Assists in detecting of otherwise undetectable subpopulations of cells. scLVM is a program that can be used for estimating the proportion of variance in expression across cells that is explained by technical noise, biological variability and cell cycle. It also can be applied for creating a ‘corrected’ gene expression data set, in which the effect of the identified factor is removed.
Allows to perform several low-level analyses on of single-cell RNA-seq data. Scran is a package that provides functions to normalize cell-specific biases, assign cell cycle phase, and detect highly variable and significantly correlated genes.
A statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-seq experiments. Oscope capitalizes on the fact that cells from an unsynchronized population represent distinct states in a system. Oscope utilizes co-regulation information among oscillators to identify groups of putative oscillating genes, and then reconstructs the cyclic order of samples for each group, defined as the order that specifies each sample's position within one cycle of the oscillation, referred to as a base cycle. The reconstructed order is based on minimizing distance between each gene's expression and its gene-specific profile defined by the group's base cycle allowing for phase shifts between different genes.
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).
Reconstructs cell cycle time-series using single-cell transcriptome data. reCAT is a computational method consists of four steps: (i) the data processing, including quality control, normalization, and clustering of single cells, (ii) the order of the clusters is then recovered by finding a traveling salesman cycle, (iii) two scoring methods, Bayes-scores and mean-scores subsequently discriminate among cycle stages and (iv) a hidden Markov model (HMM) and a Kalman smoother finally estimate the underlying gene expression levels of the single-cell time-series.
Detects and deletes the cell-cycle effect from scRNA-Seq data. ccRemover identifies the components of the cell-cycle effect from scRNA-Seq data and removes them from the data, while preserving the other components of the data. The software identifies the cell-cycle effect using the expression profiles of all genes. It is quite tolerant to incomplete and/or inaccurate annotations. Its application to remove the cell-cycle effect can allow previously distorted signals of interest to emerge from the data and improve the analysis of scRNA-Seq data.
Allows to reproduce analyses and figures of the publication. AGA offers an algorithm that combines computational clustering and trajectory inference analysis strategies by explaining cell to cell variability in terms of discrete and continuous latent parameters. This method enables the creation of cellular differentiation manifold maps with complex topologies, efficiently and safely across various datasets.
Allows creation of workflow for the analysis of Single cell RNA sequencing (scRNA-seq) experiments. ascend can handle data generated from any single cell library preparation platform. It includes functions to leverage multiple CPUs, allowing most analyses to be performed on a standard desktop or laptop. In summary, this tool implements a state-of-the-art unsupervised clustering method and integrates established analysis techniques for normalization and differential gene expression.
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