inital website

DESCRIPTION

@@ -7,7 +7,6 @@ Authors@R: c(
   person(given = "Xuewei", family = "Cao", email = "xc2270@cumc.columbia.edu", role = c("cre", "aut")),
   person(given = "Haochen", family = "Sun", email = "hs3393@cumc.columbia.edu", role = "aut"),
   person(given = "Ru", family = "Feng", email = "rf2872@cumc.columbia.edu", role = "aut"),
-  person(given = "Rahul", family = "Mazumder", email = "rahulmaz@mit.edu", role = "aut"),
   person(given = "Daniel", family = "Nachun", role = "aut"),
   person(given = "Kushal", family = "Dey", email = "deyk@mskcc.org", role = c("aut")),
   person(given = "Gao", family = "Wang", email = "gw2411@cumc.columbia.edu", role = c("aut"))

LICENSE

@@ -1,2 +1,2 @@
 YEAR: 2025
-COPYRIGHT HOLDER: StatFunGen authors
+COPYRIGHT HOLDER: StatFunGen Lab at Columbia University

R/colocboost_assemble.R

@@ -242,6 +242,7 @@ colocboost_assemble <- function(cb_obj,
         if (!is.null(cb_output$ucos_details$ucos)) {
           cb_output$vpa <- apply(do.call(cbind, cb_output$ucos_details$ucos_weight), 1, function(w0) 1 - prod(1 - w0))
           names(cb_output$vpa) <- data_info$variables
+          class(cb_output) <- "colocboost"
           cb_output$ucos_summary <- get_ucos_summary(cb_output)
         } else {
           tmp <- list("vpa" = NULL, "ucos_summary" = NULL)

R/colocboost_plot.R

@@ -244,12 +244,12 @@ colocboost_plot <- function(cb_output, y = "log10p",
               x0 <- intersect(args$x, cs)
               y1 <- args$y[match(x0, args$x)]
               points(x0, y1,
-                pch = 4, col = adjustcolor(legend_text$col[i.uncoloc], alpha.f = 0.3),
+                pch = 4, col = adjustcolor(legend_text$col[uncoloc$cos_idx_to_uncoloc[i.uncoloc]], alpha.f = 0.3),
                 cex = 1.5, lwd = 1.5
               )
               texts <- c(texts, uncoloc$cos_uncoloc_texts[i.cs])
-              shape_col <- c(shape_col, adjustcolor(legend_text$col[i.uncoloc], alpha.f = 1))
-              texts_col <- c(texts_col, adjustcolor(legend_text$col[i.uncoloc], alpha.f = 0.8))
+              shape_col <- c(shape_col, adjustcolor(legend_text$col[uncoloc$cos_idx_to_uncoloc[i.uncoloc]], alpha.f = 1))
+              texts_col <- c(texts_col, adjustcolor(legend_text$col[uncoloc$cos_idx_to_uncoloc[i.uncoloc]], alpha.f = 0.8))
             }
           }
           if (length(texts) == 0) {

_pkgdown.yml

@@ -5,8 +5,6 @@ template:
 
 navbar:
   left:
-    - text: "Home"
-      href: index.html
     - text: "News"
       href: articles/announcements.html
     - text: "Installation"
@@ -24,16 +22,16 @@ articles:
     contents:
     - Input_Data_Format
     - Individual_Level_Colocalization
-    - Summary_Level_Colocalization
+    - Summary_Statistics_Colocalization
     - Disease_Prioritized_Colocalization
-    - ColocBoost_tutorial_advance
-    - ColocBoost_tutorial_strong_colocalization
-    - ColocBoost_tutorial_diagnostic
+    - Interpret_ColocBoost_Output
+    - Visualization_ColocBoost_Output
 
   - title: internal
     contents:
     - announcements
     - installation
+    - ColocBoost_tutorial_diagnostic
 
 reference:
   - title: "Example Data"

