In dynverse/dyngen: A Multi-Modal Simulator for Spearheading Single-Cell Omics Analyses

knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
knitr::opts_knit$set(progress = FALSE, verbose = FALSE)

In this vignette, we will take a look at characteristic features of dyngen versus the reference dataset it uses. To this end, we'll be using countsimQC [@Soneson2018] to calculate key statistics of both datasets and create comparative visualisations.

Run dyngen simulation

We use an internal function from the dyngen package to download and cache one of the reference datasets.

library(tidyverse)
library(dyngen)

set.seed(1)

data("realcounts", package = "dyngen")
name_realcounts <- "zenodo_1443566_real_silver_bone-marrow-mesenchyme-erythrocyte-differentiation_mca"
url_realcounts <- realcounts %>% filter(name == name_realcounts) %>% pull(url)
realcount <- dyngen:::.download_cacheable_file(url_realcounts, getOption("dyngen_download_cache_dir"), verbose = FALSE)

We run a simple dyngen dataset as follows, where the number of cells and genes are determined by the size of the reference dataset.

backbone <- backbone_bifurcating_loop()

num_cells <- nrow(realcount)
num_feats <- ncol(realcount)
num_tfs <- nrow(backbone$module_info)
num_tar <- round((num_feats - num_tfs) / 2)
num_hks <- num_feats - num_tfs - num_tar

config <-
  initialise_model(
    backbone = backbone,
    num_cells = num_cells,
    num_tfs = num_tfs,
    num_targets = num_tar,
    num_hks = num_hks,
    gold_standard_params = gold_standard_default(),
    simulation_params = simulation_default(
      total_time = 1000,
      experiment_params = simulation_type_wild_type(num_simulations = 100)
    ),
    experiment_params = experiment_snapshot(
      realcount = realcount
    ),
    verbose = FALSE
  )

# the simulation is being sped up because rendering all vignettes with one core
# for pkgdown can otherwise take a very long time
set.seed(1)

config <-
  initialise_model(
    backbone = backbone,
    num_cells = num_cells,
    num_tfs = num_tfs,
    num_targets = num_tar,
    num_hks = num_hks,
    verbose = interactive(),
    download_cache_dir = tools::R_user_dir("dyngen", "data"),
    simulation_params = simulation_default(
      total_time = 1000,
      census_interval = 2, 
      ssa_algorithm = ssa_etl(tau = 300/3600),
      experiment_params = simulation_type_wild_type(num_simulations = 10)
    ),
    experiment_params = experiment_snapshot(
      realcount = realcount
    )
  )

out <- generate_dataset(config, make_plots = TRUE)

out$plot

Both datasets are stored in a list for easy usage by countsimQC.

datasets <- list(
  real = t(as.matrix(realcount)),
  dyngen = t(as.matrix(out$dataset$counts))
)

ddsList <- lapply(datasets, function(ds) {
  DESeq2::DESeqDataSetFromMatrix(
    countData = round(as.matrix(ds)), 
    colData = data.frame(sample = seq_len(ncol(ds))), 
    design = ~1
  )
})

Run countsimQC computations

Below are some computations countsimQC makes. Normally these are not visible to the user, but for the sake of transparency these are included in the vignette.

library(countsimQC)

## Define helper objects
nDatasets <- length(ddsList)
colRow <- c(2, 1)
panelSize <- 4
thm <- 
  theme_bw() + 
  theme(
    axis.text = element_text(size = 15),
    axis.title = element_text(size = 14),
    strip.text = element_text(size = 15)
  )

Compute key characteristics

obj <- countsimQC:::calculateDispersionsddsList(ddsList = ddsList, maxNForDisp = Inf)

sampleCorrDF <- countsimQC:::calculateSampleCorrs(ddsList = obj, maxNForCorr = 500)

featureCorrDF <- countsimQC:::calculateFeatureCorrs(ddsList = obj, maxNForCorr = 500)

Summarize sample characteristics

sampleDF <- map2_df(obj, names(obj), function(x, dataset_name) {
  tibble(
    dataset = dataset_name,
    Libsize = colSums(x$dge$counts),
    Fraczero = colMeans(x$dge$counts == 0),
    TMM = x$dge$samples$norm.factors,
    EffLibsize = Libsize * TMM
  )
})

