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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("metagenomeSeq","nosson")

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

Maintainer: Joseph N. Paulson : jpaulson at umiacs.umd.edu

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

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Version

Version

1.10.0

License

Artistic-2.0

Issues

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Repository

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

Maintainer

Joseph N Paulson

Last Published

February 15th, 2017

Functions in metagenomeSeq (1.10.0)

Cumulative sum scaling percentile selection

biom2MRexperiment

Biome to MRexperiment objects

aggregateBySample

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

calculateEffectiveSamples

Estimated effective samples per feature

correlationTest

Correlation of each row of a matrix or MRexperiment object

aggregateByTaxonomy

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

Cumulative sum scaling factors.

Cumulative sum scaling normalization

calcNormFactors

Cumulative sum scaling normalization factors Return a vector of the the sum up to and including a quantile.

Calculate the correct indices for the output of correlationTest

cumNormStatFast

Cumulative sum scaling percentile selection

Access MRexperiment object experiment data

Wrapper to calculate Discovery Odds Ratios on feature values.

Various statistics of the count data.

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

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

Compute the Expectation step.

Compute the zero Maximization step.

Compute the Maximization step calculation for features still active.

Export the normalized MRexperiment dataset as a matrix.

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

getCountDensity

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

getNegativeLogLikelihoods

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

Calculate the current Z estimate responsibilities (posterior probabilities)

Discover differentially abundant time intervals

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

isItStillActive

Function to determine if a feature is still active.

fitSSTimeSeries

Discover differentially abundant time intervals using SS-Anova

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

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

OTU abundance matrix of mice samples from a diet longitudinal study

metagenomeSeq-package

Statistical analysis for sparse high-throughput sequencing

Table of features unique to a group

Load a count dataset associated with a study.

Trapezoidal Integration Compute the area of a function with values 'y' at the points 'x'. Function comes from the pracma package.

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

Basic plot of binned vectors.

plotClassTimeSeries

Plot abundances by class

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

Basic plot function of the raw or normalized data.

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

Load a clinical/phenotypic dataset associated with a study.

newMRexperiment

Create a MRexperiment object

Access the normalization factors in a MRexperiment object

Basic plot function of the raw or normalized data.

Basic heatmap plot function for normalized counts.

class permutations for smoothing-spline time series analysis Creates a list of permuted class memberships for the time series permuation tests.

smoothing-splines anova fits for each permutation

Table of top-ranked microbial marker gene from linear model fit

metagenomeSeq-deprecated

Depcrecated functions in the metagenomeSeq package.

Function to make labels simpler

Accessor for the counts slot of a MRexperiment object

Basic correlation plot function for normalized or unnormalized counts.

Basic plot function of the raw or normalized data.

smoothing-splines anova fit

ssIntervalCandidate

calculate interesting time intervals Calculates time intervals of interest using SS-Anova fitted confidence intervals.

Access sample depth of coverage from MRexperiment object

Load objects organized in the Biome format.

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

Plot of rarefaction effect

MRexperiment2biom

MRexperiment to biom objects

Plot difference function for particular bacteria

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

returnAppropriateObj

Check if MRexperiment or matrix and return matrix

Settings for the fitZig function

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

Access the posterior probabilities that results from analysis