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chore: formatting
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@jiajic
jiajic committed Feb 27, 2024
1 parent 46b0f03 commit 35f0857
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1 change: 1 addition & 0 deletions NEWS.md
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@
## Enhancements
* Use `mixedsort()` for unique clusters metadata info
* Remove unnecessary matrix densification and expose `seed` param in `doScrubletDetect()`
* Remove unnecessary matrix densification in `makeSignMatrixRank()`


# Giotto 4.0.3 (2024/02/20)
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170 changes: 87 additions & 83 deletions R/spatial_genes.R
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Original file line number Diff line number Diff line change
Expand Up @@ -498,14 +498,16 @@ NULL

#' @describeIn calculate_spatial_enrichment calculate using 'data.table' implementation
#' @keywords internal
.calc_spatial_enrichment_dt = function(bin_matrix,
spatial_network,
calc_hub = F,
hub_min_int = 3,
group_size = 'automatic',
do_fisher_test = TRUE,
adjust_method = 'fdr',
cores = NA) {
.calc_spatial_enrichment_dt <- function(
bin_matrix,
spatial_network,
calc_hub = FALSE,
hub_min_int = 3,
group_size = 'automatic',
do_fisher_test = TRUE,
adjust_method = 'fdr',
cores = NA
) {


# set number of cores automatically, but with limit of 10
Expand All @@ -519,12 +521,12 @@ NULL
spat_netw_min = spatial_network[,.(from, to)]

# divide matrix in groups
if(!is.na(group_size) & is.numeric(group_size)) {
if (!is.na(group_size) & is.numeric(group_size)) {
group_size = group_size
if(group_size > nrow(bin_matrix)) {
stop('group_size is too big, it can not be greater than the number of feats')
}
} else if(group_size == 'automatic') {
} else if (group_size == 'automatic') {

test_number = ceiling(nrow(bin_matrix)/10)
test_number = max(2, test_number)
Expand All @@ -533,23 +535,23 @@ NULL

groups = ceiling(nrow(bin_matrix)/group_size)
cut_groups = cut(1:nrow(bin_matrix), breaks = groups, labels = 1:groups)
if(any(table(cut_groups) == 1)) {
if (any(table(cut_groups) == 1)) {
stop('With group size = ', group_size, ' you have a single gene in a group. Manually pick another group size')
}
indexes = 1:nrow(bin_matrix)
names(indexes) = cut_groups


total_list = list()
for(group in unique(cut_groups)) {
for (group in unique(cut_groups)) {

sel_indices = indexes[names(indexes) == group]

bin_matrix_DT = data.table::as.data.table(bin_matrix[sel_indices,])
bin_matrix_DT[, feat_ID := rownames(bin_matrix[sel_indices,])]
bin_matrix_DTm = data.table::melt.data.table(bin_matrix_DT, id.vars = 'feat_ID')

if(do_fisher_test == TRUE) {
if (do_fisher_test == TRUE) {
test = .spat_fish_func_dt(bin_matrix_DTm = bin_matrix_DTm,
spat_netw_min = spat_netw_min,
calc_hub = calc_hub,
Expand All @@ -570,7 +572,7 @@ NULL

result = do.call('rbind', total_list)

if(do_fisher_test == TRUE) {
if (do_fisher_test == TRUE) {
min_pvalue = min(result$p.value[result$p.value > 0])
result[, p.value := ifelse(p.value == 0, min_pvalue, p.value)]
result[, adj.p.value := stats::p.adjust(p.value, method = adjust_method)]
Expand Down Expand Up @@ -680,8 +682,13 @@ binSpectSingleMatrix = function(expression_matrix,
set.seed <- NULL
}

do_parallel <- as.logical(do_parallel)
calc_hub <- as.logical(calc_hub)
get_av_expr <- as.logical(get_av_expr)
get_high_expr <- as.logical(get_high_expr)
do_fisher_test <- as.logical(do_fisher_test)

if(verbose == TRUE) cat('\n This is the single parameter version of binSpect')
vmsg(.v = verbose, '\n This is the single parameter version of binSpect')


# set number of cores automatically, but with limit of 10
Expand All @@ -703,97 +710,94 @@ binSpectSingleMatrix = function(expression_matrix,

# spatial network
# TODO: verify binarization of spatial network
if(is.null(spatial_network)) {
if (is.null(spatial_network)) {
stop("You need to provide a spatial network in data.table format to the 'spatial_network' parameter \n")
}


## start binarization ##
## ------------------ ##

if(!is.null(bin_matrix)) {
if (!is.null(bin_matrix)) {
# TODO: verify format of bin_matrix and compatibility with spatial network
bin_matrix = bin_matrix
} else {

if(bin_method == 'kmeans') {

bin_matrix = kmeans_binarize_wrapper(expr_values = expression_matrix,
subset_feats = subset_feats,
kmeans_algo = kmeans_algo,
nstart = nstart,
iter_max = iter_max,
extreme_nr = extreme_nr,
sample_nr = sample_nr,
# set.seed = set.seed,
seed = seed)

