Title: Differential Analysis of Rhythmic Transcriptome Data
Version: 1.0.1
Description: A flexible approach, inspired by cosinor regression, for differential analysis of rhythmic transcriptome data. See Singer and Hughey (2018) <doi:10.1177/0748730418813785>.
Depends: R (≥ 3.4)
License: GPL-2
URL: https://limorhyde.hugheylab.org, https://github.com/hugheylab/limorhyde
Encoding: UTF-8
RoxygenNote: 7.1.2
Imports: pbs (≥ 1.1)
Suggests: annotate (≥ 1.58.0), data.table (≥ 1.12.2), foreach (≥ 1.4.4), ggplot2 (≥ 2.2.1), knitr (≥ 1.20), limma (≥ 3.36.1), matrixStats (≥ 0.56.0), org.Mm.eg.db (≥ 3.6.0), qs (≥ 0.25.2), rmarkdown (≥ 1.9), testthat (≥ 3.0.4)
VignetteBuilder: knitr
BugReports: https://github.com/hugheylab/limorhyde/issues
NeedsCompilation: no
Packaged: 2022-02-17 17:13:40 UTC; joshuaschoenbachler
Author: Jake Hughey [aut, cre], Jordan Singer [ctb]
Maintainer: Jake Hughey <jakejhughey@gmail.com>
Repository: CRAN
Date/Publication: 2022-02-18 08:20:05 UTC

Basis matrix for cosinor

Description

Generate basis matrix for cosinor regression.

Usage

getCosinorBasis(x, period, intercept)

Arguments

x

Values of the predictor variable.

period

Period for the predictor variable.

intercept

If TRUE, a column of ones will be included in the basis.

Value

A matrix with a row for each value of x and a column for each component of the decomposition.

Examples

b = getCosinorBasis(seq(0, 20, 4), period = 24, intercept = FALSE)

Basis matrix for periodic splines

Description

Generate basis matrix for a periodic B-spline using pbs::pbs().

Usage

getSplineBasis(x, period, nKnots, intercept)

Arguments

x

Values of the predictor variable.

period

Period for the predictor variable.

nKnots

Number of internal knots.

intercept

If TRUE, a column of ones will be included in the basis.

Value

A matrix with a row for each value of x and a column for each component of the decomposition.

Examples

b = getSplineBasis(seq(0, 20, 4), period = 24, nKnots = 3, intercept = FALSE)

Convert a periodic time variable into components usable in linear models

Description

Decompose a periodic time variable into multiple components based on either the first harmonic of a Fourier series or on a periodic smoothing spline.

Usage

limorhyde(
  time,
  colnamePrefix = NULL,
  period = 24,
  sinusoid = TRUE,
  nKnots = 3,
  intercept = FALSE
)

Arguments

time

Numeric vector of times, e.g., at which samples were acquired.

colnamePrefix

Character string with which to prefix the column names of the basis.

period

Number corresponding to the period to use for the decomposition (in the same units as time).

sinusoid

If TRUE, the decomposition is based on cosinor, i.e., cosine and sine. If FALSE, the decomposition is based on a periodic smoothing spline from the pbs package.

nKnots

Number of internal knots for the periodic spline. Only used if sinusoid is FALSE.

intercept

If TRUE, a column of ones will be included in the basis.

Value

A matrix with a row for each sample and a column for each component of the time decomposition.

Examples

# create an example data frame
nSamples = 12
d = data.frame(
  sample = paste0('sample_', 1:nSamples),
  genotype = factor(rep(c('WT', 'KO'), each = nSamples / 2),
                    levels = c('WT', 'KO')),
  zt = rep(seq(0, 24 - 24 / nSamples * 2, 24 / nSamples * 2), times = 2),
  stringsAsFactors = FALSE)

# call limorhyde
limo = limorhyde(d$zt, 'zt_')
d = cbind(d, limo)

# create a design matrix that could be used with methods such as limma
design = model.matrix(~ genotype * (zt_cos + zt_sin), data = d)