Description Usage Arguments Value Examples

Apply DESCEND to deconvolve the true expression level distribution for every geneand calculate relavant distribution measurements. Parallel computing is allowed. For deconvolution of two or more cell populations, see `descendMultiPop`

. For model details, see `deconvG`

.

1 2 3 4 5 |

`count.matrix` |
the observed UMI count matrix. It should be an R object of class |

`ercc.matrix` |
the ERCC spike-ins are used for computing the cell-specific efficiency constants as |

`scaling.consts` |
a vector of cell specific scaling constants, either the cell efficiency or the library size |

`Z` |
covariates for nonzero mean. Default is NULL. |

`Z0` |
covariates for nonzero fraction. Used only when zeroInflate is True. Default is NULL. |

`n.cores` |
the number of cores used for parallel computing. Default is 1. Used only when parallel computing is done in a single machine. For using multi-machine cores, need to assign |

`cl` |
an object of class "cluster". See more details in |

`type` |
Default is "FORK" to save memory. Change it to "PSOCK" if you are using Windows and cl is NULL. More details see |

`do.LRT.test` |
whether do LRT test on the coefficients and nonzero fraction or not. Default is True |

`family` |
family of the noise distribution, support either "Poisson" or "Negative Binomial" with known tuning parameter |

`NB.size` |
over-dispersion parameter when the family is Negative Binomial: mu = mu + mu^2/size |

`show.message` |
whether show messages for the computing progresses. Default is TRUE |

`verbose` |
verbose the estimation and testing procedures or not. Default is True. |

`ercc.trueMol` |
the true input number of molecules of the ercc spike-ins when |

`control` |
settings see |

a list of DESCEND objects. The length of the list is the same as the number of genes.
See also `DESCEND`

. NA if the gene is too sparse or DESCEND fails to find a solution.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
## Not run:
## For a Windows machine add the argument: type = "PSOCK"
data(zeisel)
result <- runDescend(zeisel$count.matrix.small,
scaling.consts = zeisel$library.size, n.cores = 3)
hvg <- findHVG(result)
hvg$HVG.genes
result1 <- runDescend(zeisel$count.matrix.small,
zeisel$ercc.matrix, ercc.trueMol = zeisel$trueMol,
Z0 = log(zeisel$cell.size),
n.cores = 3)
ests <- getEstimates(result1)
ests$CV
## End(Not run)
``` |

jingshuw/descend documentation built on Sept. 2, 2018, 11:10 a.m.

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