findICFSpot

Identifies cell-to-cell Interaction Changed Features (ICF) for spots, i.e. features expression residual that are different due to proximity to other cell types. Works using results from celltype deconvolution methods such as those from runDWLSDeconv().

findICFSpot(
  gobject,
  spat_unit = NULL,
  feat_type = NULL,
  expression_values = c("normalized", "scaled", "custom"),
  ave_celltype_exp,
  selected_features = NULL,
  spatial_network_name = "Delaunay_network",
  deconv_name = "DWLS",
  minimum_unique_cells = 5,
  minimum_unique_int_cells = 5,
  CCI_cell_score = 0.1,
  dwls_cutoff = 0.001,
  diff_test = "permutation",
  nr_permutations = 100,
  adjust_method = "fdr",
  do_parallel = TRUE,
  cores = NA,
  set_seed = TRUE,
  seed_number = 1234,
  verbose = FALSE
)

Arguments

gobject

A giotto object

spat_unit

spatial unit (e.g. 'cell')

feat_type

feature type (e.g. 'rna')

expression_values

expression values to use

ave_celltype_exp

matrix or data.frame. Average feature expression in each cell type. Colnames should be the cell type and rownames are feat names.

selected_features

subset of selected features (optional)

spatial_network_name

name of spatial network to use

deconv_name

name of deconvolution/spatial enrichment values to use

minimum_unique_cells

minimum number of target cells required

minimum_unique_int_cells

minimum number of interacting cells required

CCI_cell_score

cell proximity score to filter no interacted cell

dwls_cutoff

cell type proportion cutoff to label the cell

diff_test

which differential expression test

nr_permutations

number of permutations if diff_test = permutation

adjust_method

which method to adjust p-values

do_parallel

run calculations in parallel with mclapply

cores

number of cores to use if do_parallel = TRUE

set_seed

set a seed for reproducibility

seed_number

seed number

verbose

be verbose

Value

icfObject that contains the differential feat scores

Details

Function to calculate if features expression residual are differentially expressed in cell types when they interact (approximated by physical proximity) with other cell types. Feature expression residual calculated as: (observed expression in spot - cell_type_proportion * average_expressed_in_cell_type) The results data.table in the icfObject contains - at least - the following columns:

• features: All or selected list of tested features

• sel: average feature expression residual in the interacting cells from the target cell type

• other: average feature expression residual in the NOT-interacting cells from the target cell type

• pcc_sel: correlation between cell proximity score and expression residual in the interacting cells from the target cell type

• pcc_other: correlation between cell proximity score and expression residual in the NOT-interacting cells from the target cell type

• pcc_diff: correlation difference between sel and other

• p.value: associated p-value

• p.adj: adjusted p-value

• cell_type: target cell type

• int_cell_type: interacting cell type

• nr_select: number of cells for selected target cell type

• int_nr_select: number of cells for interacting cell type

• unif_int: cell-cell interaction

See also

findInteractionChangedFeats()

Examples

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"
x <- findMarkers_one_vs_all(g,
    cluster_column = "leiden_clus", min_feats = 20
)
#> 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
sign_gene <- x$feats

sign_matrix <- matrix(rnorm(length(sign_gene) * 7, mean = 10),
    nrow = length(sign_gene)
)
rownames(sign_matrix) <- sign_gene
colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))

g <- runDWLSDeconv(gobject = g, sign_matrix = sign_matrix)
#> Error: package 'quadprog' is not yet installed
#> 
#>  To install:
#> install.packages(c("quadprog"))
ave_celltype_exp <- calculateMetaTable(g, metadata_cols = "leiden_clus")
ave_celltype_exp <- data.table::dcast(
    ave_celltype_exp, variable ~ leiden_clus
)
ave_celltype_exp <- as.matrix(ave_celltype_exp, rownames = "variable")
colnames(ave_celltype_exp) <- colnames(sign_matrix)

res <- findICFSpot(g,
    spat_unit = "cell",
    feat_type = "rna",
    ave_celltype_exp = ave_celltype_exp,
    spatial_network_name = "spatial_network"
)
#> Error in featExpDWLS(gobject = gobject, spat_unit = spat_unit, feat_type = feat_type,     ave_celltype_exp = ave_celltype_exp): ncol(ave_celltype_exp) needs to be the same as
#>             ncol(dwls_values) - 1
force(res)
#> Error: object 'res' not found