Identify marker feats for all clusters in a one vs all manner.
findMarkers_one_vs_all(
gobject,
feat_type = NULL,
spat_unit = NULL,
expression_values = c("normalized", "scaled", "custom"),
cluster_column,
subset_clusters = NULL,
method = c("scran", "gini", "mast"),
pval = 0.01,
logFC = 0.5,
min_feats = 10,
min_genes = NULL,
min_expr_gini_score = 0.5,
min_det_gini_score = 0.5,
detection_threshold = 0,
rank_score = 1,
adjust_columns = NULL,
verbose = TRUE,
...
)giotto object
feature type
spatial unit
feat expression values to use
clusters to use
selection of clusters to compare
method to use to detect differentially expressed feats
scran & mast: filter on minimal p-value
scan & mast: filter on logFC
minimum feats to keep per cluster, overrides pval and logFC
deprecated, use min_feats
gini: filter on minimum gini coefficient for expression
gini: filter minimum gini coefficient for detection
gini: detection threshold for feat expression
gini: rank scores to include
mast: column in pDataDT to adjust for (e.g. detection rate)
be verbose
additional parameters for the findMarkers function in scran or zlm function in MAST
data.table with marker feats
Wrapper for all one vs all functions to detect marker feats for clusters.
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 : 3.12
findMarkers_one_vs_all(g, cluster_column = "leiden_clus")
#> using 'Scran' to detect marker feats. If used in published
#> research, please cite: Lun ATL, McCarthy DJ, Marioni JC (2016).
#> 'A step-by-step workflow for low-level analysis of single-cell RNA-seq
#> data with Bioconductor.'
#> F1000Res., 5, 2122. doi: 10.12688/f1000research.9501.2.
#> start with cluster 1start with cluster 2start with cluster 3start with cluster 4start with cluster 5start with cluster 6start with cluster 7
#> Top p.value FDR logFC feats cluster ranking
#> <int> <num> <num> <num> <char> <char> <num>
#> 1: 1 0.0003543270 0.1954678 0.4994571 Arpp19 1 3
#> 2: 2 0.0006166177 0.1954678 -0.1260320 Emilin1 1 531
#> 3: 3 0.0009440501 0.1995093 -0.3386237 Sod3 1 632
#> 4: 4 0.0029328557 0.4458839 0.5955190 Pde1a 1 1
#> 5: 5 0.0035164343 0.4458839 0.5293216 Ipcef1 1 2
#> ---
#> 188: 104 0.0965348270 0.5884912 1.1651781 Myrf 7 3
#> 189: 111 0.1128359409 0.6444864 0.9399060 Pbxip1 7 10
#> 190: 113 0.1251822332 0.7023499 1.2522299 Pou3f1 7 1
#> 191: 117 0.1336981269 0.7244839 0.9716501 Aspa 7 9
#> 192: 127 0.1795461454 0.8963170 1.0321280 Lmo1 7 8