Description Usage Arguments Details Value Examples
Calculate tests for differential states within cell populations using method 'diffcytDSLMM'
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d_counts 

d_medians 

formula 
Model formula object, created with 
contrast 
Contrast matrix, created with 
weights 
(Optional) Whether to include precision weights within each model (across samples, i.e. within the model for each cluster); these represent the relative uncertainty in calculating each median value (within each model). Accepts values of TRUE, FALSE, or a numeric vector of custom weights. Default = TRUE, in which case cluster cell counts are used as weights. 
markers_to_test 
(Optional) Logical vector specifying which markers to test for
differential expression (from the set of markers stored in the 
min_cells 
Filtering parameter. Default = 3. Clusters are kept for differential
testing if they have at least 
min_samples 
Filtering parameter. Default = 
Calculates tests for differential states within cell populations (i.e. differential expression of cell state markers within clusters), using linear mixed models (LMMs). Clusters are defined using cell type markers, and cell states are characterized by the median transformed expression of cell state markers.
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 linear mixed models (LMMs) for each clustermarker combination (cell state markers only), and calculates differential tests separately for each clustermarker combination. The response variable in each model is the median arcsinhtransformed marker expression of the cell state marker, which is assumed to follow a Gaussian distribution. There is one model per cluster per cell state marker. Within each model, samplelevel weights are included (by default) for the number of cells per sample; these weights represent the relative uncertainty in calculating each median value. (Additional uncertainty exists due to variation in the total number of cells per cluster; however, it is not possible to account for this, since there are separate models for each clustermarker combination.) 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 analysis of marker expression stratified by cell population'.)
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.
If no random intercept terms are included in the model formula, model fitting is performed using a linear model (LM) instead of a LMM.
The contrast matrix specifying the contrast of interest can be created with
createContrast
. See createContrast
for more details.
By default, differential tests are performed for all cell state markers (which are
identified with the vector id_state_markers
stored in the metadata of the
cluster medians input object). The optional argument markers_to_test
allows the
user to specify a different set of markers to test (e.g. to investigate differences for
cell type markers).
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.
Weights: By default, cluster cell counts are used as precision weights within each model (across samples only, i.e. within the model for each cluster); these represent the relative uncertainty in calculating each median value (within each model). See above for details.
Returns a new SummarizedExperiment
object, where rows =
clustermarker combinations, and columns = samples. In the rows, clusters are
repeated for each cell state marker (i.e. the sheets or assays
from the
previous d_medians
object are stacked into a single matrix). Differential test
results are stored in the rowData
slot. Results include raw pvalues
(p_val
) and adjusted pvalues (p_adj
), which can be used to rank
clustermarker combinations by evidence for differential states within cell
populations. The results can be accessed with the rowData
accessor
function.
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# 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 states (DS) signal
ix_DS < 901:1000
ix_cols_type < 1:10
ix_cols_DS < 19:20
d_input[[1]][ix_DS, ix_cols_type] < d_random(n = 1000, mean = 3, ncol = 10)
d_input[[2]][ix_DS, ix_cols_type] < d_random(n = 1000, mean = 3, ncol = 10)
d_input[[3]][ix_DS, c(ix_cols_type, ix_cols_DS)] < d_random(n = 1200, mean = 3, ncol = 12)
d_input[[4]][ix_DS, c(ix_cols_type, ix_cols_DS)] < d_random(n = 1200, mean = 3, ncol = 12)
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)
# Calculate medians
d_medians < calcMedians(d_se)
# Create model formula
formula < createFormula(experiment_info, cols_fixed = "group_id")
# Create contrast matrix
contrast < createContrast(c(0, 1))
# Test for differential states (DS) within clusters
res_DS < testDS_LMM(d_counts, d_medians, formula, contrast)

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