Summarize feature characteristics

featureDF <- map2_df(obj, names(obj), function(x, dataset_name) {
  rd <- SummarizedExperiment::rowData(x$dds)
  tibble(
    dataset = dataset_name,
    Tagwise = sqrt(x$dge$tagwise.dispersion),
    Common = sqrt(x$dge$common.dispersion),
    Trend = sqrt(x$dge$trended.dispersion),
    AveLogCPM = x$dge$AveLogCPM,
    AveLogCPMDisp = x$dge$AveLogCPMDisp, 
    average_log2_cpm = apply(edgeR::cpm(x$dge, prior.count = 2, log = TRUE), 1, mean), 
    variance_log2_cpm = apply(edgeR::cpm(x$dge, prior.count = 2, log = TRUE), 1, var),
    Fraczero = rowMeans(x$dge$counts == 0),
    dispGeneEst = rd$dispGeneEst,
    dispFit = rd$dispFit,
    dispFinal = rd$dispersion,
    baseMeanDisp = rd$baseMeanDisp,
    baseMean = rd$baseMean
  )
})

Summarize data set characteristics

datasetDF <- map2_df(obj, names(obj), function(x, dataset_name) {
  tibble(
    dataset = dataset_name,
    prior_df = paste0("prior.df = ", round(x$dge$prior.df, 2)),
    nVars = nrow(x$dge$counts),
    nSamples = ncol(x$dge$counts),
    AveLogCPMDisp = 0.8 * max(featureDF$AveLogCPMDisp),
    Tagwise = 0.9 * max(featureDF$Tagwise)
  )
})

Data set dimensions {.tabset .tabset-pills}

These bar plots show the number of samples (columns) and features (rows) in each data set.

Number of samples (columns)

ggplot(datasetDF, aes(x = dataset, y = nSamples, fill = dataset)) + 
  geom_bar(stat = "identity", alpha = 0.5) + 
  xlab("") + ylab("Number of samples (columns)") + 
  thm + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

Number of features (rows)

ggplot(datasetDF, aes(x = dataset, y = nVars, fill = dataset)) + 
  geom_bar(stat = "identity", alpha = 0.5) + 
  xlab("") + ylab("Number of features (rows)") + 
  thm + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

Dispersion/BCV plots {.tabset .tabset-pills}

Disperson/BCV plots show the association between the average abundance and the dispersion or "biological coefficient of variation" (sqrt(dispersion)), as calculated by edgeR [@Robinson2010edgeR] and DESeq2 [@Love2014DESeq2]. In the edgeR plot, the estimate of the prior degrees of freedom is indicated.

edgeR

The black dots represent the tagwise dispersion estimates, the red line the common dispersion and the blue curve represents the trended dispersion estimates. For further information about the dispersion estimation in edgeR, see @Chen2014Dispersion.

ggplot(featureDF %>% dplyr::arrange(AveLogCPMDisp), 
       aes(x = AveLogCPMDisp, y = Tagwise)) + 
  geom_point(size = 0.25, alpha = 0.5) + 
  facet_wrap(~dataset, nrow = colRow[2]) + 
  geom_line(aes(y = Trend), color = "blue", size = 1.5) + 
  geom_line(aes(y = Common), color = "red", size = 1.5) +
  geom_text(data = datasetDF, aes(label = prior_df)) + 
  xlab("Average log CPM") + ylab("Biological coefficient of variation") + 
  thm

DESeq2

The black dots are the gene-wise dispersion estimates, the red curve the fitted mean-dispersion relationship and the blue circles represent the final dispersion estimates.For further information about the dispersion estimation in DESeq2, see @Love2014DESeq2.

ggplot(featureDF %>% dplyr::arrange(baseMeanDisp), 
       aes(x = baseMeanDisp, y = dispGeneEst)) + 
  geom_point(size = 0.25, alpha = 0.5) + 
  facet_wrap(~dataset, nrow = colRow[2]) + scale_x_log10() + scale_y_log10() +  
  geom_point(aes(y = dispFinal), color = "lightblue", shape = 21) + 
  geom_line(aes(y = dispFit), color = "red", size = 1.5) + 
  xlab("Base mean") + ylab("Dispersion") + 
  thm

Mean-variance plots {.tabset .tabset-pills}

This scatter plot shows the relation between the empirical mean and variance of the features. The difference between these mean-variance plots and the mean-dispersion plots above is that the plots in this section do not take the information about the experimental design and sample grouping into account, but simply display the mean and variance of log2(CPM) estimates across all samples, calculated using the cpm function from edgeR [@Robinson2010edgeR], with a prior count of 2.

