Add selected results from doHMRF to the giotto object
addHMRF(
gobject,
spat_unit = NULL,
feat_type = NULL,
HMRFoutput,
k = NULL,
betas_to_add = NULL,
hmrf_name = NULL
)giotto object
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
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#> "/usr/bin/python3"
spat_genes <- binSpect(g)
#>
#> This is the single parameter version of binSpect
#>
#> 1. matrix binarization complete
#>
#> 2. spatial enrichment test completed
#>
#> 3. (optional) average expression of high
#> expressing cells calculated
#>
#> 4. (optional) number of high expressing cells
#> calculated
output_folder <- file.path(tempdir(), "HMRF")
if (!file.exists(output_folder)) dir.create(output_folder, recursive = TRUE)
out <- doHMRF(
g,
spatial_genes = spat_genes[seq_len(20)]$feats,
expression_values = "scaled",
spatial_network_name = "Delaunay_network",
k = 6, betas = c(0, 10, 5),
output_folder = output_folder
)
#> Error: package 'smfishHmrf' is not yet installed
#>
#> To install:
#> ## active python env: '/usr/bin/python3'
#> ## python version: 3.12
#> ## restart session then use GiottoClass::set_giotto_python_path() if this is incorrect
#> reticulate::conda_install(envname = '/usr/bin/python3', packages = c('smfishHmrf'), pip = TRUE)
g <- addHMRF(
gobject = g,
HMRFoutput = out,
k = 6,
betas_to_add = 20,
hmrf_name = "HMRF"
)
#> Error: object 'out' not found
spatPlot(
gobject = g, cell_color = "HMRF_k6_b.20",
)
#> Error: spatPlot2D()
#> HMRF_k6_b.20 is not a color or a column name