Identify marker feats for selected clusters based on the MAST package.
findMastMarkers(
gobject,
feat_type = NULL,
spat_unit = NULL,
expression_values = c("normalized", "scaled", "custom"),
cluster_column,
group_1 = NULL,
group_1_name = NULL,
group_2 = NULL,
group_2_name = NULL,
adjust_columns = NULL,
verbose = FALSE,
...
)
giotto object
feature type
spatial unit
feat expression values to use
clusters to use
group 1 cluster IDs from cluster_column for pairwise comparison
custom name for group_1 clusters
group 2 cluster IDs from cluster_column for pairwise comparison
custom name for group_2 clusters
column in pDataDT to adjust for (e.g. detection rate)
be verbose
additional parameters for the zlm function in MAST
data.table with marker feats
This is a minimal convenience wrapper around the
zlm
from the MAST package to detect differentially expressed feats. Caution:
with large datasets
MAST might take a long time to run and finish
g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> 3.2 read Giotto spatial centroid information
#> 3.3 read Giotto spatial overlap information
#> 4. read Giotto image information
#> python already initialized in this session
#> active environment : '/usr/bin/python3'
#> python version : 3.10
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#> "/usr/bin/python3"
findMastMarkers(
gobject = g, cluster_column = "leiden_clus", group_1 = 1,
group_2 = 2
)
#> Error: package 'MAST' is not yet installed
#>
#> To install:
#> if(!requireNamespace('BiocManager', quietly = TRUE)) install.packages('BiocManager');
#> BiocManager::install(c("MAST"))