Gene expression data for GSE34018

Description

Data are based on total RNA, measured by microarray, obtained from livers of wild-type and liver-specific Reverb alpha/beta double knockout mice at various times in a 12h:12h light:dark cycle. To save space and time, the data include only a subset of genes, and so are mainly useful for examples of how to use limorhyde2.

Usage

GSE34018

Format

A list with two elements:

• y: Matrix of normalized, log-transformed expression values. Rows correspond to genes (rownames are Entrez Gene IDs) and columns to samples.

• metadata: data.table with one row per sample. time is in hours.

Source

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE34018

See Also

GSE54650, getModelFit()

Gene expression data for GSE54650

Description

Data are based on total RNA, measured by microarray, obtained from livers of wild-type mice at various times after transfer to constant darkness. To save space and time, the data include only a subset of genes, and so are mainly useful for examples of how to use limorhyde2.

Usage

GSE54650

Format

A list with two elements:

• y: Matrix of normalized, log-transformed expression values. Rows correspond to genes (rownames are Entrez Gene IDs) and columns to samples.

• metadata: data.table with one row per sample. time is in hours.

Source

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54650

See Also

GSE34018, getModelFit()

Compute differential rhythm statistics from fitted models

Description

This function computes differences in rhythmicity between fitted curves for a given pair of conditions.

Usage

getDiffRhythmStats(fit, rhyStats, conds = fit$conds, dopar = TRUE)

Arguments

Value

A data.table containing the following differential rhythm statistics:

• mean_mesor

• mean_peak_trough_amp

• mean_rms_amp (only calculated if rms to getRhythmStats() was TRUE)

• diff_mesor

• diff_peak_trough_amp

• diff_rms_amp (only calculated if rms to getRhythmStats() was TRUE)

• diff_peak_phase: circular difference between -fit$period/2 and fit$period/2

• diff_trough_phase: circular difference between -fit$period/2 and fit$period/2

• diff_rhy_dist: Euclidean distance between polar coordinates (peak_trough_amp, peak_phase)

• rms_diff_rhy: root mean square difference in mean-centered fitted curves (only calculated if rms to getRhythmStats() was TRUE)

The stats will be based on the value for cond2 minus the value for cond1. The rows of the data.table depend on the 'fitType' attribute of rhyStats:

• 'fitType' is 'posterior_mean' or 'raw': one row per feature per pair of conditions.

• 'fitType' is 'posterior_samples': one row per feature per posterior sample per pair of conditions.

See Also

getRhythmStats(), getStatsIntervals()

Examples

library('data.table')

# rhythmicity in one condition
y = GSE54650$y
metadata = GSE54650$metadata

fit = getModelFit(y, metadata)
fit = getPosteriorFit(fit)
rhyStats = getRhythmStats(fit, features = c('13170', '13869'))

# rhythmicity and differential rhythmicity in multiple conditions
y = GSE34018$y
metadata = GSE34018$metadata

fit = getModelFit(y, metadata, nKnots = 3L, condColname = 'cond')
fit = getPosteriorFit(fit)
rhyStats = getRhythmStats(fit, features = c('13170', '12686'))
diffRhyStats = getDiffRhythmStats(fit, rhyStats)

Compute expected measurements from fitted models

Description

This function computes expected measurements (corresponding to the fitted curves) for the specified times and features in all combinations of conditions and covariates (if they exist).

Usage

getExpectedMeas(
  fit,
  times,
  fitType = c("posterior_mean", "posterior_samples", "raw"),
  features = NULL,
  dopar = TRUE
)

Arguments

Value

A data.table.

See Also

getModelFit(), getPosteriorFit(), getPosteriorSamples(), getExpectedMeasIntervals()

Examples

library('data.table')

y = GSE34018$y
metadata = GSE34018$metadata

fit = getModelFit(y, metadata)
fit = getPosteriorFit(fit)

measObs = mergeMeasMeta(y, metadata, features = c('13170', '12686'))
measFitMean = getExpectedMeas(
  fit, times = seq(0, 24, 0.5), features = c('13170', '12686'))

Compute credible intervals for expected measurements

Description

This functions uses posterior samples to quantify uncertainty in the expected measurements from fitted models.

Usage

getExpectedMeasIntervals(expectedMeas, mass = 0.9, method = c("eti", "hdi"))

Arguments

Value

A data.table containing lower and upper bounds of the expected measurement for each combination of feature, time, and possibly condition and covariate.

