fitKernelDensity: Data normalization

In qzhang503/proteoQ: Processing and Informatic Analysis of Mass Spectrometrirc Data

Data normalization

Description

fitKernelDensity calculates the fitted density of log2FC.

Usage

fitKernelDensity(df, n_comp = 3L, seed = NULL, ...)

Arguments

df	An input data frame.
n_comp	Integer; the number of Gaussian components to be used with method_align = MGKernel. A typical value is 2 or 3. The variable n_comp overwrites the argument k in normalmixEM.
seed	Integer; a seed for reproducible fitting at method_align = MGKernel.
...	slice_: variable argument statements for the identification of row subsets. The partial data will be taken for parameterizing the alignment of log2FC across samples. The full data set will be updated subsequently with the newly derived parameters. Note that there is no data entry removals from the complete data set with the slice_ procedure. The variable argument statements should be in the following format: each of the statement contains a list of logical expression(s). The lhs needs to start with slice_. The logical condition(s) at the rhs needs to be enclosed in exprs with round parenthesis. For example, pep_len is a column key present in Peptide.txt. The slice_peps_at = exprs(pep_len >= 10, pep_len <= 50) will extract peptide entries with the number of amino acid residues betwen 10 and 50 for log2FC alignment. Shorter or longer peptide sequences will remain in Peptide.txt but not used in the parameterization. See also normPSM for the variable arguments of filter_. Additional parameters from normalmixEM, i.e., maxit, the maximum number of iterations allowed; epsilon, tolerance limit for declaring algorithm convergence.

Value

A data frame.

qzhang503/proteoQ documentation built on March 16, 2024, 5:27 a.m.