Quantifying uncertainty in (differential) rhythmicity
In limorhyde2: Quantify Rhythmicity and Differential Rhythmicity in Genomic Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = '#>',
  message = FALSE,
  fig.align = 'center',
  fig.retina = 2)

Introduction

Here we show how to use limorhyde2 to quantify uncertainty in rhythmicity and differential rhythmicity. This step is not essential and can be computationally expensive, but can provide additional information. As limorhyde2 is a Bayesian approach focused on effect sizes rather than statistical significance, quantifying uncertainty relies on the concepts of posterior probability and credible intervals.

The data are based on liver samples from wild-type and Rev-erb$\alpha/\beta$ double-knockout mice (Cho et al. 2012 and GSE34018).

Load packages

library('cowplot')
library('data.table')
library('ggplot2')
library('limorhyde2')
library('qs')

# doParallel::registerDoParallel() # register a parallel backend to minimize runtime
theme_set(theme_bw())

Load the data

The expression data are in a matrix with one row per gene and one column per sample. The metadata are in a table with one row per sample. To save time and space, the expression data include only a subset of genes.

y = GSE34018$y
y[1:5, 1:5]

metadata = GSE34018$metadata
metadata

Fit linear models and compute posterior fits

Because the samples were acquired at relatively low temporal resolution (every 4 h), we use three knots instead of the default four, which reduces the flexibility of the spline curves. We specify condColname so getModelFit() knows to fit a differential rhythmicity model.

fit = getModelFit(y, metadata, nKnots = 3L, condColname = 'cond')
fit = getPosteriorFit(fit)

Draw samples from the posterior fits

The posterior fits consist of not just a single set of model coefficients (the posterior means), but distributions of model coefficients. Sampling from these distributions is the first step to quantifying uncertainty in the fits. Here we generate 50 posterior samples, although an actual analysis would require more to accurately estimate the credible intervals.

fit = getPosteriorSamples(fit, nPosteriorSamples = 50L)

Get fitted time-courses

We can use the posterior samples to quantify uncertainty in the expected measurements, i.e., the fitted curves, by specifying the fitType argument. Here we focus on three genes.

genes = data.table(
  id = c('13170', '12686', '26897'),
  symbol = c('Dbp', 'Elovl3', 'Acot1'))
times = seq(0, 24, 0.5)

measFitSamps = getExpectedMeas(
  fit, times = times, fitType = 'posterior_samples', features = genes$id)
measFitSamps[genes, symbol := i.symbol, on = .(feature = id)]
print(measFitSamps, nrows = 10L)

Given the expected measurements from the posterior samples, we can compute the lower and upper bounds of the credible interval for each combination of feature, condition, and time-point. By default, getExpectedMeasIntervals() calculates the 90% equal-tailed interval.

measFitInts = getExpectedMeasIntervals(measFitSamps)
print(measFitInts, nrows = 10L)

It's always a good idea to also calculate the posterior mean fitted curves.

measFitMean = getExpectedMeas(fit, times = times, features = genes$id)
measFitMean[genes, symbol := i.symbol, on = .(feature = id)]

Now we can plot the results. In the first row, each curve corresponds to a posterior sample. In the second row, the ribbons indicate the credible intervals.

timeBreaks = seq(0, 24, 4)
pal = 'Dark2'

p1 = ggplot(measFitSamps) +
  facet_wrap(vars(symbol), scales = 'fixed', nrow = 1) +
  geom_line(aes(x = time, y = value, color = cond,
                group = interaction(cond, posterior_sample)), alpha = 0.2) +
  labs(x = 'Circadian time (h)', y = 'Expression (norm.)', color = 'Condition') +
  scale_x_continuous(breaks = timeBreaks) +
  scale_color_brewer(palette = pal) +
  theme(legend.position = 'none')

p2 = ggplot() +
  facet_wrap(vars(symbol), scales = 'fixed', nrow = 1) +
  geom_ribbon(aes(x = time, ymin = lower, ymax = upper, fill = cond),
              alpha = 0.3, data = measFitInts) +
  geom_line(aes(x = time, y = value, color = cond), data = measFitMean) +
  labs(x = 'Circadian time (h)', y = 'Expression (norm.)', color = 'Condition',
       fill = 'Condition') +
  scale_x_continuous(breaks = timeBreaks) +
  scale_fill_brewer(palette = pal) +
  scale_color_brewer(palette = pal) +
  theme(legend.position = 'bottom')

plot_grid(p1, p2, ncol = 1, rel_heights = c(1, 1.25))

Get rhythm and differential rhythm statistics

We can also use the posterior samples to quantify uncertainty in the statistics, again by specifying the fitType argument.

rhyStatsSamps = getRhythmStats(fit, features = genes$id, fitType = 'posterior_samples')
diffRhyStatsSamps = getDiffRhythmStats(fit, rhyStatsSamps)
diffRhyStatsSamps[genes, symbol := i.symbol, on = .(feature = id)]
print(diffRhyStatsSamps, nrows = 10L)

In the plots below, each point represents a posterior sample.

p1 = ggplot(diffRhyStatsSamps) +
  facet_wrap(vars(symbol), nrow = 1) +
  geom_point(aes(x = diff_peak_trough_amp, y = diff_mesor), alpha = 0.2) +
  labs(x = bquote(Delta * 'amplitude (norm.)'), y = bquote(Delta * 'mesor (norm.)'))

p2 = ggplot(diffRhyStatsSamps) +
  facet_wrap(vars(symbol), nrow = 1) +
  geom_point(aes(x = diff_peak_trough_amp, y = diff_peak_phase), alpha = 0.2) +
  labs(x = bquote(Delta * 'amplitude (norm.)'), y = bquote(Delta * 'phase (h)'))

plot_grid(p1, p2, ncol = 1)

Finally, we can compute credible intervals for the rhythm and differential rhythm statistics. Again, by default these are 90% equal-tailed intervals. Currently getStatsIntervals() does not calculate intervals for phase-based statistics, since phase and phase difference are circular quantities.

rhyStatsInts = getStatsIntervals(rhyStatsSamps)
print(rhyStatsInts, nrows = 10L)

diffRhyStatsInts = getStatsIntervals(diffRhyStatsSamps)
print(diffRhyStatsInts, nrows = 10L)