SAMEclustering

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

Although several methods have been recently developed to cluster single-cell RNA-seq (scRNA-Seq) data, they utilize different characteristics of data and yield varying results in terms of both the number of clusters and actual cluster assignments. Here, we present SAME-clustering, Single-cell RNA-seq Aggregated clustering via Mixture model Ensemble, a flexible, accurate and robust method for clustering scRNA-Seq data. SAMEclustering takes as input, results from multiple clustering methods, to build one consensus solution. SAME-clustering currently uses five state-of-the-art methods, SC3, CIDR, Seurat, t-SNE + k-means and SIMLR to obtain individual cluster solutions. Of the five sets of solutions, we choose a maximally diverse subset of four according to variation in pairwise Adjusted Rand Index (ARI) and we solve for an ensemble cluster solution using EM algorithms.

SAMEclustering is maintained by Ruth Huh [rhuh@live.unc.edu], Yuchen Yang [yyuchen@email.unc.edu] and Yun Li [yun_li@med.unc.edu].

News and Updates

Apr 4, 2021

• Version 1.10
  • Fix the some errors in Seurat executing.

Oct 26, 2020

• Version 1.00
  • The Seurat version used in SAMEclustering is updated to v3. Seurat v2 is no longer compatible
  • Only count data is acceptable by SAMEclustering. Other formats, such as FPKM, CPM and RPKM are no longer compatible.
  • Fix the typing error in the message delivered during the running

Dec 5, 2018

• Version 0.99.0 released
  • First official release
  • It can work on Windows, Mac and Linux platforms

Installation

You can install SAMEclustering from github with:

install.packages("devtools")

devtools::install_github("yycunc/SAMEclustering")

SAMEclustering Examples

Here we will provide examples using two datasets: one from Zheng et al., (Nature Communications, 2016)。 Zheng dataset contains 500 human peripheral blood mononuclear cells (PBMCs) sequenced using GemCode platform, which consists of three cell types, CD56+ natural killer cells, CD19+ B cells and CD4+/CD25+ regulatory T cells. The original data can be downloaded from 10X GENOMICS website.

Load the data

library("SAMEclustering")
data("data_SAME")

Zheng dataset

Setup the input expression matrix

dim(data_SAME$Zheng.expr)

data_SAME$Zheng.expr[1:5, 1:5]

Perform individual clustering

Here we perform single-cell clustering using five popular methods, SC3, CIDR, Seurat, t-SNE + k-means and SIMLR. Genes expressed in less than 10% or more than 90% of cells are removed for CIDR, tSNE + k-means and SIMLR clustering. To improve the performance of cluster ensemble, we choose a maximally diverse set of four individual cluster solutions according to variation in pairwise ARI.

cluster.result <- individual_clustering(inputTags = data_SAME$Zheng.expr, mt_filter = TRUE,
percent_dropout = 10, SC3 = TRUE, CIDR = TRUE, nPC.cidr = NULL, Seurat = TRUE, nGene_filter = FALSE,
nPC.seurat = NULL, resolution = 0.7, tSNE = TRUE, dimensions = 2, perplexity = 30, SIMLR = TRUE, diverse = TRUE, 
save.results = FALSE, SEED = 123)

The function indiviual_clustering will output a matrix, where each row represents the cluster results of each method, and each column represents a cell. User can also extend SAMEclustering to other scRNA-seq clustering methods, by putting all clustering results into a M by N matrix with M clustering methods and N single cells.

cluster.result[1:4, 1:10]

Cluster ensemble

Using the individual clustering results generated in last step, we perform cluster ensemble using EM algorithm.

cluster.ensemble <- SAMEclustering(Y = t(cluster.result), rep = 3, SEED = 123)

Function SAMEclustering will output a list with optimal clusters and clustering number based on AIC and BIC index, respectively.

cluster.ensemble

We can compare the clustering results to the true labels using the ARI. In our implementation, we use the clusters produced using the BIC criterion as our ensemble solution.

library(cidr)

# Cell labels of ground truth
head(data_SAME$Zheng.celltype)

# Calculating ARI for cluster ensemble
adjustedRandIndex(cluster.ensemble$BICcluster, data_SAME$Zheng.celltype)

Citation

Huh, R., Yang, Y., Jiang, Y., Shen, Y., Li, Y. (2020) SAME-clustering: Single-cell Aggregated Clustering via Mixture Model Ensemble. Nucleic Acids Research, 48: 86-95. [PMID: 31777938].