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Normalization software tools | Single-cell RNA sequencing data analysis

Single-cell transcriptomics is becoming an important component of the molecular biologist's toolkit. A critical step when analyzing data generated using this technology is normalization. However, normalization is typically performed using methods…
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DESeq
Desktop

DESeq

A method for differential analysis of RNA-sequencing count data, using…

A method for differential analysis of RNA-sequencing count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a…

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Single-cell…
Desktop

Single-cell normalization

A quantitative statistical method to distinguish true biological variability…

A quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. This approach quantifies the statistical significance…

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SCDE
Desktop

SCDE Single-Cell Differential Expression

Implements a set of statistical methods for analyzing single-cell RNA-seq data.

Implements a set of statistical methods for analyzing single-cell RNA-seq data.

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SAMstrt
Desktop

SAMstrt

Provides the significance analysis of sequencing data with spike-in…

Provides the significance analysis of sequencing data with spike-in normalization.

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MAST
Desktop

MAST Model-based Analysis of Single-cell Transcriptomics

A flexible statistical framework for the analysis of single-cell RNA sequencing…

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…

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Bignorm
Desktop

Bignorm

Yields filtered datasets and subsequent assemblies of competitive quality in…

Yields filtered datasets and subsequent assemblies of competitive quality in much shorter time. Bignorm is a read normalization tool that introduces as an innovation to Diginorm has shown to have a…

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SpikeIns
Desktop

SpikeIns

Allows users to make estimation of the reliability of spike-in normalization in…

Allows users to make estimation of the reliability of spike-in normalization in single-cell transcriptome studies. SpikeIns employs plate-based protocols to proceed. It can be used for routine…

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SCnorm
Desktop

SCnorm

Allows robust normalization of single cell RNA-seq data. SCnorm uses quantile…

Allows robust normalization of single cell RNA-seq data. SCnorm uses quantile regression to estimate the dependence of transcript expression on sequencing depth for every gene. The software groups…

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GRM
Desktop

GRM

A major roadblock towards accurate interpretation of single cell RNA-seq data…

A major roadblock towards accurate interpretation of single cell RNA-seq data is large technical noise resulted from small amount of input materials. The existing methods mainly aim to find…

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BASiCS
Desktop

BASiCS Bayesian Analysis of Single-Cell Sequencing data

An integrated Bayesian hierarchical model where: (i) cell-specific…

An integrated Bayesian hierarchical model where: (i) cell-specific normalisation constants are estimated as part of the model parameters, (ii) technical variability is quantified based on spike-in…

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Pseudocounted…
Desktop

Pseudocounted Quantile/NODES

A normalization technique that substantially reduces technical variability and…

A normalization technique that substantially reduces technical variability and improves the quality of downstream analyses. pQ homogenizes the expression of all genes below a fixed rank in each cell.…

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SCONE
Desktop

SCONE Single-Cell Overview of Normalized Expression

An R package for single-cell RNA-seq data quality control and normalization.…

An R package for single-cell RNA-seq data quality control and normalization. This data-driven framework uses summaries of expression data to assess the efficacy of normalization workflows.

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