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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]
The aim behind the choice of a variance-stabilizing transformation is to find a simple function ƒ to apply to values x in a data set to create new values y = ƒ(x) such that the variability of the values y is not related to their mean value.
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.
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.
The following is a Python implementation of BatchNorm for 2D convolutions: import numpy as np def batchnorm_cnn ( x , gamma , beta , epsilon = 1e-9 ): # Calculate the mean and variance for each channel. mean = np . mean ( x , axis = ( 0 , 1 , 2 ), keepdims = True ) var = np . var ( x , axis = ( 0 , 1 , 2 ), keepdims = True ) # Normalize the ...
Feature scaling is a method used to normalize the range of independent variables or features of data. In data processing , it is also known as data normalization and is generally performed during the data preprocessing step.
Normalizing moments, using the standard deviation as a measure of scale. Coefficient of variation: Normalizing dispersion, using the mean as a measure of scale, particularly for positive distribution such as the exponential distribution and Poisson distribution.
Packages like ggplot2 in R and Matplotlib in Python are often used to create the visuals. The table can also be annotated using a reference annotation file, usually in GTF or GFF format to provide more context about the genes, such as the chromosome name, strand, and start and positions, and aid result interpretation.