Offers a method for dimensionality reduction based on parametrization. t-SNE parametrizes the non-linear mapping between the data space and the latent space by means of a feed-forward neural network. This software is implemented into seven different languages, and, additionally, as Barnes-Hut and parametric implementation. This tool is fitted for the visualization of high-dimensional datasets.
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Offers a platform for data exploration either in biomedical and nonbiomedical context. PHATE is a standalone software, available in three different programming languages, that provides unsupervised data-driven visualization of both local and global structures in high dimensional data. It can be used with several datatypes such as single-cell RNA sequencing, CyTOF, image data, and connectivity data like social networks or HI-C DNA contact maps.
An easy-to-use application for microarray, RNA-Seq and metabolomics analysis. For splicing sensitive platforms (RNA-Seq or Affymetrix Exon, Gene and Junction arrays), AltAnalyze will assess alternative exon (known and novel) expression along protein isoforms, domain composition and microRNA targeting. In addition to splicing-sensitive platforms, AltAnalyze provides comprehensive methods for the analysis of other data (RMA summarization, batch-effect removal, QC, statistics, annotation, clustering, network creation, lineage characterization, alternative exon visualization, gene-set enrichment and more).
Facilitates the analysis of cellular heterogeneity, the identification of cell types, and comparison of functional markers in response to perturbations, based on a versatile method. SPADE helps to organize high-dimensional cytometry data in an unsupervised manner, and to investigate natural and pathogenic cellular heterogeneity for biological insight. The SPADE algorithm consists of four components: (i) density-dependent downsampling, (ii) clustering, (iii) linking clusters with a minimum spanning tree, and (iv) upsampling to restore all cells in the final result. This modularized process allows more efficient sub-algorithms to replace the current components. In this sense, SPADE can be viewed as a framework for cytometric data analysis and visualization that has the capacity to be evolved and adapted.
Allows users to analyze single-cell gene expression experiments. Monocle can realize differential expression analysis, clustering, visualization, and other useful tasks on single-cell expression data. The software enjoins individual cells according to a defined progress through a biological process, without knowing ahead of time which genes define progress through that process. It is designed to work with RNA-Seq and quantitative polymerase chain reaction (qPCR) data, and implements Census and BEAM tools.
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Single Cell Transcriptomics Bioinformatics and Computational Challenges
Tools (4):
ZIFA, scLVM, SCDE, SIMLR
Topics (1):
scRNA-seq analysis
A Single Cell Transcriptome Atlas of the Human Pancreas
Tools (3):
CEL-Seq, StemID, t-SNE
Topics (1):
scRNA-seq analysis
Resolving Early Mesoderm Diversification through Single Cell Expression Profiling
Tools (4):
GSNAP, HTSeq, FastQC, t-SNE
Topics (1):
scRNA-seq analysis
Using neural networks for reducing the dimensions of single cell RNA Seq data
Tools (3):
SINCERA, SNN-Cliq, ZIFA
Topics (1):
scRNA-seq analysis
Differentiation dynamics of mammary epithelial cells revealed by single cell RNA sequencing
Tools (4):
edgeR, scran, destiny, Monocle
Topics (3):
scRNA-seq analysis, Breast Neoplasms, Breast Diseases