Package MetaLonDA. R topics documented: January 22, Type Package
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1 Type Package Package MetaLonDA January 22, 2018 Title Metagenomics Longitudinal Differential Abundance Method Version Date Author Ahmed Metwally, Yang Dai, Patricia Finn, David Perkins Maintainer Ahmed Metwally Identify time intervals of differentially abundant metagenomics features in longitudinal studies <doi: / >. License MIT + file LICENSE Depends R(>= 3.2.0) Imports ggplot2, gss, plyr, catools, parallel, doparallel, metagenomeseq, DESeq2, edger Suggests knitr, rmarkdown biocviews Repository CRAN NeedsCompilation no Encoding UTF-8 LazyData true RoxygenNote Date/Publication :30:02 UTC R topics documented: areapermutation curvefitting findsiginterval intervalarea metalonda metalondaall metalonda_test_data
2 2 areapermutation normalize permutation visualizearea visualizearhistogram visualizefeature visualizefeaturespline visualizetimeintervals Index 14 areapermutation Calculate Area Ratio (AR) of each feature s time interval for all permutations Fits longitudinal samples from the same group using negative binomial or LOWESS for all permutations areapermutation(perm) perm list has all the permutated models Value returns a list of all permutation area ratio data(metalonda_test_data) n.sample = 5 # sample size; n.timepoints = 10 # time point; n.perm = 3 n.group= 2 # number of group; Group = factor(c(rep(0,n.sample*n.timepoints), rep(1,n.sample*n.timepoints))) Time = rep(rep(1:n.timepoints, times = n.sample), 2) ID = factor(rep(1:(2*n.sample), each = n.timepoints)) points = seq(1, 10, length.out = 10) aggretage.df = data.frame(count = metalonda_test_data[1,], Time = Time, Group = Group, ID = ID) perm = permutation(aggretage.df, n.perm = 3, method = "nbinomial", points) areapermutation(perm)
3 curvefitting 3 curvefitting Fit longitudinal data Fits longitudinal samples from the same group using negative binomial smoothing splines or LOWESS curvefitting(df, method = "nbinomial", points) df method points dataframe has the Count, Group, ID, Time fitting method (negative binomial, LOWESS) points at which the prediction should happen Value returns the fitted model data(metalonda_test_data) n.sample = 5 n.timepoints = 10 n.group = 2 Group = factor(c(rep(0, n.sample*n.timepoints), rep(1, n.sample*n.timepoints))) Time = rep(rep(1:n.timepoints, times = n.sample), 2) ID = factor(rep(1:(2*n.sample), each = n.timepoints)) points = seq(1, 10, length.out = 10) aggretage.df = data.frame(count = metalonda_test_data[1,], Time = Time, Group = Group, ID = ID) cf = curvefitting(df = aggretage.df, method= "nbinomial", points)
4 4 intervalarea findsiginterval Find significant time intervals Identify significant time intervals findsiginterval(adjusted.pvalue, threshold = 0.05, sign) Value adjusted.pvalue vector of the adjusted p-value threshold sign p-value cut off vector hold area sign of each time interval returns a list of the start and end points of all significant time intervals p = c(0.04, 0.01, 0.02, 0.04, 0.06, 0.2, 0.06, 0.04) sign = c(1, 1, 1, 1, -1, -1, 1, 1) findsiginterval(p, threshold = 0.05, sign) intervalarea Calculate Area Ratio (AR) of each feature s time interval Calculate Area Ratio (AR) of each feature s time interval intervalarea(curve.fit.df) curve.fit.df gss data object of the fitted spline
5 metalonda 5 Value returns the area ratio for all time intervals data(metalonda_test_data) n.sample = 5 n.timepoints = 10 n.group= 2 Group = factor(c(rep(0,n.sample*n.timepoints), rep(1,n.sample*n.timepoints))) Time = rep(rep(1:n.timepoints, times = n.sample), 2) ID = factor(rep(1:(2*n.sample), each = n.timepoints)) points = seq(1, 10, length.out = 10) aggregate.df = data.frame(count = metalonda_test_data[1,], Time = Time, Group = Group, ID = ID) model = curvefitting(df = aggregate.df, method= "nbinomial", points) intervalarea(model) metalonda Metagenomic Longitudinal Differential Abundance Analysis for one feature Find significant time intervals of the one feature metalonda(count, Time, Group, ID, n.perm = 500, fit.method = "nbinomial", points, text = 0, parall = FALSE, pvalue.threshold = 0.05, adjust.method = "BH", time.unit = "days") Count Time Group ID n.perm fit.method points text matrix has the number of reads that mapped to each feature in each sample. vector of the time label of each sample. vector of the group label of each sample. vector of the subject ID label of each sample. number of permutations. fitting method (NB, LOWESS). points at which the prediction should happen. Feature s name.
