Description Usage Arguments Value Author(s) References Examples

ccrepe calculates compositionality-corrected p-values and q-values for compositional data by generating
first a null distribution of the distance metric generated by permutation and renormalization of the data,
and then by generating an alternative distribution of the distance metric by bootstrap resampling of the data.
For greater detail, see References

The two distributions are compared using a pooled-variance Z-test to give a compositionality-corrected p-value.
The p-values can be calculated for all appropriate (passing certain quality-control measures) pairwise comparisons,
or for a subset of user-specified ones.

Q-values are additionally calculated using the Benjamin-Hochberg-Yekutieli procedure (see References)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 |

`x` |
First dataframe or matrix containing the relative abundances in cavity1 : columns are bugs, rows are samples.
(Rows should therefore sum to a constant.) |

`y` |
Second dataframe or matrix (optional) containing the relative abundances in cavity2: columns are bugs, rows are samples. |

`sim.score` |
A function defining a similarity measure, such as cor or nc.score. This similarity measure can be a pre-defined R function or user-defined. If the latter,
certain properties should be satisfied as detailed below (also see examples). The default similarity measure is Spearman correlation. |

`sim.score.args` |
A list of arguments for the measurement function.
For example: In the case of cor, the following would be acceptable:
sim.score.args = list(method='spearman',use='complete.obs' ). |

`min.subj` |
Minimum number of samples that must be non-missing in a bug/feature/column in order to apply the similarity measure to that bug/feature/column. This is to ensure that there are sufficient subjects to perform a bootstrap (default: 20). |

`iterations` |
The number of iterations of bootstrap and permutation (default: 1000). |

`subset.cols.x` |
A vector of column indices from x to indicate which features to compare |

`subset.cols.y` |
A vector of column indices from y to indicate which features to compare |

`errthresh` |
If feature has number of zeros greater than errthresh^(1/n) , that feature is excluded |

`verbose` |
Logical: an indicator whether the user requested verbose output, which prints periodic progress of the algorithm through the dataset(s), as well as including more detailed output. (default:FALSE) |

`iterations.gap` |
If output is verbose - number of iterations after issue a status message (Default=100 - displayed only if verbose=TRUE). |

`distributions` |
Output Distribution file (default:NA). |

`compare.within.x` |
A boolean value indicating whether to do comparisons given by taking all subsets of size 2 from subset.cols.x or to do comparisons given by taking all possible combinations of subset.cols.x or subset.cols.y. If TRUE but subset.cols.y=NA, returns all comparisons involving any features in subset.cols.x. This argument is only used when y=NA. |

`concurrent.output` |
Optional output file to which each comparison will be written as it is calculated. |

`make.output.table` |
A boolean value indicating whether to include table-formatted output. |

Returns a list containing the calculation results and the parameters used.

Default parameters shown:

`min.subj` |
Description above |

`errThresh` |
Description same as errthresh above |

`sim.score` |
A matrix of the simliarity scores for all the requested comparisons. The (i,j)th element of sim.score correponds to the similarity score of column i (or the ith column of subset.cols.1) and column j (or the jth column of subset.cols.1) in one dataset, or to the similarity score of column i (or the ith column of subset.cols.1) in dataset x and column j (or the jth column of subset.cols.2)in dataset y in the case of two datasets. |

`p.values` |
A matrix of the p-values for all the requested comparisons. The (i,j)th element of p.values corresponds to the p-value of the (i,j)th element of sim.score. |

`q.values` |
A matrix of the Benjamini-Hochberg-Yekutieli FDR corrected p-values. The (i,j)th element of q.values corresponds to the q-value fo the (i,j)th element of sim.score. |

`z.stat` |
A matrix of the z-statistics for all the requested comparisons. The (i,j)th element corresponds to the z-statistic which gave rise to the (i,j)th p-value. |

`output.table` |
(Only if make.output.table=TRUE) A table where each row is one comparision. Each row contains the features being compared with their similarity scores, z-statistics, p-values and q-values |

Additional parameters if verbose=TRUE:

`iterations` |
Description Above |

`subset.cols.x` |
Description Above |

`subset.cols.y` |
Description Above |

`iterations.gap` |
Description Above |

`sim.score.parameters` |
Description Above |

`compare.within.x` |
Description Above |

`make.output.table` |
Description Above |

Emma Schwager <[email protected]>

Emma Schwager and Colleagues. Detecting statistically significant associtations between sparse and high dimensional compositioanl data. In Progress.

Benjamini and Yekutieli (2001). "The control of the false discovery rate in multiple testing under dependency." The Annals of Statistics. Vol. 19, No. 4. pp. 1165-1188.

1 2 3 4 5 6 7 8 9 | ```
data <- matrix(rlnorm(40,meanlog=0,sdlog=1),nrow=10)
data.rowsum <- apply(data,1,sum)
data.norm <- data/data.rowsum
testdata <- data.norm
dimnames(testdata) <- list(paste("Sample",seq(1,10)),paste("Feature",seq(1,4)))
ccrepe.results <-ccrepe (x=testdata, iterations=20, min.subj=10)
ccrepe.results.nc.score <- ccrepe(x=testdata,iterations=20,min.subj=10,sim.score=nc.score)
ccrepe.results
ccrepe.results.nc.score
``` |

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