initHMRF_V2: initHMRF_V2
In RubD/Giotto: Spatial Single-Cell Transcriptomics Toolbox

initHMRF_V2

R Documentation

initHMRF_V2

Description

Run initialzation for HMRF model

Usage

initHMRF_V2(
  gobject,
  expression_values = c("scaled", "normalized", "custom"),
  spatial_network_name = "Delaunay_network",
  use_spatial_genes = c("binSpect", "silhouetteRank"),
  gene_samples = 500,
  gene_sampling_rate = 2,
  gene_sampling_seed = 10,
  gene_sampling_from_top = 2500,
  filter_method = c("none", "elbow"),
  user_gene_list = NULL,
  use_score = FALSE,
  hmrf_seed = 100,
  k = 10,
  tolerance = 1e-05,
  zscore = c("none", "rowcol", "colrow"),
  nstart = 1000,
  factor_step = 1.05
)

Arguments

`gobject`	giotto object
`expression_values`	expression values to use
`spatial_network_name`	name of spatial network to use for HMRF
`use_spatial_genes`	which of Giotto's spatial genes to use
`gene_samples`	number of spatial gene subset to use for HMRF
`gene_sampling_rate`	parameter (1-50) controlling proportion of gene samples from different module when sampling, 1 corresponding to equal gene samples between different modules; 50 corresponding to gene samples proportional to module size.
`gene_sampling_seed`	random number seed to sample spatial genes
`gene_sampling_from_top`	total spatial genes before sampling
`filter_method`	filter genes by top or by elbow method, prior to sampling
`user_gene_list`	user-specified genes (optional)
`use_score`	use score as gene selection criterion (applies when use_spatial_genes=silhouetteRank)
`hmrf_seed`	random number seed to generate initial mean vector of HMRF model
`k`	number of HMRF domains
`tolerance`	error tolerance threshold
`nstart`	number of Kmeans initializations from which to select the best initialization
`factor_step`	dampened factor step

Details

There are two steps to running HMRF. This is the first step, the initialization. First, user specify which of Giotto's spatial genes to run, through use_spatial_genes. Spatial genes have been stored in the gene metadata table. A first pass of genes will filter genes that are not significantly spatial, as determined by filter_method. If filter_method is none, then top 2500 (gene_sampling_from_top) genes ranked by pvalue are considered spatial. If filter_method is elbow, then the exact cutoff is determined by the elbow in the -log10Pvalue vs. gene rank plot. Second, the filtered gene set is subject to sampling to select 500 (controlled by gene_samples) genes for running HMRF. Third, once spatial genes are finalized, we are ready to initialize HMRF. This consists of running a K-means algorithm to determine initial centroids (nstart, hmrf_seed) of HMRF. The initialization is then finished. This function returns a list containing y (expression), nei (neighborhood structure), numnei (number of neighbors), blocks (graph colors), damp (dampened factor), mu (mean), sigma (covariance), k, genes, edgelist. This information is needed for the second step, doHMRF.