Statistical analysis for sparse high-throughput sequencing

Vignettes

- Package overview
- README.md
- fitTimeSeries: differential abundance analysis through time or location
- metagenomeSeq: statistical analysis for sparse high-throughput sequencing

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**aggregateBySample:**Aggregates a MRexperiment object or counts matrix to by a...**aggregateByTaxonomy:**Aggregates a MRexperiment object or counts matrix to a...**biom2MRexperiment:**Biom to MRexperiment objects**calcNormFactors:**Cumulative sum scaling (css) normalization factors**calcPosComponent:**Positive component**calcShrinkParameters:**Calculate shrinkage parameters**calcStandardError:**Calculate the zero-inflated log-normal statistic's standard...**calculateEffectiveSamples:**Estimated effective samples per feature**calcZeroAdjustment:**Calculate the zero-inflated component's adjustment factor**calcZeroComponent:**Zero component**correctIndices:**Calculate the correct indices for the output of...**correlationTest:**Correlation of each row of a matrix or MRexperiment object**cumNorm:**Cumulative sum scaling normalization**cumNormMat:**Cumulative sum scaling factors.**cumNormStat:**Cumulative sum scaling percentile selection**cumNormStatFast:**Cumulative sum scaling percentile selection**doCountMStep:**Compute the Maximization step calculation for features still...**doEStep:**Compute the Expectation step.**doZeroMStep:**Compute the zero Maximization step.**exportMat:**Export the normalized MRexperiment dataset as a matrix.**exportStats:**Various statistics of the count data.**expSummary:**Access MRexperiment object experiment data**extractMR:**Extract the essentials of an MRexperiment.**filterData:**Filter datasets according to no. features present in features...**fitDO:**Wrapper to calculate Discovery Odds Ratios on feature values.**fitFeatureModel:**Computes differential abundance analysis using a...**fitFeatureModelResults-class:**Class "fitFeatureModelResults" - a formal class for storing...**fitLogNormal:**Computes a log-normal linear model and permutation based...**fitMultipleTimeSeries:**Discover differentially abundant time intervals for all...**fitPA:**Wrapper to run fisher's test on presence/absence of a...**fitSSTimeSeries:**Discover differentially abundant time intervals using...**fitTimeSeries:**Discover differentially abundant time intervals**fitZeroLogNormal:**Compute the log fold-change estimates for the zero-inflated...**fitZig:**Computes the weighted fold-change estimates and t-statistics.**fitZigResults-class:**Class "fitZigResults" - a formal class for storing results...**getCountDensity:**Compute the value of the count density function from the...**getEpsilon:**Calculate the relative difference between iterations of the...**getNegativeLogLikelihoods:**Calculate the negative log-likelihoods for the various...**getPi:**Calculate the mixture proportions from the zero model / spike...**getZ:**Calculate the current Z estimate responsibilities (posterior...**isItStillActive:**Function to determine if a feature is still active.**libSize:**Access sample depth of coverage from MRexperiment object**libSize-set:**Replace the library sizes in a MRexperiment object**loadBiom:**Load objects organized in the Biom format.**loadMeta:**Load a count dataset associated with a study.**loadMetaQ:**Load a count dataset associated with a study set up in a...**loadPhenoData:**Load a clinical/phenotypic dataset associated with a study.**lungData:**OTU abundance matrix of samples from a smoker/non-smoker...**makeLabels:**Function to make labels simpler**mergeMRexperiments:**Merge two MRexperiment objects together**Browse all...**

Description Usage Arguments See Also

View source: R/fitZeroLogNormal.R

Fit the positive (log-normal) component

1 | ```
calcPosComponent(mat, mod, weights)
``` |

`mat` |
A matrix of normalized counts |

`mod` |
A model matrix |

`weights` |
Weight matrix for samples and counts |

`fitZeroLogNormal`

`fitFeatureModel`

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