Calculation of R2 Matrix between Sample Variables and Principal Components

This function processes sample variables from AnyHermesData and the corresponding principal components matrix, and then generates the matrix of R2 values.

h_pca_df_r2_matrix(pca, df)

Arguments

pca

(matrix)
comprises principal components generated by calc_pca().

df

(data.frame)
from the SummarizedExperiment::colData() of a AnyHermesData object.

Value

A matrix with R2 values for all combinations of sample variables and principal components.

Details

• Note that only the df columns which are numeric, character, factor or logical are included in the resulting matrix, because other variable types are not supported.

• In addition, df columns which are constant, all NA, or character or factor columns with too many levels are also dropped before the analysis.

See also

h_pca_var_rsquared() which is used internally to calculate the R2 for one sample variable.

Examples

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)
#>  num [1:18, 1:39] 0.102 0.183 0.147 0.391 0.662 ...
#>  - attr(*, "dimnames")=List of 2
#>   ..$ : chr [1:18] "PC1" "PC2" "PC3" "PC4" ...
#>   ..$ : chr [1:39] "AGEGRP" "AGE18" "STDDRS" "STDDRSD" ...

# We can see that only about half of the columns from `df` were
# used for the correlations.
ncol(r2_all)
#> [1] 39
ncol(df)
#> [1] 74