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DESeq2 employs statistical methods to normalize and analyze RNA-seq data, making it a valuable tool for researchers studying gene expression patterns and regulation. It is available through the Bioconductor repository. It was first presented in 2014. [1] As of September 2023, its use has been cited over 30,000 times. [2]
Using DESeq2 as a framework, DEvis provides a wide variety of tools for data manipulation, visualization, and project management. DEXSeq is Bioconductor package that finds differential differential exon usage based on RNA-Seq exon counts between samples. DEXSeq employs negative binomial distribution, provides options to visualization and ...
Methods: Most tools use regression or non-parametric statistics to identify differentially expressed genes, and are either based on read counts mapped to a reference genome (DESeq2, limma, edgeR) or based on read counts derived from alignment-free quantification (sleuth, [106] Cuffdiff, [107] Ballgown [108]). [109]
Within computational biology, an MA plot is an application of a Bland–Altman plot for visual representation of genomic data. The plot visualizes the differences between measurements taken in two samples, by transforming the data onto M (log ratio) and A (mean average) scales, then plotting these values.
The first requirement ensures that the method of kernel density estimation results in a probability density function. The second requirement ensures that the average of the corresponding distribution is equal to that of the sample used. If K is a kernel, then so is the function K* defined by K*(u) = λK(λu), where λ > 0. This can be used to ...
A policeman walks through the shuttered Christmas market the day after a car-ramming attack killed 5 and injured 200 in Magdeburg, Germany, Dec. 21, 2024.
Finally, a normalized count matrix with gene expression values is obtained. ADT data analysis [2] [7] [10] [11] (based on the developer's guidelines): CITE-seq-Count is a Python package from CITE-Seq developers that can be used to obtain raw counts. Seurat package from Satija lab further allows combining of the protein and RNA counts and ...
In other cases, the aggregate function can be computed by computing auxiliary numbers for cells, aggregating these auxiliary numbers, and finally computing the overall number at the end; examples include AVERAGE (tracking sum and count, dividing at the end) and RANGE (tracking max and min, subtracting at the end).