SSNResolution: Identify optimal cluster resolution of scale-free shared...

In NMikolajewicz/scMiko: scRNAseq analysis functions. Developed using Seurat framework.

Identify optimal cluster resolution of scale-free shared nearest neighbor network (SNN)

Description

Identify optimal cluster resolution of scale-free shared nearest neighbor network (SNN)

Usage

SSNResolution(
  object,
  graph,
  target.purity = 0.7,
  start.res = 0.5,
  step.size = 0.05,
  target.level = "global",
  max.iter = 100,
  verbose = F
)

Arguments

object	Seurat object
graph	name of SNN graph in 'object'.
target.purity	Target purity to find resolution for.
start.res	Initial resolution to begin optimization with. Default is 0.5.
step.size	Step size between consecutive resolutions to test. Default is 0.05.
target.level	Specify whether 'target.purity' is achieved at "global" level [i.e., median(purity)] or at "cluster" level. Default is "global".
max.iter	Maximum number of iterations. If 'max.iter' is reached, non-optimal resolution may be returned.
verbose	Show progress. Default is T.

Value

optimal cluster resolution

Author(s)

Nicholas Mikolajewicz

References

https://nmikolajewicz.github.io/scMiko/articles/Module_Detection.html

See Also

neighborPurity

Examples

# load human gastrulation data
so.query <- readRDS("../data/demo/so_tyser2021_220621.rds")

# Expression-based feature selection
features_expr <- findNetworkFeatures(object = so.query, method = "expr",
                                     min_pct = 0.5)

# Highly-variable genes
features_hvg <- findNetworkFeatures(object = so.query, method = "hvg",
                                    n_features =  2000)

# run SSN
so.gene <- runSSN(object = so.query ,
     features = unique(c(features_hvg, features_dev)),
     scale_free = T,
     robust_pca = F,
     data_type = "pearson",
     reprocess_sct = T,
     slot = c("scale"),
     batch_feature = NULL,
     pca_var_explained = 0.9,
     optimize_resolution = T,
     target_purity = 0.8,
     step_size =  0.05,
     n_workers = parallel::detectCores(),
     verbose = F)

# get network connectivity plot
plt_connectivity <- SSNConnectivity(so.gene, quantile_threshold = 0.85, raster_dpi = 500)

# visualize
plt_connectivity$plot_edge + labs(title = "Network Connectivity")


# specify pruning threshold [0,1] (low values = less pruning, high values = more pruning)
prune.threshold <- 0.1

get feature-specific connectivities (wi)
df.wi   <- pruneSSN(object = so.gene,
                    graph = "RNA_snn_power",
                    prune.threshold = prune.threshold,
                    return.df = T)

# visualize
plt.prune <- df.wi %>%
  ggplot(aes(x = wi_l2)) +
  geom_histogram(bins = 30) +
  geom_vline(xintercept = prune.threshold, linetype = "dashed", color = "tomato") +
  labs(x = "Degree (L2 norm)", y = "Count",
       title = "Network Pruning",
       subtitle = paste0(signif(100*sum(df.wi$wi_l2 <=  prune.threshold)/nrow(df.wi), 3),
                         "% (", sum(df.wi$wi_l2 <=  prune.threshold), "/", nrow(df.wi), ") genes pruning" )) +
  theme_miko(grid = T)

print(plt.prune)

# get (pruned) gene module list
mod.list   <- pruneSSN(object = so.gene, graph = "RNA_snn_power", prune.threshold = prune.threshold)

NMikolajewicz/scMiko documentation built on June 28, 2023, 1:41 p.m.