View source: R/dinamic.R View source: R/quickLook.R

quickLook | R Documentation |

Find DiNAMIC's null distribution

```
quickLook(
x,
marker.data,
annot.file,
num.perms,
num.iters,
gain.loss = "gain",
reformat.annot = FALSE,
random.seed = NULL
)
```

`x` |
An n by m numeric matrix containing DNA copy number data from n subjects at m markers. |

`marker.data` |
A dataframe containing marker position data for markers in the autosomes. Column 1 contains the chromosome number for each marker, and column 2 contains the position (in base pairs) for each markers. Additional columns, if present, represent information about the markers (e.g. probe names). |

`annot.file` |
A cytoband annotation dataframe. Each row corresponds to a distinct cytoband, and column 1 contains the chromosome number, column 2 contains the start position (in base pairs), column 3 contains the end position (in base pairs), and column 4 contains the cytoband name (e.g. p21.3). Additional columns may be present, but they are not used. |

`num.perms` |
A positive integer that represents the number of cyclic shifts used to create the empirical distribution. |

`num.iters` |
A positive integer that represents the number of distinct gain (loss) loci that will be assessed. See "Details" for more information. |

`gain.loss` |
A character string that indicates whether recurrent gains ( |

`reformat.annot` |
A logical value that indicates whether annot.file needs to be reformatted (default = FALSE).
See the "Note" section of |

`random.seed` |
An optional random seed (default = NULL). |

This function applies the "Quick Look" version of DiNAMIC's cyclic shift procedure to assess the statistical
significance of recurrent DNA copy number aberrations. Either recurrent gains (`gain.loss = "gain"`

) or
recurrent losses (`gain.loss = "loss"`

) are assessed using a null distribution based on num.perms cyclic shifts
of `x`

. Iterative calls to DiNAMIC's peeling procedure (implemented here in the `peeling`

function)
allow users to assess the statistical significance of num.iters distinct gains (losses). As noted in Bioinformatics
(2011) 27(5) 678 - 685, the "Quick Look" procedure calculates the null distribution once, and the same distribution
is used to assess the statistical significance of the most aberrant gain or loss after each iteration of the peeling
procedure. This approach is less computationally intensive than "Detailed Look" because the null distribution is
only computed once, but simulations suggest that it provides less power to detect recurrent gains (losses). The
resulting p-values are corrected for multiple comparisons because the null distribution is based on computing
`max(colSums(x))`

or `min(colSums(x))`

.

A matrix with `num.iters`

rows. The entries of each row correspond to the marker that is
being assessed. More specifically, the entries are (1) the chromosome number, (2) the marker position
(in base pairs), (3) additional marker information present in marker.data, (4) the marker number, and
(5) the p-value obtained from the null distribution, (6) the endpoints of the peak interval (in base pairs),
as described in Bioinformatics (2011) 27(5) 678 - 685.

```
quickLook(wilms.data, wilms.markers, annot.file, 100, 3)
```

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