6 6 metalondaall parall boolean to indicate whether to use multicore. pvalue.threshold p-value threshold cutoff for identifing significant time intervals. adjust.method time.unit multiple testing correction method. time unit used in the Time vector (hours, days, weeks, months, etc.) Value returns a list of the significant time intervals for the tested feature. data(metalonda_test_data) n.sample = 5 n.timepoints = 10 n.group = 2 Group = factor(c(rep(0, n.sample*n.timepoints), rep(1,n.sample*n.timepoints))) Time = rep(rep(1:n.timepoints, times = n.sample), 2) ID = factor(rep(1:(2*n.sample), each = n.timepoints)) points = seq(1, 10, length.out = 10) ## Not run: output.nbinomial = metalonda(count = metalonda_test_data[1,], Time = Time, Group = Group, ID = ID, fit.method = "nbinomial", n.perm = 10, points = points, text = rownames(metalonda_test_data)[1], parall = FALSE, pvalue.threshold = 0.05, adjust.method = "BH") ## End(Not run) metalondaall Metagenomic Longitudinal Differential Abundance Analysis for all Features Identify significant features and their significant time interval metalondaall(count, Time, Group, ID, n.perm = 500, fit.method = "nbinomial", num.intervals = 100, parall = FALSE, pvalue.threshold = 0.05, adjust.method = "BH", time.unit = "days", norm.method = "none", prefix = "Output")
7 metalondaall 7 Count Time Group ID n.perm fit.method num.intervals parall Count matrix of all features Time label of all samples Group label of all samples individual ID label for samples number of permutations The fitting method (NB, LOWESS) The number of time intervals at which metalonda test differential abundance logic to indicate whether to use multicore pvalue.threshold p-value threshold cutoff adjust.method time.unit norm.method prefix Multiple testing correction methods time unit used in the Time vector (hours, days, weeks, months, etc.) normalization method to be used to normalize count matrix (CSS, median-ratio, etc.) prefix for the output figure Value Returns a list of the significant features a long with their significant time intervals ## Not run: data(metalonda_test_data) n.sample = 5 n.timepoints = 10 n.group = 2 Group = factor(c(rep(0, n.sample*n.timepoints), rep(1,n.sample*n.timepoints))) Time = rep(rep(1:n.timepoints, times = n.sample), 2) ID = factor(rep(1:(2*n.sample), each = n.timepoints)) points = seq(1, 10, length.out = 10) output.nbinomial = metalondaall(count = metalonda_test_data, Time = Time, Group = Group, ID = ID, n.perm = 10, fit.method = "nbinomial", num.intervals = 100, parall = FALSE, pvalue.threshold = 0.05, adjust.method = "BH", time.unit = "hours", norm.method = "none", prefix = "Test") ## End(Not run)
8 8 normalize metalonda_test_data Simulated data of OTU abundance for 2 phenotypes each has 5 subjects at 10 time-points The dataset is used for testing the MetaLonDA metalonda_test_data Format A data frame with 2 OTUs patterns normalize Normalize count matrix Normalize count matrix normalize(count, method = "css") count method count matrix normalization method
