multiSpecificity: Evaluate specificity of single-cell markers across several...

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

Evaluate specificity of single-cell markers across several cluster resolutions.

Description

Evaluate specificity of single-cell markers across several cluster resolutions using co-dependency index-based specificity measure. Consider running multiCluster(...) first.

Usage

multiSpecificity(
  object,
  cluster_names,
  features = NULL,
  deg_prefilter = T,
  geosketch.subset = F,
  cdi_bins = seq(0, 1, by = 0.01),
  min.pct = 0.1,
  n.workers = 1,
  return_dotplot = T,
  verbose = T
)

Arguments

object	Seurat object with multi-resolution clusters provided in meta data.
cluster_names	Vector specifying names of all cluster configurations found in meta data.
features	If specified, marker specificity analysis is limited to specified features. Otherwise all features are used (more computationally intensive).
deg_prefilter	If TRUE, wilcoxon analysis is performed first to subset DEG features for downstream analysis. Results in faster performance. Default is TRUE.
geosketch.subset	Use GeoSketch method to subsample scRNA-seq data while preserving rare cell states (https://doi.org/10.1016/j.cels.2019.05.003). Logical, T or F (Default F). Recommended if cell type representation is imbalanced.
cdi_bins	Vector specifying binning for CDI-based specificity curve. Must range [0,1]. Default is seq(0, 1, by = 0.01).
min.pct	Minimal expression of features that are considered in specificity analysis. Represents fraction of expression cells and must range [0,1]. Higher values result in faster performance. Default is 0.1.
n.workers	Number of workers used for parallel implementation. Default is 1.
return_dotplot	If TRUE, dot plots visualizing expression of top specific markers are returned. Default is T.
verbose	Print progress. Default is TRUE.

Value

Seurat object

Author(s)

Nicholas Mikolajewicz

See Also

multiCluster

Examples

# clustering data
ms.list <- multiSpecificity(object = so.query, cluster_names = cluster.name, features = NULL, deg_prefilter = T,
cdi_bins = seq(0, 1, by = 0.01), min.pct = 0.1,
n.workers = 4, return_dotplot = T,  verbose = T)
df.auc.spec <- ms.list$specificity_summary
qm.res.sum.sum.all <- ms.list$specificity_raw
plt.clust.spec <- ms.list$auc_plot
plt.auc.spec <- ms.list$resolution_plot
plt.auc.dot <- ms.list$dot_plot

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