dim_reduce: Dimensionality reduction and visualization.
In bAIo-lab/Questools: multivariate analysis and visualization

Description Usage Arguments Value Author(s) Examples

Reduce dimensionality with a method in {tsne, umap, pca}.

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dim_reduce(data, method = "pca", annot1 = NULL,
  annot1.name = "annot1", annot2 = NULL, annot2.name = "annot2",
  levels = NULL)

`data`	A normalized dataframe or matrix with no missing data to be reduced in dimension.
`method`	A character string as the name of the method. Available values are "pca" (the default), "tsne", "umap".
`annot1`	A vector of continuous or factor values to color samples in the resulted plot (the order of values should be the same as the order of rows in data). Default to NULL.
`annot1.name`	The name of the variable indicating annot1 vector. Defaults to "annot1".
`annot2`	A vector of factor values indicating sample shapes to plot (the order of values should be the same as the order of rows in data). Default to NULL.
`annot2.name`	The name of the variable indicating annot2 vector. Defaults to "annot2".
`levels`	An integer value indicating the maximum number of levels of a categorical variable. To be used to distinguish the categorical variable. Defaults to NULL because it is supposed that `data` has been preprocessed using `data_preproc` and the categorical variables are specified. If it is set, first will run `data_preproc` to specify categorical and continuous variables.

A plot of data points in the 2 dimensional space.

Elyas Heidari

data("NHANES")

## Using different methods on the raw data
df <- NHANES[sample(nrow(NHANES), 500), ]
plt_pca <- dim_reduce(df, method = "pca", levels = 15)
plt_tsne <- dim_reduce(df, method = "tsne", annot1 = df$BMXBMI, annot1.name = "BMI", levels = 15)
plt_umap <- dim_reduce(df, method = "umap", annot1 = df$LBXTC, annot1.name = "Total Cholesterol",
annot2 = as.factor(df$RIAGENDR), annot2.name = "Gender", levels = 15)