report_cell_metrics: Reports BEARscc metrics for cells.

In BEARscc: BEARscc (Bayesian ERCC Assesstment of Robustness of Single Cell Clusters)

Description

To quantitatively evaluate the results, three metrics are calculated from the noise consensus matrix: 'stability' is the average frequency with which cells within a cluster associate with each other across simulated replicates; 'promiscuity' measures the association frequency between cells within a cluster and those outside of it; and 'score' is the difference between 'stability' and 'promiscuity'. Importantly, 'score' reflects the overall "robustness" of a cluster and its constitutive samples to technical variance. These metrics may be calculated on cell or cluster-wise basis; here, they are calculated cell-wise.

Usage

report_cell_metrics(cluster_labels, consensus_matrix)

Arguments

cluster_labels	Cluster labels for each cell across various cluster numbers and the original clustering.
consensus_matrix	A noise consensus output by compute_consensus()

Value

A melted data.frame of BEARscc metrics for each cell:

[,1]	"Cluster.identity"	The number of the cluster within the respective clustering
[,2]	"Cell"	The identifier of the sample in question.
[,3]	"Cluster.size"	Number of samples in the cluster.
[,4]	"Metric"	Whether the metric is the BEARscc Score, Promiscuity, or Stability.
[,5]	"Value"	Value of the relevant BEARscc metric for the cell in a given clustering.
[,6]	"Clustering"	The clustering pertinant to the cell-wise metrics described.

Author(s)

David T. Severson <[e-mail]>

Maintainer: Benjamin Schuster-Boeckler <[e-mail]>

Examples

data(analysis_examples)

cell_scores.df <- report_cell_metrics(BEARscc_clusts.df, noise_consensus)
cell_scores.df

BEARscc documentation built on Nov. 8, 2020, 7:56 p.m.