View source: R/pca_cor_samplevar.R
This function processes sample variables from
AnyHermesData and the
corresponding principal components matrix, and then generates the matrix of R2 values.
Note that only the
df columns which are
logical are included in the resulting matrix, because other variable types are not
df columns which are constant, all
columns with too many levels are also dropped before the analysis.
A matrix with R2 values for all combinations of sample variables and principal components.
h_pca_var_rsquared() which is used internally to calculate the R2 for one
object <- hermes_data %>% add_quality_flags() %>% filter() %>% normalize() # Obtain the principal components. pca <- calc_pca(object)$x # Obtain the `colData` as a `data.frame`. df <- as.data.frame(colData(object)) # Correlate them. r2_all <- h_pca_df_r2_matrix(pca, df) str(r2_all) # We can see that only about half of the columns from `df` were # used for the correlations. ncol(r2_all) ncol(df)
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