testDA_GLMM: Test for differential abundance: method 'diffcyt-DA-GLMM'

In diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering

Description

Calculate tests for differential abundance of cell populations using method 'diffcyt-DA-GLMM'

Usage

testDA_GLMM(
  d_counts,
  formula,
  contrast,
  min_cells = 3,
  min_samples = NULL,
  normalize = FALSE,
  norm_factors = "TMM"
)

Arguments

d_counts	SummarizedExperiment object containing cluster cell counts, from calcCounts.
formula	Model formula object, created with createFormula. This should be a list containing three elements: formula, data, and random_terms: the model formula, data frame of corresponding variables, and variable indicating whether the model formula contains any random effect terms. See createFormula for details.
contrast	Contrast matrix, created with createContrast. See createContrast for details.
min_cells	Filtering parameter. Default = 3. Clusters are kept for differential testing if they have at least min_cells cells in at least min_samples samples.
min_samples	Filtering parameter. Default = number of samples / 2, which is appropriate for two-group comparisons (of equal size). Clusters are kept for differential testing if they have at least min_cells cells in at least min_samples samples.
normalize	Whether to include optional normalization factors to adjust for composition effects (see details). Default = FALSE.
norm_factors	Normalization factors to use, if normalize = TRUE. Default = "TMM", in which case normalization factors are calculated automatically using the 'trimmed mean of M-values' (TMM) method from the edgeR package. Alternatively, a vector of values can be provided (the values should multiply to 1).

Details

Calculates tests for differential abundance of clusters, using generalized linear mixed models (GLMMs).

This methodology was originally developed and described by Nowicka et al. (2017), F1000Research, and has been modified here to make use of high-resolution clustering to enable investigation of rare cell populations. Note that unlike the original method by Nowicka et al., we do not attempt to manually merge clusters into canonical cell populations. Instead, results are reported at the high-resolution cluster level, and the interpretation of significant differential clusters is left to the user via visualizations such as heatmaps (see the package vignette for an example).

This method fits generalized linear mixed models (GLMMs) for each cluster, and calculates differential tests separately for each cluster. The response variables in the models are the cluster cell counts, which are assumed to follow a binomial distribution. There is one model per cluster. We also include a filtering step to remove clusters with very small numbers of cells, to improve statistical power.

For more details on the statistical methodology, see Nowicka et al. (2017), F1000Research (section 'Differential cell population abundance'.)

The experimental design must be specified using a model formula, which can be created with createFormula. Flexible experimental designs are possible, including blocking (e.g. paired designs), batch effects, and continuous covariates. Blocking variables can be included as either random intercept terms or fixed effect terms (see createFormula). For paired designs, we recommend using random intercept terms to improve statistical power; see Nowicka et al. (2017), F1000Research for details. Batch effects and continuous covariates should be included as fixed effects. In addition, we include random intercept terms for each sample to account for overdispersion typically seen in high-dimensional cytometry count data. The sample-level random intercept terms are known as 'observation-level random effects' (OLREs); see Nowicka et al. (2017), F1000Research for more details.

The contrast matrix specifying the contrast of interest can be created with createContrast. See createContrast for more details.

Filtering: Clusters are kept for differential testing if they have at least min_cells cells in at least min_samples samples. This removes clusters with very low cell counts across conditions, to improve power.

Normalization: Optional normalization factors can be included to adjust for composition effects in the cluster cell counts per sample. For example, in an extreme case, if several additional clusters are present in only one condition, while all other clusters are approximately equally abundant between conditions, then simply normalizing by the total number of cells per sample will create a false positive differential abundance signal for the non-differential clusters. (For a detailed explanation in the context of RNA sequencing gene expression, see Robinson and Oshlack, 2010.) Normalization factors can be calculated automatically using the 'trimmed mean of M-values' (TMM) method (Robinson and Oshlack, 2010), implemented in the edgeR package (see also the edgeR User's Guide for details). Alternatively, a vector of values can be provided (the values should multiply to 1).

Value

Returns a new SummarizedExperiment object, with differential test results stored in the rowData slot. Results include raw p-values (p_val) and adjusted p-values (p_adj), which can be used to rank clusters by evidence for differential abundance. The results can be accessed with the rowData accessor function.

Examples

# For a complete workflow example demonstrating each step in the 'diffcyt' pipeline, 
# see the package vignette.

# Function to create random data (one sample)
d_random <- function(n = 20000, mean = 0, sd = 1, ncol = 20, cofactor = 5) {
  d <- sinh(matrix(rnorm(n, mean, sd), ncol = ncol)) * cofactor
  colnames(d) <- paste0("marker", sprintf("%02d", 1:ncol))
  d
}

# Create random data (without differential signal)
set.seed(123)
d_input <- list(
  sample1 = d_random(), 
  sample2 = d_random(), 
  sample3 = d_random(), 
  sample4 = d_random()
)

# Add differential abundance (DA) signal
ix_DA <- 801:900
ix_cols_type <- 1:10
d_input[[3]][ix_DA, ix_cols_type] <- d_random(n = 1000, mean = 2, ncol = 10)
d_input[[4]][ix_DA, ix_cols_type] <- d_random(n = 1000, mean = 2, ncol = 10)

experiment_info <- data.frame(
  sample_id = factor(paste0("sample", 1:4)), 
  group_id = factor(c("group1", "group1", "group2", "group2")), 
  stringsAsFactors = FALSE
)

marker_info <- data.frame(
  channel_name = paste0("channel", sprintf("%03d", 1:20)), 
  marker_name = paste0("marker", sprintf("%02d", 1:20)), 
  marker_class = factor(c(rep("type", 10), rep("state", 10)), 
                        levels = c("type", "state", "none")), 
  stringsAsFactors = FALSE
)

# Prepare data
d_se <- prepareData(d_input, experiment_info, marker_info)

# Transform data
d_se <- transformData(d_se)

# Generate clusters
d_se <- generateClusters(d_se)

# Calculate counts
d_counts <- calcCounts(d_se)

# Create model formula
formula <- createFormula(experiment_info, cols_fixed = "group_id", cols_random = "sample_id")

# Create contrast matrix
contrast <- createContrast(c(0, 1))

# Test for differential abundance (DA) of clusters
res_DA <- testDA_GLMM(d_counts, formula, contrast)

diffcyt documentation built on Nov. 8, 2020, 6:37 p.m.