Summarize linear model coefficeints for correlation plots
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@@ -416,6 +416,18 @@ calculate_interaction_scores <- function(df, df_bg, type, overlap_threshold = 2)
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R_Squared_r = first(R_Squared_r),
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R_Squared_AUC = first(R_Squared_AUC),
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# lm intercepts
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lm_intercept_L = first(lm_intercept_L),
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lm_intercept_K = first(lm_intercept_K),
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lm_intercept_r = first(lm_intercept_r),
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lm_intercept_AUC = first(lm_intercept_AUC),
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# lm slopes
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lm_slope_L = first(lm_slope_L),
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lm_slope_K = first(lm_slope_K),
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lm_slope_r = first(lm_slope_r),
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lm_slope_AUC = first(lm_slope_AUC),
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# NG, DB, SM values
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NG = first(NG),
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DB = first(DB),
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@@ -439,7 +451,7 @@ calculate_interaction_scores <- function(df, df_bg, type, overlap_threshold = 2)
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TRUE ~ "No Effect"
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),
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# For correlations
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# For correlation plots
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lm_R_squared_L = if (!all(is.na(Z_lm_L)) && !all(is.na(Avg_Zscore_L))) summary(lm(Z_lm_L ~ Avg_Zscore_L))$r.squared else NA,
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lm_R_squared_K = if (!all(is.na(Z_lm_K)) && !all(is.na(Avg_Zscore_K))) summary(lm(Z_lm_K ~ Avg_Zscore_K))$r.squared else NA,
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lm_R_squared_r = if (!all(is.na(Z_lm_r)) && !all(is.na(Avg_Zscore_r))) summary(lm(Z_lm_r ~ Avg_Zscore_r))$r.squared else NA,
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@@ -1208,12 +1220,11 @@ generate_correlation_plot_configs <- function(df) {
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x_var <- paste0("Z_lm_", rel$x)
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y_var <- paste0("Z_lm_", rel$y)
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# Access the correlation statistics from the correlation_stats list
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# Extract the R-squared, intercept, and slope from the df
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relationship_name <- paste0(rel$x, "_vs_", rel$y) # Example: L_vs_K
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stats <- correlation_stats[[relationship_name]]
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intercept <- stats$intercept
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slope <- stats$slope
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r_squared <- stats$r_squared
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intercept <- mean(df[[paste0("lm_intercept_", rel$x)]], na.rm = TRUE)
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slope <- mean(df[[paste0("lm_slope_", rel$x)]], na.rm = TRUE)
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r_squared <- mean(df[[paste0("lm_R_squared_", rel$x)]], na.rm = TRUE)
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# Generate the label for the plot
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plot_label <- paste("Interaction", rel$x, "vs.", rel$y)
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