Get_topics: Performing topic model to single-cell CRISPR screening data.

In bm2-lab/MASCOT: Model-based Understanding of SIngle-cell CRISPR screening

Performing topic model to single-cell CRISPR screening data.

Usage

Get_topics(expression_profile, perturb_information, topic_number = c(4:6), seed_num = 2018, burnin = 0, thin = 500, iter = 500)

Arguments

expression_profile	A dataframe showing the expression profile only for the selected highly dispersion differentially expressed genes.
perturb_information	A character vector showing the perturbation of each sample after all the filterings.
topic_number	The range of topic number. The default is 4 to 6. In most cases, 4 is better.
seed_num	Object of class "integer"; used to set the seed for Gibbs sampling. Default 2018.
burnin	Object of class "integer"; number of omitted Gibbs iterations at beginning, by default 0.
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 500.

Value

models	A list of "LDA"" class with the topic number you choose.
perturb_information	A character vector showing the perturbation of each sample after all the filterings.

Author(s)

Bin Daun

References

Blei D.M., Ng A.Y., Jordan M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.

bm2-lab/MASCOT documentation built on Dec. 17, 2024, 10:58 p.m.