Statistical methods for differential discovery analyses in high-dimensional cytometry data.
| Input projection |
. |
y-axis |
numeric, measurement value |
x-axis |
factor, sample IDs |
row |
factor, channel / marker ID / name |
column |
factor, cluster IDs |
colors |
factor, group IDs (fixed effect) |
labels |
factor, optional, patient / batch IDs (random effect) |
| Input parameters |
. |
method |
statistical method to be used (any of DA_edgeR or DA_GLMM for Differential Abundance, or DS_limma or DS_LMM for Differential State) |
reference.index |
Index of the reference category to be used. Default is 1, meaning that the first color specified in the crosstab will be used as a reference. In case more than 2 colors are present in the data, each of them will be compared to the reference group. |
| Output relations |
. |
logFC |
log fold change |
logCPM |
log of counts per million |
LR |
likelihood ratio |
p_val |
p-value |
p_adj |
adjusted p-value |
group_1 |
First group in the comparison |
group_2 |
Second group in the comparison |
diffcyt Bioconductor package
Weber, L.M., Nowicka, M., Soneson, C. et al. diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering. Commun Biol 2, 183 (2019).
Publication