Description Usage Arguments Details Value Author(s) References

These functions are provided for compatibility with older versions of ‘HTSFilter’ only, and will be defunct at the next release.

1 2 3 4 5 6 7 8 9 | ```
## S4 method for signature 'CountDataSet'
HTSFilter(x, conds = NA, s.min = 1, s.max = 200,
s.len = 100, loess.span = 0.3, normalization = c("DESeq", "TMM",
"none"), plot = TRUE, plot.name = NA, parallel = FALSE,
BPPARAM = bpparam())
## S4 method for signature 'CountDataSet'
HTSBasicFilter(x, method, cutoff.type = "value",
cutoff = 10, length = NA, normalization = c("DESeq", "TMM", "none"))
``` |

`x` |
A numeric matrix or data.frame representing the counts of dimension ( |

`conds` |
Vector of length |

`s.min` |
Minimum value of filtering threshold to be considered, with default value equal to 1 |

`s.max` |
Maximum value of filtering threshold to be considered, with default value equal to 200 |

`s.len` |
Length of sequence of filtering thresholds to be considered (from |

`loess.span` |
Span of the loess curve to be fitted to the filtering thresholds and corresponding global similarity indices, with default value equal to 0.3 |

`normalization` |
Normalization method to be used to correct for differences in library sizes, with
choices “TMM” (Trimmed Mean of M-values), “DESeq” (normalization method proposed in the
DESeq package), “pseudo.counts” (pseudo-counts obtained via quantile-quantile normalization in
the edgeR package, only available for objects of class |

`plot` |
If “TRUE”, produce a plot of the calculated global similarity indices against the filtering threshold with superimposed loess curve |

`plot.name` |
If |

`parallel` |
If |

`BPPARAM` |
Optional parameter object passed internally to |

`method` |
Basic filtering method to be used: “mean”, “sum”, “rpkm”, “variance”, “cpm”, “max”, “cpm.mean”, “cpm.sum”, “cpm.variance”, “cpm.max”, “rpkm.mean”, “rpkm.sum”, “rpkm.variance”, or “rpkm.max” |

`cutoff.type` |
Type of cutoff to be used: a numeric value indicating the number of samples to be
used for filtering (when |

`cutoff` |
Cutoff to be used for chosen filter |

`length` |
Optional vector of length |

The following functions are deprecated and will be made defunct. Objects of type ‘CountDataSet’ from the original ‘DESeq’ package will no longer be supported; users should make use of ‘DESeqDataSet’ objects from the ‘DESeq2’ package:

HTSFilter.CountDataSet:

`HTSFilter`

HTSBasicFilter.CountDataSet:

`HTSBasicFilter`

filteredData An object of the same class as

`x`

containing the data that passed the filteron A binary vector of length

*g*, where 1 indicates a gene with normalized expression greater than the optimal filtering threshold`s.optimal`

in at least one sample (irrespective of condition labels), and 0 indicates a gene with normalized expression less than or equal to the optimal filtering threshold in all sampless The optimal filtering threshold as identified by the global similarity index

indexValues A matrix of dimension (

`s.len`

x 2) giving the tested filtering thersholds and the corresponding global similarity indices. Note that the threshold values are equally spaced on the*log*scale, and thus unequally spaced on the count scale (i.e., we test more threshold values at very low levels of expression, and fewer at very high levels of expression).normFactor A vector of length

*n*giving the estimated library sizes estimated by the normalization method specified in`normalization`

removedData A matrix containing the filtered data

Andrea Rau, Melina Gallopin, Gilles Celeux, and Florence Jaffrezic

R. Bourgon, R. Gentleman, and W. Huber. (2010) Independent filtering increases detection power for high-
throughput experiments. *PNAS* **107**(21):9546-9551.

P. Jaccard (1901). Etude comparative de la distribution
orale dans une portion des Alpes et des Jura.
*Bulletin de la Societe Vaudoise des Sciences Naturelles*, **37**:547-549.

A. Rau, M. Gallopin, G. Celeux, F. Jaffrezic (2013). Data-based filtering
for replicated high-throughput transcriptome sequencing experiments. *Bioinformatics*,
doi: 10.1093/bioinformatics/btt350.

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