ILoReg is a novel tool for cell population identification from single-cell RNA-seq (scRNA-seq) data. In our study , we showed that
ILoReg was able to identify, by both unsupervised clustering and visually, rare cell populations that other scRNA-seq data analysis pipelines were unable to identify.
The figure below illustrates the workflows of
ILoReg and a typical pipeline that applies feature selection prior to dimensionality reduction by principal component analysis (PCA).
In contrast to most scRNA-seq data analysis pipelines,
ILoReg does not reduce the dimensionality of the gene expression matrix by feature selection. Instead, it performs probabilistic feature extraction using iterative clustering projection (ICP), yielding a probability matrix, which contains probabilities of each of the N cells belonging to the k clusters. ICP is a novel machine learning algorithm that iteratively seeks a clustering with k clusters that maximizes the adjusted Rand index (ARI) between the clustering and its projection by L1-regularized logistic regression. In the ILoReg consensus approach, ICP is run L times and the L probability matrices are merged into a joint probability matrix and subsequently transformed by principal component analysis (PCA) into a lower dimensional matrix (consensus matrix). The final clustering step is performed using hierarhical clustering by the Ward's method, after which the user can extract a clustering with K consensus clusters. Two-dimensional visualization is supported using two popular nonlinear dimensionality reduction methods: t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP). Additionally, ILoReg provides user-friendly functions that enable identification of differentially expressed (DE) genes and visualization of gene expression.
The latest version of
ILoReg can be downloaded from GitHub using the devtools R package.
If you have questions related to
ILoReg, please contact us here.
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