Package aspi. R topics documented: September 20, 2016
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1 Type Package Title Analysis of Symmetry of Parasitic Infections Version Date Author Matt Wayland Maintainer Matt Wayland Package aspi September 20, 2016 Tools for the analysis and visualization of bilateral asymmetry in parasitic infections. Depends R (>= 2.10) License GPL-3 LazyData TRUE Suggests knitr, rmarkdown, testthat VignetteBuilder knitr RoxygenNote NeedsCompilation no Repository CRAN Date/Publication :46:39 R topics documented: aspi diplostomum_eyes_excl_lenses diplostomum_lenses eb.test g.test plothistogram plotvolcano simulated_asymmetry_inconsistent_bias simulated_left_bias_heterogeneous_proportions simulated_left_bias_homogeneous_proportions simulated_symmetrical_infection Index 9 1
2 2 diplostomum_eyes_excl_lenses aspi ASPI: Analysis of Symmetry in Parasitic Infections Tools for the analysis and visualization of bilateral asymmetry in parasitic infections. Functions g.test eb.test plothistogram plotvolcano Data diplostomum_eyes_excl_lenses diplostomum_lenses simulated_symmetrical_infection simulated_left_bias_homogeneous_proportions simulated_left_bias_heterogeneous_proportions simulated_asymmetry_inconsistent_bias Author(s) Matt Wayland, diplostomum_eyes_excl_lenses Numbers of Diplostomum metacercariae recorded from the eyes (excluding lenses) of each of 50 ruffe. A dataset containing the numbers of Diplostomum metacercariae recovered from the eyes (excluding lenses) of each of 50 ruffe, Gymnocephalus cernuus from Llyn Tegid, a mesotrophic lake in north Wales. diplostomum_eyes_excl_lenses
3 diplostomum_lenses 3 A data frame with 50 rows and 2 variables (row names are host IDs): left number of metacercariae in left eye right number of metacercariae in the right eye diplostomum_lenses Numbers of Diplostomum metacercariae recorded from the lenses of the eyes of each of 50 ruffe. A dataset containing the numbers of Diplostomum metacercariae recovered from the lenses of the eyes of each of 50 ruffe, Gymnocephalus cernuus from Llyn Tegid, a mesotrophic lake in north Wales. diplostomum_lenses A data frame with 50 rows and 2 variables (row names are host IDs): left number of metacercariae in lens of left eye right number of metacercariae in lens of right eye eb.test Exact binomial tests Assess symmetry of parasitic infections by performing exact binomial tests on pooled data and individual hosts. eb.test(x) Arguments x a matrix or data frame with two numeric columns; first column is for left-side and 2nd column for right-side. Identifiers for hosts can be provided as row names.
4 4 g.test Details This function performs a binomial exact tests with the null hypothesis of a 1:1 ratio. It takes as its argument a matrix or data frame with two numeric columns; first column is for left-side and 2nd column for right-side. Identifiers for hosts can be provided as row names. Uninfected hosts (zero count for both left and right sides) are ignored. Value It returns a list containing two elements: pooled hosts p-value for pooled binomial exact test (null hypothesis: the ratio of the total number of parasites from each side doesn t differ from 1:1). data.frame of results of binomial exact tests performed on the distribution of parasites in each host. Examples eb.test(diplostomum_lenses) g.test Replicated G-tests of goodness-of-fit Perform replicated G-tests of goodness-of-fit to assess symmetry of parasitic infections. g.test(x) Arguments x a matrix or data frame with two numeric columns; first column is for left-side and 2nd column for right-side. Identifiers for hosts can be provided as row names. Details This function implements Sokal & Rohlf s (1995) G-test for the specific case of an expected 1:1 ratio The function takes as its argument a matrix or data frame with two numeric columns; first column is for left-side and 2nd column for right-side. Identifiers for hosts can be provided as row names. Uninfected hosts (zero count for both left and right sides) are ignored. Cannot be applied to data containing zero counts; use eb.test instead.
5 plothistogram 5 Value A list containing two data.frames: summary hosts results of total, heterogeneity and pooled G-tests. Data frame has four columns: test, degrees of freedom, G-statistic and p-value. results of individual G-tests on distribution of parasites in each host. Data frame has seven columns: Host (ID), Left (count of parasites on left side), Right (count of parasites on right side), G (G-statistic), p (p-value), BH (p-value adjusted using Benjamini and Hochberg s procedure for controlling the false discovery rate) and Holm (p-value adjusted using Holm s method). References R.R. Sokal & F.J. Rohlf (1995) Biometry. 3rd Edition. New York: W.H. Freeman and Company. 887 pp. Examples g.test(diplostomum_eyes_excl_lenses) plothistogram Plot histogram Creates a histogram showing distribution of fold differences in abundance of parasites between left and right sides of host. plothistogram(x, nbreaks = 10,...) Arguments x a matrix or data frame with two numeric columns; first column is for left-side and 2nd column for right-side. Identifiers for hosts can be provided as row names. nbreaks number of cells for the histogram. A suggestion only; breakpoints will be set to pretty values.... optional further arguments and graphical parameters passed to plot. Details plot.histogram creates a histogram showing distribution of fold differences in abundance of parasites between left and right sides. For each infected host the number of parasites on the right side is divided by the number of parasites on the left side, and the result binary log transformed. The log2 ratio will be negative if there are more parasites on the left than right and positive if there are more parasites on the right than left. A log2 ratio of one corresponds to a one-fold difference, i.e. double the number of parasites. Perfect symmetry is a log2 ratio of zero.