9 permutation 9 permutation Permute group labels Permutes the group label of the samples in order to construct the AR empirical distibution permutation(perm.dat, n.perm = 500, method = "nbinomial", points, lev) perm.dat n.perm method points lev dataframe has the Count, Group, ID, Time number of permutations The fitting method (negative binomial, LOWESS) The points at which the prediction should happen the two level s name Value returns the fitted model for all the permutations data(metalonda_test_data) n.sample = 5 n.timepoints = 10 n.perm = 3 n.group = 2 Group = factor(c(rep(0, n.sample*n.timepoints), rep(1, n.sample*n.timepoints))) Time = rep(rep(1:n.timepoints, times = n.sample), 2) ID = factor(rep(1:(2*n.sample), each = n.timepoints)) points = seq(1, 10, length.out = 10) aggregate.df = data.frame(count = metalonda_test_data[1,], Time = Time, Group = Group, ID = ID) prm = permutation(aggregate.df, n.perm = 3, method = "nbinomial", points)
10 10 visualizearhistogram visualizearea Visualize significant time interval Visualize significant time interval visualizearea(aggregate.df, model.ss, method, start, end, text, group.levels, unit = "days") aggregate.df model.ss method start end text group.levels unit Dataframe has the Count, Group, ID, Time The fitted model Fitting method (negative binomial or LOWESS) Vector of the start points of the time intervals Vector of the end points of the time intervals Feature name Level s name time unit used in the Time vector (hours, days, weeks, months, etc.) visualizearhistogram Visualize Area Ratio (AR) empirical distribution Visualize Area Ratio (AR) empirical distribution for each time interval visualizearhistogram(permuted, text, method) permuted text method Permutation of the permuted data Feature name fitting method
11 visualizefeature 11 visualizefeature Visualize Longitudinal Feature Visualize Longitudinal Feature visualizefeature(df, text, group.levels, unit = "days") df text group.levels unit dataframe has the Count, Group, ID, Time feature name The two level s name time interval unit data(metalonda_test_data) n.sample = 5 n.timepoints = 10 n.group = 2 Group = factor(c(rep(0, n.sample*n.timepoints), rep(1, n.sample*n.timepoints))) Time = rep(rep(1:n.timepoints, times = n.sample), 2) ID = factor(rep(1:(2*n.sample), each = n.timepoints)) points = seq(1, 10, length.out = 10) aggregate.df = data.frame(count = metalonda_test_data[1,], Time = Time, Group = Group, ID = ID) visualizefeature(aggregate.df, text = rownames(metalonda_test_data)[1], Group)
12 12 visualizetimeintervals visualizefeaturespline Visualize the feature trajectory with the fitted Splines Plot the longitudinal features along with the fitted splines visualizefeaturespline(df, model, method, text, group.levels, unit = "days") df model method text group.levels unit dataframe has the Count, Group, ID, Time the fitted model The fitting method (negative binomial, LOWESS) feature name The two level s name time unit used in the Time vector (hours, days, weeks, months, etc.) visualizetimeintervals Visualize all significant time intervals for all tested features Visualize all significant time intervals for all tested features visualizetimeintervals(interval.details, prefix = "MetaLonDA_timeline", unit = "days") interval.details Dataframe has infomation about significant interval (feature name, start, end, dominant, p-value) prefix unit prefix for the output figure time unit used in the Time vector (hours, days, weeks, months, etc.)
