scaleFreeNet: Apply scale-free topology transform to shared-nearest...

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

Apply scale-free topology transform to shared-nearest neighbor graph

Description

Apply scale-free topology transform to shared-nearest neighbor graph

Usage

scaleFreeNet(
  object,
  graph_name = "RNA_snn",
  sf = c(seq(1, 5, by = 0.5), seq(6, 10)),
  sf_threshold = 0.9,
  umap_knn = 10,
  n_dim = 30,
  binary_threshold = 0.9,
  n_workers = 1,
  verbose = T
)

Arguments

object	Seurat object
sf	soft thresholds (sf) that are evaluated in search for optimal soft threshold (used to transform SNN to scale-free topology). Default is sf = c(seq(1, 5, by = 0.5), seq(6, 10)).
sf_threshold	R2 threshold [0,1] for identifying optimal soft threshold. Default is 0.9 (recommended 0.8-0.9).
umap_knn	This determines the number of neighboring points used in local approximations of UMAP manifold structure. Larger values will result in more global structure being preserved at the loss of detailed local structure. In general this parameter should often be in the range 5 to 50. default is 10.
n_dim	Number of PC dimensions to use in generating UMAP. Default is PCA.
binary_threshold	Numeric threshold used to binarize SNN graph. Default is 0.9.
n_workers	Number of workers used for parallel implementation. Default is 1.
verbose	Show progress. Default is T.
graph.name	name of SNN graph in 'object'. Default is "RNA_snn".

Value

list containing

• "object" - scale-free SNN and binarized graph are stored in 'object' graph slot, and umap embedding is stored in 'object' reduction slot.

• "st.res" - soft threshold optimization results.

Author(s)

Nicholas Mikolajewicz

References

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

See Also

runSSN

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.