Description Usage Arguments Value Author(s) Examples

View source: R/FolderAnalysis_functions.R

Performs a hybrid bootstrapping subsampling procedure similar to random forest. It bootstraps the reference samples and subsamples the amplicons associated with each gene. Returns a distribution of sample/reference ratios for each gene and sample of interest combination.

1 | ```
BootList(geneNames, sampleMatrix, refmat, replicates)
``` |

`geneNames` |
A vector of gene names, with one entry for each sequenced amplicon. |

`sampleMatrix` |
A vector or matrix of the read counts from the sample of interest. In the case of a matrix columns represent samples and rows amplicons. |

`refmat` |
A matrix of the read counts obtianed from the reference samples. Columns represent reference samples and rows amplicons. |

`replicates` |
How many bootstrap replicates should be performed. |

Returns a list of numeric matrices: For each matrix a row represent a gene while each column represents a bootstrapping/subsampling iteration.

Thomas Wolf, Cristiano Oliveira

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
data(sampleReadCounts)
data(referenceReadCounts)
## Gene names should be same size as row columns
geneNames <- row.names(referenceReadCounts)
ampliconNames <- NULL
normalizedReadCounts <- CombinedNormalizedCounts(sampleReadCounts,
referenceReadCounts,
ampliconNames = ampliconNames)
# After normalization data sets need to be splitted again to perform bootstrap
samplesNormalizedReadCounts = normalizedReadCounts["samples"][[1]]
referenceNormalizedReadCounts = normalizedReadCounts["reference"][[1]]
# Should be used values above 10000
replicates <- 10
# Perform the bootstrap based analysis
bootList <- BootList(geneNames,
samplesNormalizedReadCounts,
referenceNormalizedReadCounts,
replicates = replicates)
``` |

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