Description Usage Arguments Value Author(s) References
Obtaining the result by performing topic model with selection of the optimal topic number automatically.
1 | Get_topics(expression_profile_var_gene, sample_info_gene, topic_number_min = 3, topic_number_max = 8, alpha = 0.5, seed_num = 2017, burnin = 1000, thin = 100, iter = 1000, plot = FALSE, plot_path = "~/select_topic_number.pdf")
|
expression_profile_var_gene |
A dataframe showing the expression profile only for the selected highly dispersion differentially expressed genes. |
sample_info_gene |
A character vector showing the knockout gene of each sample after all the filterings. |
topic_number_min |
The minimum candidate topic number. |
topic_number_max |
The maximum candidate topic number. |
alpha |
alpha is the weight of specificity score. Its value is between 0 and 1. The default is 0.5. |
plot |
FALSE by default. If TRUE, plot the graph. |
plot_path |
The path of the graph you plot. It works only when the parameter "plot" is TRUE. |
seed_num |
Object of class "integer"; used to set the seed for Gibbs sampling. Default 2017. |
burnin |
Object of class "integer"; number of omitted Gibbs iterations at beginning, by default 1000. |
thin |
Object of class "integer"; number of omitted in-between Gibbs iterations, by default equals iter. |
iter |
Object of class "integer"; number of Gibbs iterations, by default equals 1000. |
Object of class "LDA" with the optimal topic number.
Bin Duan
Blei D.M., Ng A.Y., Jordan M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993<e2><80><93>1022.
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