NIMEB | R Documentation |
Use a normalization-invariant minimum enclosing ball (NIMEB) method to discriminate differential expression (DE) genes in the same or different species.
NIMEB(countsTable, train_id, gamma, nu = 0.01, reject_rate = 0.1,
ds = FALSE)
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. |
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.
## 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)
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