13 visualizetimeintervals 13
14 Index Topic datasets metalonda_test_data, 8 areapermutation, 2 curvefitting, 3 findsiginterval, 4 intervalarea, 4 metalonda, 5 metalonda_test_data, 8 metalondaall, 6 normalize, 8 permutation, 9 visualizearea, 10 visualizearhistogram, 10 visualizefeature, 11 visualizefeaturespline, 12 visualizetimeintervals, 12 14
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Title Nima Hejazi's R Toolbox Version 0.5.0 Package nima May 23, 2018 Miscellaneous R functions developed over the course of statistical research and scientific computing. These include, for example, utilities
Package estprod. May 2, 2018
Title Estimation of Production Functions Version 1.1 Date 2018-05-01 Package estprod May 2, 2018 Estimation of production functions by the Olley-Pakes, Levinsohn- Petrin and Wooldrge methodologies. The
Package ClusterBootstrap
Package ClusterBootstrap June 26, 2018 Title Analyze Clustered Data with Generalized Linear Models using the Cluster Bootstrap Date 2018-06-26 Version 1.0.0 Provides functionality for the analysis of clustered
Package qualmap. R topics documented: September 12, Type Package
Type Package Package qualmap September 12, 2018 Title Opinionated Approach for Digitizing Semi-Structured Qualitative GIS Data Version 0.1.1 Provides a set of functions for taking qualitative GIS data,
Package nngeo. September 29, 2018
Type Package Title k-nearest Neighbor Join for Spatial Data Version 0.2.2 Package nngeo September 29, 2018 K-nearest neighbor search for projected and non-projected 'sf' spatial layers. Nearest neighbor
Package AdhereR. June 15, 2018
Type Package Title Adherence to Medications Version 0.2.0 Author Dan Dediu [aut, cre], Alexandra Dima [aut] Maintainer Dan Dediu Package AdhereR June 15, 2018 Description Computation
Package rqt. November 21, 2017
Type Package Title rqt: utilities for gene-level meta-analysis Version 1.4.0 Package rqt November 21, 2017 Author I. Y. Zhbannikov, K. G. Arbeev, A. I. Yashin. Maintainer Ilya Y. Zhbannikov
Package NFP. November 21, 2016
Type Package Title Network Fingerprint Framework in R Version 0.99.2 Date 2016-11-19 Maintainer Yang Cao Package NFP November 21, 2016 An implementation of the network fingerprint
Package PCADSC. April 19, 2017
Type Package Package PCADSC April 19, 2017 Title Tools for Principal Component Analysis-Based Data Structure Comparisons Version 0.8.0 A suite of non-parametric, visual tools for assessing differences
Package NBLDA. July 1, 2018
Type Package Package NBLDA July 1, 2018 Title Negative Binomial Linear Discriminant Analysis Version 0.99.0 Date 2018-06-25 We proposed a package for classification task which uses Negative Binomial distribution
Package Hapi. July 28, 2018
Type Package Package Hapi July 28, 2018 Title Inference of Chromosome-Length Haplotypes Using Genomic Data of Single Gamete Cells Version 0.0.3 Author, Han Qu, Jinfeng Chen, Shibo Wang, Le Zhang, Julong
Package gifti. February 1, 2018
Type Package Package gifti February 1, 2018 Title Reads in 'Neuroimaging' 'GIFTI' Files with Geometry Information Version 0.7.5 Author John Muschelli Maintainer John Muschelli Functions
Package multihiccompare
Package multihiccompare September 23, 2018 Title Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are available Version 0.99.44 multihiccompare provides
Package SFtools. June 28, 2017
Type Package Title Space Filling Based Tools for Data Mining Version 0.1.0 Author Mohamed Laib and Mikhail Kanevski Package SFtools June 28, 2017 Maintainer Mohamed Laib Contains space
Package messaging. May 27, 2018
Type Package Package messaging May 27, 2018 Title Conveniently Issue Messages, Warnings, and Errors Version 0.1.0 Description Provides tools for creating and issuing nicely-formatted text within R diagnostic
Package pcr. November 20, 2017
Version 1.1.0 Title Analyzing Real-Time Quantitative PCR Data Package pcr November 20, 2017 Calculates the amplification efficiency and curves from real-time quantitative PCR (Polymerase Chain Reaction)
Package catenary. May 4, 2018
Type Package Title Fits a Catenary to Given Points Version 1.1.2 Date 2018-05-04 Package catenary May 4, 2018 Gives methods to create a catenary object and then plot it and get properties of it. Can construct