6 6 plotvolcano Examples plothistogram(diplostomum_eyes_excl_lenses) plothistogram(diplostomum_eyes_excl_lenses,nbreaks=20, main="diplostomum metacercariae in eyes of ruffe") plotvolcano Volcano plot Produces scatterplot of statistical significance vs fold difference in parasite abundance between left and right. plotvolcano(x, test = "G", padj = "BH", sigthresh = 0.05,...) Arguments x test padj Details sigthresh a matrix or data frame with two numeric columns; first column is for left-side and 2nd column for right-side. Identifiers for hosts can be provided as row names. if set to "G" (default) a G-test is performed; otherwise an exact binomial test is performed. method for correcting p-values for multiple comparisons. If set to "BH" (default), Benjamini & Hochberg s procedure is used to control the false discovery rate (FDR); otherwise Holm s methos is used to control the familywise error rate (FWER). significance threshold (defaults to 0.05); p-values below this value will be called significant.... optional further arguments and graphical parameters passed to plot. plot.volcano creates a volcano plot, i.e. a scatterplot of statistical significance (-log10(p-value)) vs fold difference (log2 ratio - as calculated for the histogram above) in parasite abundance between left and right. Each point in the scatterplot represents the parasite distribution in an individual host. A dashed horizontal line represents the user-defined p-value threshold for significance. If a parasite distribution deviates significantly from symmetry it is shown as a red square, otherwise as a blue circle. Examples plotvolcano(diplostomum_eyes_excl_lenses) plotvolcano(diplostomum_eyes_excl_lenses, test="g", padj="bh", sigthresh=0.1, main="diplostomum metacercariae in eyes of ruffe")
7 simulated_asymmetry_inconsistent_bias 7 simulated_asymmetry_inconsistent_bias Simulated data showing bilateral asymmetry with insconsistent bias Simulated data for 10 hosts, providing an example of bilateral asymmetry with inconsistent bias. simulated_asymmetry_inconsistent_bias A data frame with 10 rows and 2 variables: left number of parasites on the left side right number of parasites on the right side simulated_left_bias_heterogeneous_proportions Simulated data showing left bias with heterogeneous proportions Simulated data for 10 hosts, providing an example of bilateral asymmetry with left bias and the left:right ratio varying between hosts. simulated_left_bias_heterogeneous_proportions A data frame with 10 rows and 2 variables: left number of parasites on the left side right number of parasites on the right side
8 8 simulated_symmetrical_infection simulated_left_bias_homogeneous_proportions Simulated data for showing left bias with homogeneous proportions Simulated data for 10 hosts, providing an example of bilateral asymmetry with left bias and a similar left:right ratio in all hosts. simulated_left_bias_homogeneous_proportions A data frame with 10 rows and 2 variables: left number of parasites on the left side right number of parasites on the right side simulated_symmetrical_infection Simulated data showing bilateral symmetry Simulated data for 10 hosts, providing an example of bilateral symmetry. simulated_symmetrical_infection A data frame with 10 rows and 2 variables: left number of parasites on the left side right number of parasites on the right side
9 Index Topic datasets diplostomum_eyes_excl_lenses, 2 diplostomum_lenses, 3 simulated_asymmetry_inconsistent_bias, 7 simulated_left_bias_heterogeneous_proportions, 7 simulated_left_bias_homogeneous_proportions, 8 simulated_symmetrical_infection, 8 aspi, 2 aspi-package (aspi), 2 diplostomum_eyes_excl_lenses, 2 diplostomum_lenses, 3 eb.test, 3 g.test, 4 plothistogram, 5 plotvolcano, 6 simulated_asymmetry_inconsistent_bias, 7 simulated_left_bias_heterogeneous_proportions, 7 simulated_left_bias_homogeneous_proportions, 8 simulated_symmetrical_infection, 8 9
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Looking at the Other Side of Bonferroni
Department of Biostatistics University of Washington 24 May 2012 Multiple Testing: Control the Type I Error Rate When analyzing genetic data, one will commonly perform over 1 million (and growing) hypothesis
Package RootsExtremaInflections
Type Package Package RootsExtremaInflections May 10, 2017 Title Finds Roots, Extrema and Inflection Points of a Curve Version 1.1 Date 2017-05-10 Author Demetris T. Christopoulos Maintainer Demetris T.
Package noise. July 29, 2016
Type Package Package noise July 29, 2016 Title Estimation of Intrinsic and Extrinsic Noise from Single-Cell Data Version 1.0 Date 2016-07-28 Author Audrey Qiuyan Fu and Lior Pachter Maintainer Audrey Q.
Package platetools. R topics documented: June 25, Title Tools and Plots for Multi-Well Plates Version 0.1.1
Title Tools and Plots for Multi-Well Plates Version 0.1.1 Package platetools June 25, 2018 Collection of functions for working with multi- microtitre plates, mainly 96, 384 and 1536 plates. Depends R (>=
Package cosinor2. October 15, 2018
Package cosinor2 October 15, 2018 Title Extended Tools for Cosinor Analysis of Rhythms Statistical procedures for calculating population mean cosinor, non stationary cosinor, estimation of best fitting
Package dhga. September 6, 2016
Type Package Title Differential Hub Gene Analysis Version 0.1 Date 2016-08-31 Package dhga September 6, 2016 Author Samarendra Das and Baidya Nath Mandal
Package effectfusion
Package November 29, 2016 Title Bayesian Effect Fusion for Categorical Predictors Version 1.0 Date 2016-11-21 Author Daniela Pauger [aut, cre], Helga Wagner [aut], Gertraud Malsiner-Walli [aut] Maintainer