ggplot(featureDF, aes(x = average_log2_cpm, y = variance_log2_cpm)) + 
  geom_point(size = 0.75, alpha = 0.5) + 
  facet_wrap(~dataset, nrow = colRow[2]) + 
  xlab("Mean of log2(CPM)") + ylab("Variance of log2(CPM)") + 
  thm

Library sizes {.tabset .tabset-pills}

This plot shows a histogram of the total read count per sample, i.e., the column sums of the respective data matrices.

ggplot(sampleDF, aes(x = Libsize)) + geom_histogram(bins = 30) + 
  facet_wrap(~dataset, nrow = colRow[2]) +
  xlab("Library size") + thm

TMM normalization factors {.tabset .tabset-pills}

This plot shows a histogram of the TMM normalization factors [@Robinson2010TMM], intended to adjust for differences in RNA composition, as calculated by edgeR [@Robinson2010edgeR].

ggplot(sampleDF, aes(x = TMM)) + geom_histogram(bins = 30) + 
  facet_wrap(~dataset, nrow = colRow[2]) +
  xlab("TMM normalization factor") + thm

Effective library sizes {.tabset .tabset-pills}

This plot shows a histogram of the "effective library sizes", defined as the total count per sample multiplied by the corresponding TMM normalization factor.

ggplot(sampleDF, aes(x = EffLibsize)) + geom_histogram(bins = 30) + 
  facet_wrap(~dataset, nrow = colRow[2]) +
  xlab("Effective library size") + thm

Expression distributions (average log CPM) {.tabset .tabset-pills}

This plot shows the distribution of average abundance values for the features. The abundances are log CPM values calculated by edgeR.

ggplot(featureDF, aes(x = AveLogCPM)) + geom_histogram(bins = 30) + 
  facet_wrap(~dataset, nrow = colRow[2]) +
  xlab("Average log CPM") + thm

Fraction zeros per sample {.tabset .tabset-pills}

This plot shows the distribution of the fraction of zeros observed per sample (column) in the count matrices.

ggplot(sampleDF, aes(x = Fraczero)) + geom_histogram(bins = 30) + 
  facet_wrap(~dataset, nrow = colRow[2]) +
  xlab("Fraction zeros per sample") + thm

Fraction zeros per feature {.tabset .tabset-pills}

This plot illustrates the distribution of the fraction of zeros observed per feature (row) in the count matrices.

ggplot(featureDF, aes(x = Fraczero)) + geom_histogram(bins = 30) + 
  facet_wrap(~dataset, nrow = colRow[2]) +
  xlab("Fraction zeros per feature") + thm

Sample-sample correlations {.tabset .tabset-pills}

The plot below shows the distribution of Spearman correlation coefficients for pairs of samples, calculated from the log(CPM) values obtained via the cpm function from edgeR, with a prior.count of 2.

ggplot(sampleCorrDF, aes(x = Correlation)) + geom_histogram(bins = 30) + 
  facet_wrap(~dataset, nrow = colRow[2]) +
  xlab("Sample-sample correlation") + thm

Feature-feature correlations {.tabset .tabset-pills}

This plot illustrates the distribution of Spearman correlation coefficients for pairs of features, calculated from the log(CPM) values obtained via the cpm function from edgeR, with a prior.count of 2. Only non-constant features are considered.

ggplot(featureCorrDF, aes(x = Correlation)) + geom_histogram(bins = 30) + 
  facet_wrap(~dataset, nrow = colRow[2]) +
  xlab("Feature-feature correlation") + thm

Library size vs fraction zeros {.tabset .tabset-pills}

This scatter plot shows the association between the total count (column sums) and the fraction of zeros observed per sample.

ggplot(sampleDF, aes(x = Libsize, y = Fraczero)) + 
  geom_point(size = 1, alpha = 0.5) + 
  facet_wrap(~dataset, nrow = colRow[2]) + 
  xlab("Library size") + ylab("Fraction zeros") + thm

Mean expression vs fraction zeros {.tabset .tabset-pills}

This scatter plot shows the association between the average abundance and the fraction of zeros observed per feature. The abundance is defined as the log(CPM) values as calculated by edgeR.

ggplot(featureDF, aes(x = AveLogCPM, y = Fraczero)) + 
  geom_point(size = 0.75, alpha = 0.5) + 
  facet_wrap(~dataset, nrow = colRow[2]) + 
  xlab("Average log CPM") + ylab("Fraction zeros") + thm

References

dynverse/dyngen documentation built on Oct. 29, 2022, 10:12 a.m.