See Also

getExpectedMeas(), getStatsIntervals()

Examples

library('data.table')

y = GSE34018$y
metadata = GSE34018$metadata

fit = getModelFit(y, metadata)
fit = getPosteriorFit(fit)
fit = getPosteriorSamples(fit, nPosteriorSamples = 10L)

measFitSamps = getExpectedMeas(
  fit, times = seq(0, 24, 0.5), fitType = 'posterior_samples',
  features = c('13170', '12686'))
measFitInts = getExpectedMeasIntervals(measFitSamps)

Fit linear models for rhythmicity in one or more conditions

Description

This is the first step in an analysis using limorhyde2, the second is to moderate the fits using getPosteriorFit().

Usage

getModelFit(
  y,
  metadata,
  period = 24,
  nKnots = 3L,
  degree = if (nKnots > 2) 3L else 2L,
  sinusoid = FALSE,
  timeColname = "time",
  condColname = NULL,
  covarColnames = NULL,
  sampleColname = "sample",
  nShifts = 3L,
  method = c("trend", "voom", "deseq2"),
  lmFitArgs = list(),
  eBayesArgs = if (method == "trend") list(trend = TRUE) else list(),
  DESeqArgs = list(),
  keepLmFits = FALSE
)

Arguments

Value

A limorhyde2 object with elements:

• metadata: As supplied above, converted to a data.table.

• timeColname: As supplied above.

• condColname: As supplied above.

• covarColnames: As supplied above.

• coefficients: Matrix with rows corresponding to features and columns to model terms, including all shifted models.

• shifts: Numeric vector indicating amount by which timepoints were shifted for each shifted model.

• period: As supplied above.

• conds: If condColname is not NULL, a vector of unique values of the condition variable.

• nKnots: Number of knots.

• degree: As supplied above.

• sinusoid: As supplied above.

• nConds: Number of conditions.

• nCovs: Number of covariates.

• lmFits: If keepLmFits is TRUE, a list of objects from limma or DESeq2, with length equal to length of the shifts element.

See Also

getPosteriorFit()

Examples

library('data.table')

# rhythmicity in one condition
y = GSE54650$y
metadata = GSE54650$metadata

fit = getModelFit(y, metadata)
fit = getPosteriorFit(fit)
rhyStats = getRhythmStats(fit, features = c('13170', '13869'))

# rhythmicity and differential rhythmicity in multiple conditions
y = GSE34018$y
metadata = GSE34018$metadata

fit = getModelFit(y, metadata, nKnots = 3L, condColname = 'cond')
fit = getPosteriorFit(fit)
rhyStats = getRhythmStats(fit, features = c('13170', '12686'))
diffRhyStats = getDiffRhythmStats(fit, rhyStats)

Compute posterior fit for linear models for rhythmicity

Description

This is the second step in an analysis using limorhyde2, the first is to fit linear models using getModelFit(). This function obtains posterior estimates of coefficients using multivariate adaptive shrinkage (mash), which learns patterns in the data and accounts for noise in the original fits. The defaults for arguments should work well in most cases, so only change them if you know what you're doing.

Usage

getPosteriorFit(
  fit,
  covMethod = c("data-driven", "canonical", "both"),
  getSigResArgs = list(),
  npc = fit$nKnots,
  covEdArgs = list(),
  overwrite = FALSE,
  ...
)

Arguments

Value

A limorhyde2 object containing everything in fit with added or updated elements:

• mashData: list of mash data objects

• mashFits: list of mash fit objects

• mashCoefficients: Matrix of posterior mean coefficients, with rows corresponding to features and columns to model terms.

• mashIdx: Vector indicating which model terms were included in the mash fit.

See Also

getModelFit(), getRhythmStats(), getExpectedMeas()

Examples

library('data.table')

# rhythmicity in one condition
y = GSE54650$y
metadata = GSE54650$metadata

fit = getModelFit(y, metadata)
fit = getPosteriorFit(fit)
rhyStats = getRhythmStats(fit, features = c('13170', '13869'))

# rhythmicity and differential rhythmicity in multiple conditions
y = GSE34018$y
metadata = GSE34018$metadata

fit = getModelFit(y, metadata, nKnots = 3L, condColname = 'cond')
fit = getPosteriorFit(fit)
rhyStats = getRhythmStats(fit, features = c('13170', '12686'))
diffRhyStats = getDiffRhythmStats(fit, rhyStats)

Draw samples from posterior distributions of fitted models

Description

This is an optional step in an analysis using limorhyde2, and is useful for quantifying uncertainty in posterior estimates of fitted curves and rhythmic statistics. The function calls mashr::mash_compute_posterior_matrices().

