; "donostah" for the Donoho-Stahel projection based estimator; "pairwiseQC" for the orthogonalized quadrant correlation pairwise estimator. All these algorithms come from the R package 'robust'. "FastMix" is the proposed trimming method. |

`trim.fix` |
Whether only consider trimmed subjects in fix effect estiamtion. The default value is FALSE. |

`fixed.results ` |
the estimated fix effects and their p-values. They are overall effects shared by all genes. |

`beta.mat ` |
individual coefficient estimation. |

`Yhat` |
fitted response. |

`sigma.beta` |
the covariance estimation of fixed effect. |

`VC ` |
variance component estimation. The first column is the one for common random error. The second column is the one for random effects. |

`eta` |
the chi sqiare type statsitics used for p-value calculation. |

`re.pvalue` |
the overall p-value for detecting outliers in random effects. |

`re.ind.pvalue` |
the individual p-value for outlier detection for each random effect. |

`out_idx` |
he potential covariates with outliers when robust = "FastMix. It is NULL when robust != "FastMix" |

Hao Sun

1 2 3 4 5 6 7 8 9 | ```
## load the data example and transform the data
gnames <- rownames(GeneExp); m <- nrow(GeneExp)
if (is.null(gnames)) {
rownames(GeneExp) <- gnames <- paste0("Gene", 1:m)
}
Data2 <- DataPrep(GeneExp, CellProp, Demo)
## fit the model
mod <- ols.eblup.trim(Des=Data2$X, Y=Data2$Y, random="all", robust = "FastMix")
``` |

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