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DESeq2 is a software package in the field of bioinformatics and computational biology for the statistical programming language R. It is primarily employed for the analysis of high-throughput RNA sequencing (RNA-seq) data to identify differentially expressed genes between different experimental conditions.
Text normalization is the process of transforming text into a single canonical form that it might not have had before. Normalizing text before storing or processing it allows for separation of concerns, since input is guaranteed to be consistent before operations are performed on it. Text normalization requires being aware of what type of text ...
fastqp Simple FASTQ quality assessment using Python. Kraken: [9] A set of tools for quality control and analysis of high-throughput sequence data. HTSeq [10] The Python script htseq-qa takes a file with sequencing reads (either raw or aligned reads) and produces a PDF file with useful plots to assess the technical quality of a run.
In morphology and lexicography, a lemma is the canonical form of a set of words. In English, for example, run, runs, ran, and running are forms of the same lexeme, so we can select one of them; ex. run, to represent all the forms. Lexical databases such as Unitex use this kind of representation.
A rewriting system has the unique normal form property (UN) if for all normal forms a, b ∈ S, a can be reached from b by a series of rewrites and inverse rewrites only if a is equal to b. A rewriting system has the unique normal form property with respect to reduction (UN →) if for every term reducing to normal forms a and b, a is equal to ...
In words, the softmax applies the standard exponential function to each element of the input vector (consisting of real numbers), and normalizes these values by dividing by the sum of all these exponentials.
Each ij cell, then, is the number of times word j occurs in document i. As such, each row is a vector of term counts that represents the content of the document corresponding to that row. For instance if one has the following two (short) documents: D1 = "I like databases" D2 = "I dislike databases", then the document-term matrix would be:
The data in the following example were intentionally designed to contradict most of the normal forms. In practice it is often possible to skip some of the normalization steps because the data is already normalized to some extent. Fixing a violation of one normal form also often fixes a violation of a higher normal form.