SAMEclustering: SAMEclustering

In yycunc/SAMEclustering: SAME: Single-cell RNA-seq Aggregated clustering via Mixture model Ensemble for Single-cell RNA-seq Data

Description

SAME (Single-cell RNA-seq Aggregated clustering via Mixture model Ensemble): Cluster ensemble for single-cell RNA-seq data

Usage

SAMEclustering(Y, MAX = NULL, rep, SEED = 1)

Arguments

Y	a J*N matrix with J individual clustering methods and N cells.
MAX	defines the maximum number of clusters used for cluster ensemble. Default is the maximum cluster number estimated by all the single solutions.
rep	defines how many times wants to run the cluster ensemble step.
SEED	sets the seed of the random number generator. Setting the seed to a fixed value can produce reproducible cluster ensemble results.

Value

SAMEclustering returns a list object containing:

• AICcluster: optimal ensemble clustering result determined by Akaike information criterion (AIC)

• final_k_AIC: optimal cluster number determined by AIC

• BICcluster: optimal ensemble clustering result determined by Bayesian information criterion (BIC)

• final_k_BIC: optimal cluster number determined by BIC

Author(s)

Ruth Huh <[e-mail]>, Yuchen Yang <[e-mail]>, Yun Li <[e-mail]>

References

Ruth Huh, Yuchen Yang, Yun Li. SAME 2018

Examples

# Load the example data data_SAME
data("data_SAME")

# Zheng dataset
# Run individual_clustering
cluster.result <- individual_clustering(inputTags=data_SAME$Zheng.expr, SEED=123)

# Cluster ensemble using SAME clustering:
cluster.ensemble <- SAMEclustering(Y = t(cluster.result), rep = 3, SEED=123)

# Biase dataset
# Run individual_clustering
cluster.result <- individual_clustering(inputTags = data_SAME$Biase.expr, datatype = "FPKM", seurat_min_cell = 200, resolution_min = 1.2, tsne_min_cells = 200, tsne_min_perplexity = 10, SEED=123)

# Cluster ensemble using SAME clustering:
cluster.ensemble <- SAMEclustering(Y=t(cluster.result), rep = 3, SEED=123)

yycunc/SAMEclustering documentation built on May 6, 2021, 6:05 p.m.