Correlation Plots

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Correlation Plots

In this vignette, we go through how you can visualize associations between the features included in your analyses.

Data set-up

library(metasnf)

# We'll just use the first few columns for this demo
cort_sa_minimal <- cort_sa[, 1:5]

# And one more mock categorical feature for demonstration purposes
city <- fav_colour
city$"city" <- sample(
    c("toronto", "montreal", "vancouver"),
    size = nrow(city),
    replace = TRUE
)
city <- city |> dplyr::select(-"colour")

# Make sure to throw in all the data you're interested in visualizing for this
# data_list, including out-of-model measures and confounding features.
data_list <- generate_data_list(
    list(cort_sa_minimal, "cortical_sa", "neuroimaging", "continuous"),
    list(income, "household_income", "demographics", "ordinal"),
    list(pubertal, "pubertal_status", "demographics", "continuous"),
    list(fav_colour, "favourite_colour", "demographics", "categorical"),
    list(city, "city", "demographics", "categorical"),
    list(anxiety, "anxiety", "behaviour", "ordinal"),
    list(depress, "depressed", "behaviour", "ordinal"),
    uid = "unique_id"
)

## Warning in generate_data_list(list(cort_sa_minimal, "cortical_sa",
## "neuroimaging", : 188 subject(s) dropped due to incomplete data.

summarize_dl(data_list)

##               name        type       domain length width
## 1      cortical_sa  continuous neuroimaging     87     5
## 2 household_income     ordinal demographics     87     2
## 3  pubertal_status  continuous demographics     87     2
## 4 favourite_colour categorical demographics     87     2
## 5             city categorical demographics     87     2
## 6          anxiety     ordinal    behaviour     87     2
## 7        depressed     ordinal    behaviour     87     2

# This matrix contains all the pairwise association p-values
assoc_pval_matrix <- calc_assoc_pval_matrix(data_list)

assoc_pval_matrix[1:3, 1:3]

##            mrisdp_303 mrisdp_304 mrisdp_305
## mrisdp_303  0.0000000  0.4957321  0.4380079
## mrisdp_304  0.4957321  0.0000000  0.2998851
## mrisdp_305  0.4380079  0.2998851  0.0000000

Heatmaps

Here’s what a basic heatmap looks like:

ap_heatmap <- assoc_pval_heatmap(
    assoc_pval_matrix
)

save_heatmap(
    ap_heatmap,
    "assoc_pval_heatmap.png",
    width = 650,
    height = 500,
    res = 100
)

Most of this data was generated randomly, but the “colour” feature is really just a categorical mapping of “cbcl_depress_r”.

You can draw attention to confounding features and/or any out of model measures by specifying their names as shown below.

ap_heatmap2 <- assoc_pval_heatmap(
    assoc_pval_matrix,
    confounders = list(
        "Colour" = "colour",
        "Pubertal Status" = "pubertal_status"
    ),
    out_of_models = list(
        "City" = "city"
    )
)

save_heatmap(
    ap_heatmap2,
    "assoc_pval_heatmap2.png",
    width = 680,
    height = 500,
    res = 100
)

The ComplexHeatmap package offers functionality for splitting heatmaps into slices. One way to do the slices is by clustering the heatmap with k-means:

ap_heatmap3 <- assoc_pval_heatmap(
    assoc_pval_matrix,
    confounders = list(
        "Colour" = "colour",
        "Pubertal Status" = "pubertal_status"
    ),
    out_of_models = list(
        "City" = "city"
    ),
    row_km = 3,
    column_km = 3
)

save_heatmap(
    ap_heatmap3,
    "assoc_pval_heatmap3.png",
    width = 680,
    height = 500,
    res = 100
)

Another way to divide the heatmap is by feature domain. This can be done by providing a data_list with all the features in the assoc_pval_matrix and setting split_by_domain to TRUE.

ap_heatmap4 <- assoc_pval_heatmap(
    assoc_pval_matrix,
    confounders = list(
        "Colour" = "colour",
        "Pubertal Status" = "pubertal_status"
    ),
    out_of_models = list(
        "City" = "city"
    ),
    data_list = data_list,
    split_by_domain = TRUE
)

save_heatmap(
    ap_heatmap4,
    "assoc_pval_heatmap4.png",
    width = 700,
    height = 500,
    res = 100
)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.