h_pca_df_r2_matrix: Calculation of R2 Matrix between Sample Variables and...

In insightsengineering/hermes: Preprocessing, analyzing, and reporting of RNA-seq data

Calculation of R2 Matrix between Sample Variables and Principal Components

Description

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

Usage

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.

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.

Value

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

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)

# We can see that only about half of the columns from `df` were
# used for the correlations.
ncol(r2_all)
ncol(df)

insightsengineering/hermes documentation built on March 1, 2023, 5:58 a.m.