Usage

getPosteriorSamples(fit, nPosteriorSamples = 200L, overwrite = FALSE)

Arguments

Value

A limorhyde2 object containing everything in fit with added or updated element:

• mashPosteriorSamples: a three-dimensional array of coefficients, with dim 1 corresponding to features, dim 2 to model terms, and dim 3 to posterior samples.

See Also

getPosteriorFit(), getRhythmStats(), getExpectedMeas(), getStatsIntervals()

Examples

library('data.table')

y = GSE54650$y
metadata = GSE54650$metadata

fit = getModelFit(y, metadata)
fit = getPosteriorFit(fit)
fit = getPosteriorSamples(fit, nPosteriorSamples = 10L)

rhyStatsSamps = getRhythmStats(
  fit, features = c('13170', '13869'), fitType = 'posterior_samples')
rhyStatsInts = getStatsIntervals(rhyStatsSamps)

Compute rhythm statistics from fitted models

Description

This function uses stats::optim() to compute various properties of fitted curves with respect to time, potentially in each condition and for each posterior sample, and adjusting for any covariates.

Usage

getRhythmStats(
  fit,
  fitType = c("posterior_mean", "posterior_samples", "raw"),
  features = NULL,
  dopar = TRUE,
  rms = FALSE
)

Arguments

Value

A data.table containing the following rhythm statistics:

• peak_phase: time between 0 and fit$period at which the peak or maximum value occurs

• peak_value

• trough_phase: time between 0 and fit$period at which the trough or minimum value occurs

• trough_value

• peak_trough_amp: peak_value - trough_value

• rms_amp: root mean square difference between fitted curve and mean value between time 0 and fit$period (only calculated if rms is TRUE)

• mesor: mean value between time 0 and fit$period

The rows of the data.table depend on the fit object and fitType:

• fit contains data from one condition and fitType is posterior_mean' or 'raw': one row per feature.

• fit contains data from one condition and fitType is 'posterior_samples': one row per feature per posterior sample.

• fit contains data from multiple conditions and fitType is 'posterior_mean' or 'raw': one row per feature per condition.

• fit contains data from multiple conditions and fitType is 'posterior_samples': one row per feature per condition per posterior sample.

See Also

getModelFit(), getPosteriorFit(), getPosteriorSamples(), getDiffRhythmStats(), getStatsIntervals()

Examples

library('data.table')

# rhythmicity in one condition
y = GSE54650$y
metadata = GSE54650$metadata

fit = getModelFit(y, metadata)
fit = getPosteriorFit(fit)
rhyStats = getRhythmStats(fit, features = c('13170', '13869'))

# rhythmicity and differential rhythmicity in multiple conditions
y = GSE34018$y
metadata = GSE34018$metadata

fit = getModelFit(y, metadata, nKnots = 3L, condColname = 'cond')
fit = getPosteriorFit(fit)
rhyStats = getRhythmStats(fit, features = c('13170', '12686'))
diffRhyStats = getDiffRhythmStats(fit, rhyStats)

Compute credible intervals for rhythm or differential rhythm statistics

Description

This function uses posterior samples to quantify uncertainty in the properties of fitted curves.

Usage

getStatsIntervals(posteriorStats, mass = 0.9, method = c("eti", "hdi"))

Arguments

Value

A data.table containing lower and upper bounds of various statistics for each feature or each feature-condition pair. For peak_trough_amp and rms_amp, a negative lower bound indicates a rhythm of the opposite phase.

See Also

getRhythmStats(), getDiffRhythmStats(), getExpectedMeasIntervals()

Examples

library('data.table')

y = GSE54650$y
metadata = GSE54650$metadata

fit = getModelFit(y, metadata)
fit = getPosteriorFit(fit)
fit = getPosteriorSamples(fit, nPosteriorSamples = 10L)

rhyStatsSamps = getRhythmStats(
  fit, features = c('13170', '13869'), fitType = 'posterior_samples')
rhyStatsInts = getStatsIntervals(rhyStatsSamps)

Merge measurements and metadata

Description

This function is useful for plotting time-courses for individual features.

Usage

mergeMeasMeta(y, metadata, features = NULL, sampleColname = "sample")

Arguments

Value

A data.table with one row for each sample-feature pair.

See Also

getExpectedMeas()

Examples

library('data.table')

y = GSE34018$y
metadata = GSE34018$metadata

fit = getModelFit(y, metadata)
fit = getPosteriorFit(fit)

measObs = mergeMeasMeta(y, metadata, features = c('13170', '12686'))
measFitMean = getExpectedMeas(
  fit, times = seq(0, 24, 0.5), features = c('13170', '12686'))