} else if(bin_method == 'rank') {

bin_matrix = rank_binarize_wrapper(expr_values = expression_matrix,
subset_feats = subset_feats,
percentage_rank = percentage_rank)
}
bin_matrix <- switch(bin_method,
"kmeans" = kmeans_binarize_wrapper(
expr_values = expression_matrix,
subset_feats = subset_feats,
kmeans_algo = kmeans_algo,
nstart = nstart,
iter_max = iter_max,
extreme_nr = extreme_nr,
sample_nr = sample_nr,
# set.seed = set.seed,
seed = seed
),
"rank" = rank_binarize_wrapper(
expr_values = expression_matrix,
subset_feats = subset_feats,
percentage_rank = percentage_rank
)
)
}

if(verbose == TRUE) cat('\n 1. matrix binarization complete \n')
vmsg(.v = verbose, '\n 1. matrix binarization complete \n')

## start with enrichment ##
## --------------------- ##

if(implementation == 'simple') {
if(do_parallel == TRUE) {
warning('Parallel not yet implemented for simple. Enrichment will default to serial.')
}

if(calc_hub == TRUE) {
warning('Hub calculation is not possible with the simple implementation, change to matrix if requird.')
}


result = .calc_spatial_enrichment_minimum(spatial_network = spatial_network,
bin_matrix = bin_matrix,
adjust_method = adjust_method,
do_fisher_test = do_fisher_test)


} else if(implementation == 'matrix') {

result = .calc_spatial_enrichment_matrix(spatial_network = spatial_network,
bin_matrix = bin_matrix,
adjust_method = adjust_method,
do_fisher_test = do_fisher_test,
do_parallel = do_parallel,
cores = cores,
calc_hub = calc_hub,
hub_min_int = hub_min_int,
verbose = verbose)

} else if(implementation == 'data.table') {

result = .calc_spatial_enrichment_dt(bin_matrix = bin_matrix,
spatial_network = spatial_network,
calc_hub = calc_hub,
hub_min_int = hub_min_int,
group_size = group_size,
do_fisher_test = do_fisher_test,
adjust_method = adjust_method,
cores = cores)
}

if(verbose == TRUE) cat('\n 2. spatial enrichment test completed \n')
result <- switch(implementation,
"simple" = {
if(do_parallel) {
warning('Parallel not yet implemented for simple. Enrichment will default to serial.')
}
if(calc_hub) {
warning('Hub calculation is not possible with the simple implementation, change to matrix if required.')
}

.calc_spatial_enrichment_minimum(
spatial_network = spatial_network,
bin_matrix = bin_matrix,
adjust_method = adjust_method,
do_fisher_test = do_fisher_test
)
},
"matrix" = .calc_spatial_enrichment_matrix(
spatial_network = spatial_network,
bin_matrix = bin_matrix,
adjust_method = adjust_method,
do_fisher_test = do_fisher_test,
do_parallel = do_parallel,
cores = cores,
calc_hub = calc_hub,
hub_min_int = hub_min_int,
verbose = verbose
),
"data.table" = .calc_spatial_enrichment_dt(
bin_matrix = bin_matrix,
spatial_network = spatial_network,
calc_hub = calc_hub,
hub_min_int = hub_min_int,
group_size = group_size,
do_fisher_test = do_fisher_test,
adjust_method = adjust_method,
cores = cores
)
)

vmsg(.v = verbose, '\n 2. spatial enrichment test completed \n')



## start with average high expression ##
## ---------------------------------- ##

if(get_av_expr == TRUE) {
if (get_av_expr) {

# expression
if(!is.null(subset_feats)) {
if (!is.null(subset_feats)) {
expr_values = expression_matrix[rownames(expression_matrix) %in% subset_feats, ]
} else {
expr_values = expression_matrix
Expand All @@ -806,25 +810,25 @@ binSpectSingleMatrix = function(expression_matrix,
av_expr_DT = data.table::data.table(feats = names(av_expr), av_expr = av_expr)
result = merge(result, av_expr_DT, by = 'feats')

if(verbose == TRUE) cat('\n 3. (optional) average expression of high expressing cells calculated \n')
vmsg(.v = verbose, '\n 3. (optional) average expression of high expressing cells calculated \n')
}



## start with number of high expressing cells ##
## ------------------------------------------ ##

if(get_high_expr == TRUE) {
if (get_high_expr) {
high_expr = rowSums(bin_matrix)
high_expr_DT = data.table::data.table(feats = names(high_expr), high_expr = high_expr)
result = merge(result, high_expr_DT, by = 'feats')

if(verbose == TRUE) cat('\n 4. (optional) number of high expressing cells calculated \n')
vmsg(.v = verbose, '\n 4. (optional) number of high expressing cells calculated \n')
}


# sort
if(do_fisher_test == TRUE) {
if (do_fisher_test) {
data.table::setorder(result, -score)
} else {
data.table::setorder(result, -estimate)
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