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metagenomeSeq

Statistical analysis for sparse high-throughput sequencing

metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and undersampling of microbial communities on disease association detection and the testing of feature correlations.

To install the latest release version of metagenomeSeq:

source("http://bioconductor.org/biocLite.R")
biocLite("metagenomeSeq")

To install the latest development version of metagenomeSeq:

install.packages("devtools")
library("devtools")
install_github("Bioconductor-mirror/metagenomeSeq")

Author: Joseph Nathaniel Paulson, Hisham Talukder, Mihai Pop, Hector Corrada Bravo

Maintainer: Joseph N. Paulson : jpaulson at jimmy.harvard.edu

Website: www.cbcb.umd.edu/software/metagenomeSeq

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Version

Version

1.14.0

License

Artistic-2.0

Issues

Pull Requests

Stars

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Repository

https://github.com/nosson/metagenomeSeq/

Maintainer

Joseph N Paulson

Last Published

February 15th, 2017

Functions in metagenomeSeq (1.14.0)

calcPosComponent

Positive component

Cumulative sum scaling percentile selection

Filter datasets according to no. features present in features with at least a certain depth.

cumNormStatFast

Cumulative sum scaling percentile selection

Basic plot function of the raw or normalized data.

Wrapper to calculate Discovery Odds Ratios on feature values.

biom2MRexperiment

Biom to MRexperiment objects

calcNormFactors

Cumulative sum scaling (css) normalization factors

Cumulative sum scaling factors.

Cumulative sum scaling normalization

Wrapper to run fisher's test on presence/absence of a feature.

fitSSTimeSeries

Discover differentially abundant time intervals using SS-Anova

Export the normalized MRexperiment dataset as a matrix.

aggregateBySample

Aggregates a MRexperiment object or counts matrix to by a factor.

aggregateByTaxonomy

Aggregates a MRexperiment object or counts matrix to a particular level.

Various statistics of the count data.

Compute the zero Maximization step.

Access MRexperiment object experiment data

calcShrinkParameters

Calculate shrinkage parameters

Discover differentially abundant time intervals

Computes the weighted fold-change estimates and t-statistics.

fitZeroLogNormal

Compute the log fold-change estimates for the zero-inflated log-normal model

getCountDensity

Compute the value of the count density function from the count model residuals.

Calculate the relative difference between iterations of the negative log-likelihoods.

metagenomeSeq-deprecated

Depcrecated functions in the metagenomeSeq package.

getNegativeLogLikelihoods

Calculate the negative log-likelihoods for the various features given the residuals.

OTU abundance matrix of mice samples from a diet longitudinal study

Table of top-ranked features from fitZig or fitFeatureModel

Table of top microbial marker gene from linear model fit including sequence information

newMRexperiment

Create a MRexperiment object

calcZeroAdjustment

Calculate the zero-inflated component's adjustment factor

Compute the Maximization step calculation for features still active.

calcZeroComponent

Load objects organized in the Biom format.

Load a count dataset associated with a study.

Compute the Expectation step.

calcStandardError

Calculate the zero-inflated log-normal statistic's standard error

calculateEffectiveSamples

Estimated effective samples per feature

correlationTest

Correlation of each row of a matrix or MRexperiment object

Calculate the correct indices for the output of correlationTest

Basic plot function of the raw or normalized data.

smoothing-splines anova fits for each permutation

Trapezoidal Integration

Calculate the mixture proportions from the zero model / spike mass model residuals.

Calculate the current Z estimate responsibilities (posterior probabilities)

OTU abundance matrix of samples from a smoker/non-smoker study

MRexperiment2biom

MRexperiment to biom objects

Function to make labels simpler

ssIntervalCandidate

calculate interesting time intervals

Table of top microbial marker gene from linear model fit including sequence information

class permutations for smoothing-spline time series analysis

Accessor for the counts slot of a MRexperiment object

Class "MRexperiment" -- a modified eSet object for the data from high-throughput sequencing experiments

Access the normalization factors in a MRexperiment object

Basic plot of binned vectors.

Basic plot function of the raw or normalized data.

Plot of rarefaction effect

Plot of either PCA or MDS coordinates for the distances of normalized or unnormalized counts.

Basic heatmap plot function for normalized counts.

metagenomeSeq-package

Statistical analysis for sparse high-throughput sequencing

Basic correlation plot function for normalized or unnormalized counts.

plotClassTimeSeries

Plot abundances by class

returnAppropriateObj

Check if MRexperiment or matrix and return matrix

smoothing-splines anova fit

fitFeatureModel

Computes differential abundance analysis using a zero-inflated log-normal model

isItStillActive

Function to determine if a feature is still active.

Computes a log-normal linear model and permutation based p-values.

Access sample depth of coverage from MRexperiment object

Load a count dataset associated with a study set up in a Qiime format.

Load a clinical/phenotypic dataset associated with a study.

Plot difference function for particular bacteria

Access the posterior probabilities that results from analysis

Settings for the fitZig function

Table of features unique to a group