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1 \name{get.mser.interpolation}
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2 \alias{get.mser.interpolation}
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3 %- Also NEED an '\alias' for EACH other topic documented here.
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4 \title{ Interpolate MSER dependency on the tag count }
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5 \description{
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6 MSER generally decreases with increasing sequencing depth. This
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7 function interpolates the dependency of MSER on tag counts as a
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8 log-log linear function. The log-log fit is used to estimate the depth
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9 of sequencing required to reach desired \code{target.fold.enrichment}.
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10 }
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11 \usage{
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12 get.mser.interpolation(signal.data, control.data, target.fold.enrichment = 5, n.chains = 10, n.steps = 6, step.size = 1e+05, chains = NULL, test.agreement = 0.99, return.chains = F, enrichment.background.scales = c(1), excluded.steps = c(seq(2, n.steps - 2)), ...)
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13 }
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14 %- maybe also 'usage' for other objects documented here.
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15 \arguments{
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16 \item{signal.data}{ signal chromosome tag vector list }
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17 \item{control.data}{ control chromosome tag vector list }
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18 \item{target.fold.enrichment}{ target MSER for which the depth should
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19 be estimated}
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20 \item{n.steps}{ number of steps in each subset chain. }
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21 \item{step.size}{ Either number of tags or fraction of the dataset
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22 size, see \code{step.size} parameter for \code{\link{get.mser}}. }
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23 \item{test.agreement}{ Fraction of the detected peaks that should
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24 agree between the full and subsampled datasets. See \code{test.agreement} parameter for \code{\link{get.mser}}}
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25 \item{n.chains}{ number of random subset chains }
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26 \item{chains}{ optional structure of pre-calculated chains
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27 (e.g. generated by an earlier call with \code{return.chains=T}.}
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28
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29 \item{return.chains}{ whether to return peak predictions calculated on
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30 random chains. These can be passed back using \code{chains} argument
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31 to skip subsampling/prediction steps, and just recalculate the depth
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32 estimate for a different MSER.}
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33 \item{enrichment.background.scales}{ see \code{enrichment.background.scales} parameter for \code{\link{get.mser}} }
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34 \item{excluded.steps}{ Intermediate subsampling steps that should be excluded from
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35 the chains to speed up the calculation. By default, all intermediate
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36 steps except for first two and last two are skipped. Adding
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37 intermediate steps improves interpolation at the expense of
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38 computational time.}
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39 \item{\dots}{ additional parameters are passed to \code{\link{get.mser}} }
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40 }
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41 \details{
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42 To simulate sequencing growth, the method calculates peak predictions
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43 on random chains. Each chain is produced by sequential random
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44 subsampling of the original data. The number of steps in the chain
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45 indicates how many times the random subsampling will be performed.
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46 }
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47 \value{
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48 Normally reurns a list, specifying for each backgroundscale:
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49 \item{prediction}{estimated sequencing depth required to reach
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50 specified target MSER}
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51 \item{log10.fit}{linear fit model, a result of \code{lm()} call}
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52
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53 If \code{return.chains=T}, the above structure is returned under
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54 \code{interpolation} field, along with \code{chains} field containing
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55 results of \code{\link{find.binding.positions}} calls on subsampled chains.
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56 }
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