Package 'metagenomeSeq' - Bioconductor

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10 doCountMStepcumNormStatFastCumulative sum scaling percentile selectionDescriptionCalculates the percentile for which to sum counts up to and scale by. Faster version than availablein cumNormStat.UsagecumNormStatFast(obj, pFlag = FALSE, rel = 0.1, ...)ArgumentsValueobjpFlagrelA MRexperiment object.Plot the median difference quantiles.Cutoff for the relative difference from one median difference from the referenceto the next.... Applicable if pFlag == TRUE. Additional plotting parameters.Percentile for which to scale dataSee AlsofitZig cumNorm cumNormStatExamplesdata(mouseData)p = round(cumNormStatFast(mouseData,pFlag=FALSE),digits=2)doCountMStepCompute the Maximization step calculation for features still active.DescriptionMaximization step is solved by weighted least squares. The function also computes counts residuals.UsagedoCountMStep(z, y, mmCount, stillActive, fit2 = NULL)
doEStep 11ArgumentszymmCountstillActivefit2Matrix (m x n) of estimate responsibilities (probabilities that a count comes froma spike distribution at 0).Matrix (m x n) of count observations.Model matrix for the count distribution.Boolean vector of size M, indicating whether a feature converged or not.Previous fit of the count model.DetailsMaximum-likelihood estimates are approximated using the EM algorithm where we treat mixturemembership $delta_ij$ = 1 if $y_ij$ is generated from the zero point mass as latent indicator variables.The density is defined as $f_zig(y_ij = pi_j(S_j)*f_0(y_ij) +(1-pi_j (S_j)) * f_count(y_ij;mu_i,sigma_i^2)$.The log-likelihood in this extended model is $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ijlog pi_j(s_j)+(1-delta_ij)log (1-pi_j (s_j))$. The responsibilities are defined as $z_ij = pr(delta_ij=1| data)$.ValueUpdate matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spikedistribution at 0).See AlsofitZigdoEStepCompute the Expectation step.DescriptionEstimates the responsibilities $z_ij = fracpi_j cdot I_0(y_ijpi_j cdot I_0(y_ij + (1-pi_j) cdot f_count(y_ijUsagedoEStep(countResiduals, zeroResiduals, zeroIndices)ArgumentscountResiduals Residuals from the count model.zeroResiduals Residuals from the zero model.zeroIndices Index (matrix m x n) of counts that are zero/non-zero.
Page 1: Package ‘metagenomeSeq’May 22,
Page 5 and 6: calculateEffectiveSamples 5ValueA M
Page 7 and 8: cumNorm 7ArgumentsValueobjmethodalt
Page 9: cumNormStat 9cumNormStatCumulative
Page 13 and 14: exportMat 13See AlsofitZigexportMat
Page 15 and 16: filterData 15Author(s)Joseph N. Pau
Page 17 and 18: fitMeta 17fitMetaComputes a slightl
Page 19 and 20: getCountDensity 19See AlsocumNorm z
Page 21 and 22: getPi 21ArgumentszMatrix (m x n) of
Page 23 and 24: libSize 23ArgumentsepstolstillActiv
Page 25 and 26: load_metaQ 25ValueA list with objec
Page 27 and 28: makeLabels 27Referenceshttp://www.n
Page 29 and 30: MRcounts 29Examplesdata(lungData)k
Page 31 and 32: MRexperiment2biom 31As mentioned ab
Page 33 and 34: MRtable 33See AlsofitZig MRcoefs MR
Page 35 and 36: normFactors 35ArgumentscountsphenoD
Page 37 and 38: plotFeature 37plotFeatureBasic plot
Page 39 and 40: plotMRheatmap 39Examplesdata(mouseD
Page 41 and 42: plotOTU 41plotOTUBasic plot functio
Page 43 and 44: posterior.probs 43posterior.probsAc
Page 45 and 46: Index∗Topic packagemetagenomeSeq-

Package 'metagenomeSeq' - Bioconductor

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