Identify marker feats for all clusters in a one vs all manner based on scran's implementation of findMarkers.
findScranMarkers_one_vs_all(
gobject,
spat_unit = NULL,
feat_type = NULL,
expression_values = c("normalized", "scaled", "custom"),
cluster_column,
subset_clusters = NULL,
pval = 0.01,
logFC = 0.5,
min_feats = 10,
min_genes = NULL,
verbose = TRUE,
...
)giotto object
spatial unit
feature type
feat expression values to use
clusters to use
subset of clusters to use
filter on minimal p-value
filter on logFC
minimum feats to keep per cluster, overrides pval and logFC
deprecated, use min_feats
be verbose
additional parameters for the findMarkers function in scran
data.table with marker feats
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
findScranMarkers_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