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AltAnalyze
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).
MAST / Model-based Analysis of Single-cell Transcriptomics
A flexible statistical framework for the analysis of single-cell RNA sequencing data. MAST is suitable for supervised analyses about differential expression of genes and gene modules, as well as unsupervised analyses of model residuals, to generate hypotheses regarding co-expression of genes. MAST accounts for the bimodality of single-cell data by jointly modeling rates of expression (discrete) and positive mean expression (continuous) values. Information from the discrete and continuous parts is combined to infer changes in expression levels using gene or gene set-based statistics. Because our approach uses a generalized linear framework, it can be used to jointly estimate nuisance variation from biological and technical sources, as well as biological effects of interest.
Linnorm
Provides a linear model and normality based transformation method. Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIPseq count data or any large-scale count data. It transforms such datasets for parametric tests. Some pipelines are implemented: (i) library size/batch effect normalization, (ii) cell sub-population analysis and visualization, (iii) differential expression analysis or differential peak detection, (iv) highly variable gene discovery and visualization, (v) gene correlation network analysis and visualization, (vi) stable gene selection for scRNA-seq data and (vii) data imputation.
SCRL / Single Cell Representation Learning
Learns representations for scRNA-seq data by considering the prior gene–gene association. SCRL is a data-driven and nonlinear dimension reduction method based on network-based embedding technique. It provides two advantages: (i) it can integrate both scRNA-seq data and prior biological knowledge for more insightful low-dimensional representations, and (ii) it can simultaneously learn a shared low-dimensional representation for both cells and genes.
SINCERITIES / SINgle CEll Regularized Inference using Time-stamped Expression profileS
A computational tool for inferring gene regulatory network (GRN) from time-stamped cross-sectional single cell expression data. SINCERITIES is a network interference algorithm able to regularize the linear regressions based on temporal changes of the gene expression distributions. SINCERITIES development was based on single cell expression data and single transcriptional profiling of THP-1 monocytic human leukemia cell line.
Granatum
Makes analysis more broadly accessible to researchers. Granatum is a web browser based scRNAseq analysis pipeline that conveniently walks the users through various steps of scRNA-seq analysis. It has a comprehensive list of modules, including plate merging and batch effect removal, outlier sample removal, gene filtering, gene expression normalization, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein network interaction visualization, and pseudo-time cell series construction.
Sake / Single-cell RNA-Seq Analysis and Klustering Evaluation
Assists in navigating through the expression profile. SAKE is an R package that uses non-negative matrix factorization (NMF) method for unsupervised clustering. It offers (i) quality controls modules to compare total sequenced reads to total gene transcripts detected, (ii) sample correlation heatmap plot, (iii) heatmap of sample assignment from NMF run, with dark red indicating high confidence in cluster assignments, and (iv) t-distributed stochastic neighbor embedding (t-SNE) plot to compare NMF assigned groups with t-SNE projections.
SCIMITAR / Single Cell Inference of MorphIng Trajectories and their Associated Regulation
Leverages morphing Gaussian mixtures to track biological progression and models the rewiring of these gene networks from static transcriptomes. SCIMITAR models account for heteroscedastic noise and increase the statistical power to detect progression associated genes when compared to traditional differential expression tests. It allows to detect modes in co-expression structure in the trajectory: defined coregulatory states that represent potential metastable and transitionary cell states.
SCENIC / Single Cell rEgulatory Network Inference and Clustering
Allows to reconstruct gene regulatory networks (GRNs). SCENIC uses single-cell RNA-seq data to identify stable cell states. It analyzes all the co-expression modules using cis-regulatory motif analyses. The tool reduces data dimensionality by using transcription factor (TF) regulons rather than principal components. It accounts for noise and removes technical biases, and uncovers master regulators and gene regulatory networks for each cell type.
PIVOT / Platform for Interactive analysis and Visualization Of Transcriptomics data
Allows users to analyze and visualize RNA-Seq data. PIVOT furnishes four mains functionalities (i) a graphical interface that is able to wrap existing open source packages in a single user-interface (ii) multiple tools to manipulate datasets to perform derivation or normalization (iii) a way for allowing the compatibility between inputs and outputs from different analysis modules and, (iv) functions for automatically generate reports, publication-quality figures, and reproducible computations.
flotilla
Visualizes transcriptome (RNA expression) data from hundreds of samples. Flotilla is a Python package. Flotilla is an open source, community-driven software written in Python that enables biologists with rudimentary knowledge of statistical methods and programming to analyze and visualize hundreds of RNA-seq datasets. This package includes interactive functions for common and important tasks in computational analyses of biological datasets such as dimensionality reduction, covariance analysis, classification, regression and outlier detection.
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