Differential Abundance Analysis (DAA) with t-tests
Source:R/differential_discovery.R
tof_analyze_abundance_ttest.RdThis function performs differential abundance analysis on the cell clusters contained within a `tof_tbl` using simple t-tests. Users specify which columns represent sample, cluster, and effect information, and either a paired or unpaired t-test (one per cluster) is used to detect significant differences between sample types.
Usage
tof_analyze_abundance_ttest(
tof_tibble,
cluster_col,
effect_col,
group_cols,
test_type = c("unpaired", "paired"),
min_cells = 3,
min_samples = 5,
alpha = 0.05,
quiet = FALSE
)Arguments
- tof_tibble
A `tof_tbl` or a `tibble`.
- 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.
- effect_col
Unquoted column name representing which column in `tof_tibble` should be used to break samples into groups for the t-test. Should only have 2 unique values.
- group_cols
Unquoted names of the columns other than `effect_col` that should be used to group cells into independent observations. Fills a similar role to `sample_col` in other `tof_analyze_abundance_*` functions. For example, if an experiment involves analyzing samples taken from multiple patients at two timepoints (with `effect_col = timepoint`), then group_cols should be the name of the column representing patient IDs.
- test_type
A string indicating whether the t-test should be "unpaired" (the default) or "paired".
- min_cells
An integer value used to filter clusters out of the differential abundance analysis. Clusters are not included in the differential abundance 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 abundance analysis. Clusters are not included in the differential abundance 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.
- quiet
A boolean value indicating whether warnings should be printed. Defaults to `TRUE`.
Value
A tibble with 7 columns:
- {cluster_col}
The name/ID of the cluster being tested. Each entry in this column will match a unique value in the input {cluster_col}.
- t
The t-statistic computed for each cluster.
- df
The degrees of freedom used for the t-test for each cluster.
- p_val
The (unadjusted) p-value for the t-test for each cluster.
- p_adj
The
p.adjust-adjusted p-value for the t-test for each cluster.- significant
A character vector that will be "*" for clusters for which p_adj < alpha and "" otherwise.
- mean_diff
For an unpaired t-test, the difference between the average proportions of each cluster in the two levels of `effect_col`. For a paired t-test, the average difference between the proportions of each cluster in the two levels of `effect_col` within a given patient.
- mean_fc
For an unpaired t-test, the ratio between the average proportions of each cluster in the two levels of `effect_col`. For a paired t-test, the average ratio between the proportions of each cluster in the two levels of `effect_col` within a given patient. 0.001 is added to the denominator of the ratio to avoid divide-by-zero errors.
The "levels" attribute of the result indicates the order in which the different levels of the `effect_col` were considered. The `mean_diff` value for each row of the output is computed by subtracting the second level from the first level, and the `mean_fc` value for each row is computed by dividing the first level by the second level.
See also
Other differential abundance analysis functions:
tof_analyze_abundance(),
tof_analyze_abundance_diffcyt(),
tof_analyze_abundance_glmm()