model.edgeR.trended: Modeling NB Trended (Non-parametric) Dispersion with the...
In gu-mi/NBGOF: Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data
Description
Usage
Arguments
Details
Value
Author(s)
References
View source: R/model.edgeR.trended.R
Description
This function fits an NB regression model with
trended (non-parametric) dispersions using the adjusted profile likelihood estimator.
In edgeR, this function assumes the dispersion φ_i satisfies
φ_i=s(\bar{μ}_{i\cdot}), where s(\cdot) is a smooth function of
each gene's average read counts across samples. A variety of non-parametric
approaches can be used by fitting loess or spline curves on binned genes, or
using locally weighted APL. See details below. The output of this function will
be passed to the main GOF function nb.gof.m.
Usage
1
model.edgeR.trended(counts, x, lib.sizes=colSums(counts), min.n=min.n, method=method)
Arguments
counts
an m-by-n count matrix of non-negative integers. For a typical
RNA-Seq experiment, this is the read counts with m genes and n samples.
x
an n-by-p design matrix.
lib.sizes
library sizes of an RNA-Seq experiment. Default is the column
sums of the counts matrix.
min.n
minimim number of genes in a bin. Default is 100. See dispBinTrend
for details (lower-level function of estimateGLMTrendedDisp).
method
method for estimating the trended dispersion, including "auto", "bin.spline", "bin.loess", "power" and "spline".
If NULL, then the "auto" method. Normally the number of tags analyzed is greater than 200, so the "bin.spline" method is used which
calls the dispBinTrend function. See estimateGLMTrendedDisp for more details.
Details
In this trended non-parametric model, φ_{ij} is estimated in a
first step as a smooth function of
\log(\hat{φ}_{ij}) on \log(\hat{μ}_{ij}), and then treated as known
in the second step of regression coefficient inference. See the
estimateGLMTrendedDisp and
glmFit functions in the edgeR package
for more information.
Value
A list of quantities to be used in the main nb.gof.m function.
Author(s)
Gu Mi <neo.migu@gmail.com>, Yanming Di, Daniel Schafer
References
See https://github.com/gu-mi/NBGOF/wiki/ for more details.
gu-mi/NBGOF documentation built on Oct. 25, 2020, 3:30 a.m.
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Description Usage Arguments Details Value Author(s) References
View source: R/model.edgeR.trended.R
Description
This function fits an NB regression model with
trended (non-parametric) dispersions using the adjusted profile likelihood estimator.
In edgeR, this function assumes the dispersion φ_i satisfies
φ_i=s(\bar{μ}_{i\cdot}), where s(\cdot) is a smooth function of
each gene's average read counts across samples. A variety of non-parametric
approaches can be used by fitting loess or spline curves on binned genes, or
using locally weighted APL. See details below. The output of this function will
be passed to the main GOF function nb.gof.m.
Usage
1 | model.edgeR.trended(counts, x, lib.sizes=colSums(counts), min.n=min.n, method=method)
|
Arguments
counts |
an m-by-n count matrix of non-negative integers. For a typical RNA-Seq experiment, this is the read counts with m genes and n samples. |
x |
an n-by-p design matrix. |
lib.sizes |
library sizes of an RNA-Seq experiment. Default is the column
sums of the |
min.n |
minimim number of genes in a bin. Default is 100. See |
method |
method for estimating the trended dispersion, including "auto", "bin.spline", "bin.loess", "power" and "spline".
If NULL, then the "auto" method. Normally the number of tags analyzed is greater than 200, so the "bin.spline" method is used which
calls the |
Details
In this trended non-parametric model, φ_{ij} is estimated in a
first step as a smooth function of
\log(\hat{φ}_{ij}) on \log(\hat{μ}_{ij}), and then treated as known
in the second step of regression coefficient inference. See the
estimateGLMTrendedDisp and
glmFit functions in the edgeR package
for more information.
Value
A list of quantities to be used in the main nb.gof.m function.
Author(s)
Gu Mi <neo.migu@gmail.com>, Yanming Di, Daniel Schafer
References
See https://github.com/gu-mi/NBGOF/wiki/ for more details.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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