NIMEB: Detect differential expression genes for RNA-seq data

In MEB: A normalization-invariant minimum enclosing ball method to detect differentially expressed genes for RNA-seq data

Description

Use a normalization-invariant minimum enclosing ball (NIMEB) method to discriminate differential expression (DE) genes in the same or different species.

Usage

NIMEB(countsTable, train_id, gamma, nu = 0.01, reject_rate = 0.1,
ds = FALSE)

Arguments

countsTable	Matrix or data.frame of short read counts for each genes in the same or different species.
train_id	A vector shows the position of housekeeping genes or conserved genes in countsTable.
gamma	A parameter needed for all kernels except linear.
nu	parameter needed for one-classification.
reject_rate	A value used in controling the scale of ball, default is 0.01.
ds	A value to show the data is for the same species or different species. If ds is FALSE, the data is the same species, else the data is the different species.

Value

list(.) A list of results, "model" represents the model of NIMEB, which could be used to discriminate a new gene, "gamma" represents the selected gamma parameters in model NIMEB, "train_error" represents the corresponding train_error when the value of gamma changed.

Examples

## Simulation data for the same species.
library(SummarizedExperiment)
data(sim_data_sp)
gamma <- seq(1e-06,5e-05,1e-06)
sim_model_sp <- NIMEB(countsTable = assay(sim_data_sp), train_id=1:1000,
gamma, nu = 0.01, reject_rate = 0.05, ds = FALSE)

## Real data for the same species.
data(real_data_sp)
gamma <- seq(1e-06,5e-05,1e-06)
real_model_sp <- NIMEB(countsTable = assay(real_data_sp), train_id=1:530,
gamma, nu = 0.01, reject_rate = 0.1, ds = FALSE)

## Simulation data for the different species.
data(sim_data_dsp)
gamma <- seq(1e-07,2e-05,1e-06)
sim_model_dsp <- NIMEB(countsTable = assay(sim_data_dsp), train_id=1:1000,
gamma, nu = 0.01, reject_rate = 0.1, ds = TRUE)

## Real data for the different species.
data(real_data_dsp)
gamma <- seq(5e-08,5e-07,1e-08)
real_model_dsp <- NIMEB(countsTable = assay(real_data_dsp), train_id=1:143,
gamma, nu = 0.01, reject_rate = 0.1, ds = TRUE)

MEB documentation built on Nov. 8, 2020, 6:57 p.m.