Difference between revisions of "How is it different from DESeq2?"

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(Created page with "DESeq2: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4302049/ - A method for differential analysis of count data, using shrinkage estimation for dispersions and fold change...")
 
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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4302049/  
 
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4302049/  
  
- A method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.  
+
A method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.  
- Ideal for data sets with a small sample size due to compound comparisons within samples.
+
 
- Data read as (Samples) vs. (Gene), or rather a matrix with reads mapped to gene in a specific sample
+
Ideal for data sets with a small sample size due to compound comparisons within samples.
- Mostly used for RNA seq, but also some other methods (HTS assays), such as chromatin immunoprecipitation sequencing, chromosome conformation capture, or counting observed taxa in metagenomic studies.  
+
 
- Improved version of DESeq (1).
+
Data read as (Samples) vs. (Gene), or rather a matrix with reads mapped to gene in a specific sample
 +
 
 +
Mostly used for RNA seq, but also some other methods (HTS assays), such as chromatin immunoprecipitation sequencing, chromosome conformation capture, or counting observed taxa in metagenomic studies.  
 +
 
 +
Improved version of DESeq (1).

Revision as of 15:21, 6 February 2018

DESeq2: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4302049/

A method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.

Ideal for data sets with a small sample size due to compound comparisons within samples.

Data read as (Samples) vs. (Gene), or rather a matrix with reads mapped to gene in a specific sample

Mostly used for RNA seq, but also some other methods (HTS assays), such as chromatin immunoprecipitation sequencing, chromosome conformation capture, or counting observed taxa in metagenomic studies.

Improved version of DESeq (1).