Package 'metagenomeSeq' - Bioconductor

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12 doZeroMStepDetailsValueMaximum-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 indicatorvariables. The density is defined as $f_zig(y_ij = pi_j(S_j) cdot f_0(y_ij) +(1-pi_j (S_j))cdotf_count(y_ij;mu_i,sigma_i^2)$. The log-likelihood in this extended model is $(1-delta_ij) logf_count(y;mu_i,sigma_i^2 )+delta_ij log pi_j(s_j)+(1-delta_ij)log (1-pi_j (sj))$. The responsibilitiesare defined as $z_ij = pr(delta_ij=1 | data)$.Updated matrix (m x n) of estimate responsibilities (probabilities that a count comes from a spikedistribution at 0).See AlsofitZigdoZeroMStepCompute the zero Maximization step.DescriptionUsagePerforms Maximization step calculation for the mixture components. Uses least squares to fit theparameters of the mean of the logistic distribution. $$ pi_j = sum_i^M frac1Mz_ij $$ Maximumlikelihoodestimates are approximated using the EM algorithm where we treat mixture membership$delta_ij$ = 1 if $y_ij$ is generated from the zero point mass as latent indicator variables. The densityis defined as $f_zig(y_ij = pi_j(S_j) cdot f_0(y_ij) +(1-pi_j (S_j))cdot f_count(y_ij;mu_i,sigma_i^2)$.The log-likelihood in this extended model is $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ijlog pi_j(s_j)+(1-delta_ij)log (1-pi_j (sj))$. The responsibilities are defined as $z_ij = pr(delta_ij=1| data)$.doZeroMStep(z, zeroIndices, mmZero)ArgumentszzeroIndicesmmZeroMatrix (m x n) of estimate responsibilities (probabilities that a count comes froma spike distribution at 0).Index (matrix m x n) of counts that are zero/non-zero.The zero model, the model matrix to account for the change in the number ofOTUs observed as a linear effect of the depth of coverage.ValueList of the zero fit (zero mean model) coefficients, variance - scale parameter (scalar), and normalizedresiduals of length sum(zeroIndices).
exportMat 13See AlsofitZigexportMatExport the normalized MRexperiment dataset as a matrix.DescriptionThis function allows the user to take a dataset of counts and output the dataset to the user’s workspaceas a tab-delimited file, etc.UsageexportMat(obj, log = TRUE, norm = TRUE, sep = "\t",file = "~/Desktop/matrix.tsv")ArgumentsobjlognormsepfileA MRexperiment object or count matrix.Whether or not to log transform the counts - if MRexperiment object.Whether or not to normalize the counts - if MRexperiment object.Separator for writing out the count matrix.Output file name.ValueNASee AlsocumNormExamples# see vignette
Page 1: Package ‘metagenomeSeq’May 22,
Page 5 and 6: calculateEffectiveSamples 5ValueA M
Page 7 and 8: cumNorm 7ArgumentsValueobjmethodalt
Page 9 and 10: cumNormStat 9cumNormStatCumulative
Page 11: doEStep 11ArgumentszymmCountstillAc
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-

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