Reproduce downsample benchmark

Hi,

Thank you for the great work. I had an issue when reproducing the results in the downsampling benchmark and any insight would be highly appreciated!

Basically, I am trying to calculate the KNN graph for CPM transformation and compare it with the overlapped graph. Ideally, all edges in the overlapped should be in the CPM KNN graph but I missed 173 edges. I wonder whether I missed any part during the preprocessing or I missed anything else?

Thank you for your time!

```
dataset <- "mcSCRB"

logp_cpm_fnc <- function(UMI, sf, alpha){
colsums <- MatrixGenerics::colSums2(UMI)
log1p(t(t(UMI) / colsums * 1e6))
}

calculate_knn <- function(count_mtx, pca_dim=50, k_nearest_neighbors=50){
red_dat <- BiocSingular::runPCA(t(count_mtx), rank = pca_dim, get.rotation = FALSE, BSPARAM = BiocSingular::ExactParam())$x
knn_graph <- BiocNeighbors::findAnnoy(red_dat, k = k_nearest_neighbors, warn.ties = FALSE)$index
knn_graph
}

get_neighbor_df <- function(neighbors_mtx){
# Format the N by k matrix into origin, neighbor pairs
num_nodes <- nrow(neighbors_mtx)
# Initialize an empty dataframe for the neighbor pairs
neighbor_df <- data.frame(origin=integer(), neighbor=integer())
# Loop through each node and its neighbors to fill the dataframe
for (i in 1:num_nodes) {
# Get the current node's neighbors
neighbors <- neighbors_mtx[i, ]

# Create a dataframe of pairs for the current node
current_pairs <- data.frame(origin = rep(i, length(neighbors)), neighbor = neighbors)

# Append to the main dataframe
neighbor_df <- rbind(neighbor_df, current_pairs)
}
return(neighbor_df)
}

# -------------------------
# Overlapped KNN
# -------------------------
file_name <- "downsampling_deepseq_overlap.RDS"
full_knn_list <- readRDS(file_name)

df <- full_knn_list[[dataset]]
df %>% group_by(transformation) %>% mutate(n = n()) %>% select(transformation, n) %>% unique()
list_dfs <- split(df, df$transformation)
common_knn <- Reduce(function(x, y) inner_join(x, y, by = c("origin", "neighbor")), list_dfs)
common_knn_df <- common_knn %>% select(origin, neighbor)
dim(common_knn_df)
# [1] 1085 2

# -------------------------
# CPM KNN in
# -------------------------
file_name <- "GSE103568_JM8_UMIcounts.txt.gz"
count_mtx <- read.table(file_name, header = TRUE, sep = "\t")
count_mtx <- as.matrix(count_mtx)
expressed_cells <- matrixStats::colSums2(count_mtx) > 0
expressed_genes <- matrixStats::rowSums2(count_mtx) > 0
dim(count_mtx)
# [1] 22556 249
count_mtx <- count_mtx[expressed_genes, expressed_cells]
dim(count_mtx)
# [1] 22556 249
cpm_mtx <- logp_cpm_fnc(count_mtx, sf, alpha)
cpm_knn_graph <- calculate_knn(cpm_mtx)
cpm_knn_df <- get_neighbor_df(cpm_knn_graph)
dim(cpm_knn_df)
# [1] 12450 2
cpm_overlap_df <- inner_join(common_knn_df, cpm_knn_df, by = c("origin", "neighbor"))
dim(cpm_overlap_df)
# [1] 912 2

# Rows in common_knn_df but not in cpm_knn_df
common_knn_df %>% anti_join(cpm_knn_df, by = c("origin", "neighbor")) %>% dim()
# [1] 173 2
```