Differential Expression Analysis (DEA) with diffcyt

This function performs differential expression analysis on the cell clusters contained within a `tof_tbl` using one of two methods implemented in the diffcyt package for differential discovery analysis in high-dimensional cytometry data.

Usage

tof_analyze_expression_diffcyt(
  tof_tibble,
  sample_col,
  cluster_col,
  marker_cols = where(tof_is_numeric),
  fixed_effect_cols,
  random_effect_cols,
  diffcyt_method = c("lmm", "limma"),
  include_observation_level_random_effects = FALSE,
  min_cells = 3,
  min_samples = 5,
  alpha = 0.05,
  ...
)

Arguments

tof_tibble

A `tof_tbl` or a `tibble`.

sample_col

An unquoted column name indicating which column in `tof_tibble` represents the id of the sample from which each cell was collected. `sample_col` should serve as a unique identifier for each sample collected during data acquisition - all cells with the same value for `sample_col` will be treated as a part of the same observational unit.

cluster_col

An unquoted column name indicating which column in `tof_tibble` stores the cluster ids of the cluster to which each cell belongs. Cluster labels can be produced via any method the user chooses - including manual gating, any of the functions in the `tof_cluster_*` function family, or any other method.

marker_cols

Unquoted column names representing which columns in `tof_tibble` (i.e. which high-dimensional cytometry protein measurements) should be tested for differential expression between levels of the `fixed_effect_cols`. Defaults to all numeric (integer or double) columns. Supports tidyselect helpers.

fixed_effect_cols

Unquoted column names representing which columns in `tof_tibble` should be used to model fixed effects during the differential expression analysis. Generally speaking, fixed effects represent the comparisons of biological interest (often the the variables manipulated during experiments), such as treated vs. non-treated, before-treatment vs. after-treatment, or healthy vs. non-healthy.

random_effect_cols

Unquoted column names representing which columns in `tof_tibble` should be used to model random effects during the differential expression analysis. Generally speaking, random effects represent variables that a researcher wants to control/account for, but that are not necessarily of biological interest. Example random effect variables might include batch id, patient id (in a paired design), or patient age.

Note that without many samples at each level of each of the random effect variables, it can be easy to overfit mixed models. For most high-dimensional cytometry experiments, 2 or fewer (and often 0) random effect variables are appropriate.

diffcyt_method

A string indicating which diffcyt method should be used for the differential expression analysis. Valid methods include "lmm" (the default) and "limma".

include_observation_level_random_effects

A boolean value indicating if "observation-level random effects" (OLREs) should be included as random effect terms in a "lmm" differential expression model. For details about what OLREs are, see the diffcyt paper. Defaults to FALSE.

min_cells

An integer value used to filter clusters out of the differential expression analysis. Clusters are not included in the differential expression testing if they do not have at least `min_cells` in at least `min_samples` samples. Defaults to 3.

min_samples

An integer value used to filter clusters out of the differential expression analysis. Clusters are not included in the differential expression testing if they do not have at least `min_cells` in at least `min_samples` samples. Defaults to 5.

alpha

A numeric value between 0 and 1 indicating which significance level should be applied to multiple-comparison adjusted p-values during the differential abundance analysis. Defaults to 0.05.

...

Optional additional arguments to pass to the under-the-hood diffcyt function being used to perform the differential expression analysis. See testDS_LMM and testDS_limma for details.

Value

A nested tibble with two columns: `tested_effect` and `dea_results`.

The first column, `tested_effect` is a character vector indicating which term in the differential expression model was used for significance testing. The values in this row are obtained by pasting together the column names for each fixed effect variable and each of its values. For example, a fixed effect column named fixed_effect with levels "a", "b", and "c" have two terms in `tested_effect`: "fixed_effectb" and "fixed_effectc" (note that level "a" of fixed_effect is set as the reference level during dummy coding). These values correspond to the terms in the differential expression model that represent the difference in cluster median expression values of each marker between samples with fixed_effect = "b" and fixed_effect = "a" and between samples with fixed_effect = "c" and fixed_effect = "a", respectively. In addition, note that the first row in `tested_effect` will always represent the "omnibus" test, or the test that there are significant differences between any levels of any fixed effect variable in the model.

The second column, `dea_results` is a list of tibbles in which each entry gives the differential expression results for each tested_effect. Within each entry of `dea_results`, you will find `p_val`, the p-value associated with each tested effect in each input cluster/marker pair; `p_adj`, the multiple-comparison adjusted p-value (using the p.adjust function), and other values associated with the underlying method used to perform the differential expression analysis (such as the log-fold change of clusters' median marker expression values between the conditions being compared). Each tibble in `dea_results` will also have two columns representing the cluster and marker corresponding to the p-value in each row.

Details

The two methods are based on linear mixed models ("lmm") and limma ("limma"). Both the "lmm" and "limma" methods can model both fixed effects and random effects.

See also

Other differential expression analysis functions: tof_analyze_expression(), tof_analyze_expression_lmm(), tof_analyze_expression_ttest()

Examples

# For differential discovery examples, please see the package vignettes
NULL
#> NULL