summary.subsamples {subSeq} | R Documentation

## calculate summary statistics for each subsampled depth in a subsamples object

### Description

Given a subsamples object, calculate a metric for each depth that summarizes the power, the specificity, and the accuracy of the effect size estimates at that depth.

### Usage

    ## S3 method for class 'subsamples'
    summary(object, oracle = NULL, FDR.level = 0.05,
      average = FALSE, p.adjust.method = "qvalue", ...)

### Arguments

`object` | a subsamples object
`oracle` | a subsamples object of one depth showing what each depth should be compared to; if NULL, each will be compared to the highest depth
`FDR.level` | A false discovery rate used to calculate the number of genes found significant at each level
`average` | If TRUE, averages over replications at each method+depth combination before returning
`p.adjust.method` | Method to correct p-values in order to determine significance. By default "qvalue", but can also be given any method that can be given to p.adjust.
`...` | further arguments passed to or from other methods.

### Details

To perform these calculations, one must compare each depth to an "oracle" depth, which, if not given explicitly, is assumed to be the highest subsampling depth. This thus summarizes how closely each agrees with the full experiment: if very low-depth subsamples still agree, it means that the depth is high enough that the depth does not make a strong qualitative difference.

The concordance correlation coefficient is described in Lin 1989. Its advantage over the Pearson is that it takes into account not only whether the coefficients compared to the oracle close to a straight line, but whether that line is close to the x = y line.

Note that selecting average=TRUE averages the depths of the replicates (as two subsamplings with identical proportions may have different depths by chance). This may lead to depths that are not integers.

### Value

A summary object, which is a `data.table` with one row for each subsampling depth, containing the metrics

`significant` | number of genes found significant at the given FDR
`pearson` | Pearson correlation of the coefficient estimates with the oracle
`spearman` | Spearman correlation of the coefficient estimates with the oracle
`concordance` | Concordance correlation of the coefficient estimates with the oracle
`MSE` | mean squared error between the coefficient estimates and the oracle
`estFDP` | estimated FDP: the estimated false discovery proportion, as calculated from the average oracle local FDR within genes found significant at this depth
`rFDP` | relative FDP: the proportion of genes found significant at this depth that were not found significant in the oracle
`percent` | the percentage of genes found significant in the oracle that were found significant at this depth

### References

Lawrence I-Kuei Lin (March 1989). "A concordance correlation coefficient to evaluate reproducibility". Biometrics (International Biometric Society) 45 (1): 255-268.

### Examples

    # see subsample function to see how ss is generated
    data(ss)
    # summarise subsample object
    ss.summary = summary(ss)

[Package subSeq version 1.10.0 Index]