Description Usage Arguments Details Value Examples
Calculate tests for differential abundance of cell populations using method 'diffcytDAGLMM'
1 2 3 4 5 6 7 8 9  testDA_GLMM(
d_counts,
formula,
contrast,
min_cells = 3,
min_samples = NULL,
normalize = FALSE,
norm_factors = "TMM"
)

d_counts 

formula 
Model formula object, created with 
contrast 
Contrast matrix, created with 
min_cells 
Filtering parameter. Default = 3. Clusters are kept for differential
testing if they have at least 
min_samples 
Filtering parameter. Default = 
normalize 
Whether to include optional normalization factors to adjust for composition effects (see details). Default = FALSE. 
norm_factors 
Normalization factors to use, if 
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 highresolution 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 highresolution 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 highdimensional cytometry count data. The
samplelevel random intercept terms are known as 'observationlevel 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 nondifferential 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 Mvalues' (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).
Returns a new SummarizedExperiment
object, with differential test
results stored in the rowData
slot. Results include raw pvalues
(p_val
) and adjusted pvalues (p_adj
), which can be used to rank
clusters by evidence for differential abundance. The results can be accessed with the
rowData
accessor function.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59  # 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)

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