Table 1 Methods for analysis sc-RNA sequencing data in the breast cancer.
From: Single cell RNA sequencing for breast cancer: present and future
Algorithm name | Function | Ref. |
|---|---|---|
Nanogrid single-nucleus RNA sequencing | Developed a high-throughput 3′single-core RNA sequencing method, which combines nano-grid technology, automatic imaging, and cell selection, and can sequence up to 1800 single-cores in parallel. | |
ISOP method | It provides a novel method to express the isoform level and heterogeneity in single-cell RNA sequencing data. | |
UQ-pgQ2 combined with DESeq2 | It improves the analysis based on intra-group comparison and applies it to the public RNA-seq breast cancer data set. | |
Average-based approach (gene-level expression) to isoform abundance/splicing event | It highlights the importance of splicing mechanisms in defining tumor heterogeneity. | |
SSCA and SSCVA methods | It can recover known biological characteristics from the data set and the shallow sparse connection autoencoders used for gene set projection. | |
SCmutt | It is a new and reliable statistical method which identifies specific cells with mutations found in bulk cell data. | |
CSMF method | It can reveal common and specific pattern scenarios with important biological significance from interrelated biological data. | |
EVA | It is used for evaluating the heterogeneity of gene expression in pathways or gene sets in single-cell RNA-seq data. | |
Digitaldlsorter | The algorithm deep learning scRNA-Seq deconvolution gene expression data. | |
VDJView | It can mine and analyze single-cell multi-omics data. | |
DUSC | The system integrates feature generation based on deep learning architecture and model-based clustering algorithms to obtain compact and useful single-cell transcription data. |