vignettes/Input_Data_Format.Rmd

@@ -18,44 +18,51 @@ knitr::opts_chunk$set(
 library(colocboost)
 ```
 
-## Input Data Format
 
-This vignette documents the required input data formats and provides examples of data included in the package.
+This vignette documents the standard input data formats of `colocboost`.
 
-### Individual Level Data
+## 1. Individual Level Data
 
-For analyses using individual-level data, the package requires matched X and Y data. Below is the format and an example from the package:
+For analyses using individual-level data, the basic format for single trait is as follows:
 
-```{r individual-level-example}
-# Load example individual-level data
-data(Ind_5traits)
+- `X` is an N * P matrix with N individuals and P variants. Including variant names as column names is highly recommended, especially when working with multiple X matrices and Y vectors.
+- `Y` is a length N vector containing phenotype values for the same N individuals with X.
 
-# Display the structure
-str(Ind_5traits)
-```
+The input format for multiple traits is similar, but `X` matrix should be a list of matrices, each corresponding to a different trait. `Y` vector should also be a list of vectors. 
+For example:
+
+- `X = list(X1, X2, X3, X4, X5)` where each `Xi` is a matrix for trait `i` - with the dimension of Ni * Pi, where Ni and Pi do not need to be the same for different traits.
+- `Y = list(Y1, Y2, Y3, Y4, Y5)` where each `Yi` is a vector for trait `i` - with Ni individuals.
+
+
+`colocboost` also offers flexible input options (see detailed usage with different input formats, 
+refer to [Individual Data Colocalization](https://statfungen.github.io/colocboost/articles/Individual_Level_Colocalization.html).):
 
-#### Format Requirements
+- Single X matrix with N * P with Y matrix with N * L for L traits.
+- Multiple X matrices and unmatched Y vectors with a mapping dictionary.
 
-- Data should be in a data frame or matrix
-- Each row represents an individual
-- Columns must include matched genotype (X) and phenotype (Y) data
-- Missing values should be coded as NA
 
-### Summary Statistics
+## 2. Summary Statistics
 
-For analyses using summary statistics, the package requires a data frame with matched linkage disequilibrium (LD) information:
+For analyses using summary statistics, the basic format for single trait is as follows:
 
+- `sumstat` is a data frame with required columns `z` or (`beta`, `sebeta`), and optional columns but highly recommended `n` and `variant`.
 ```{r summary-stats-example}
-# Load example summary statistics data
 data(Sumstat_5traits)
-
-# Display the structure
-str(Sumstat_5traits)
+head(Sumstat_5traits$sumstat[[1]])
 ```
 
-#### Format Requirements
+    - `z` or (`beta`, `sebeta`) - required: either z-score or (effect size and standard error)
+    - `n` - highly recommended: sample size for the summary statistics, it is highly recommendation to provide.
+    - `variant` - highly recommended: required if sumstat for different outcomes do not have the same number of variables (multiple sumstat and multiple LD).
+
+
+- `LD` is a matrix of LD. This matrix does not need to contain the exact same variants as in `sumstat`, but the `colnames` and `rownames` of `LD` should include the `variant` names for proper alignment.
+
+The input format for multiple traits is similar, but `sumstat` should be a list of data frames `sumstat = list(sumstat1, sumstat2, sumstat3)`. 
+The flexibility of input format for multiple traits is as follows (see detailed usage with different input formats, 
+refer to [Summary Statistics Colocalization](https://statfungen.github.io/colocboost/articles/Summary_Level_Colocalization.html).)::
 
-- Data should be in a data frame
-- Each row represents a variant
-- Must include effect size, standard error, and sample size information
-- LD matrix must be provided separately or calculated from the data
+- One LD matrix with a superset of variants in `sumstat` for all traits is allowed.
+- Multiple LD matrices, each corresponding to a different trait, are also allowed for the trait-specific LD structure.
+- Multiple LD matrices and unmatched `sumstat` data frames with a mapping dictionary are also allowed.  

vignettes/Interpret_ColocBoost_Output.Rmd

@@ -0,0 +1,101 @@
+---
+title: "Interpret ColocBoost Output"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Interpret ColocBoost Output}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+
+This vignette demonstrates how to interpret the output of ColocBoost, specifically to get the summary of colocalization and filtering our the weak signals.
+
+```{r setup}
+library(colocboost)
+```
+
+## 1. Look at summary of colocalization
+
+### Causal variant structure
+The dataset features two causal variants with indices 644 and 2289.
+
+- Causal variant 644 is associated with traits 1, 2, 3, and 4.
+- Causal variant 2289 is associated with traits 2, 3, and 5.
+
+```{r run-colocboost}
+# Loading the Dataset
+data(Ind_5traits)
+# Run colocboost 
+res <- colocboost(X = Ind_5traits$X, Y = Ind_5traits$Y)
+cos_summary <- res$cos_summary
+names(cos_summary)
+```
+
+The `cos_summary` object contains the colocalization summary for all colocalization events, with each row representing a single colocalization event.
+The summary includes the following columns:
+
+
+- **focal_outcome**: The focal outcome being analyzed, or `FALSE` if no focal outcome exists.
+- **colocalized_outcomes**: Traits that are colocalized within the 95% colocalization confidence set (CoS).
+- **cos_id**: A unique identifier for each 95% colocalization confidence set (CoS).
+- **purity**: The minimum absolute correlation of variants within the 95% colocalization confidence set (CoS).
+- **top_variable**: The variable with the highest variant colocalization probability (VCP).
+- **top_variable_vcp**: The variant colocalization probability for the top variable.
+- **cos_npc**: The normalized probability of colocalization for the 95% confidence set, providing empirical evidence in favor of colocalization over a trait-specific configuration.
+- **min_npc_outcome**: The minimum normalized probability among colocalized traits.
+- **n_variables**: The number of variables in the 95% colocalization confidence set (CoS).
+- **colocalized_index**: The indices of colocalized variables.
+- **colocalized_variables**: A list of colocalized variables.
+- **colocalized_variables_vcp**: The variant colocalization probabilities for all colocalized variables.
+
+
+To obtain the summary of colocalization with a specific focus on traits of interest, 
+you can use the `get_cos_summary`, see detailed usage of this function in [link](https://statfungen.github.io/colocboost/reference/get_cos_summary.html). 
+This function allows you to filter the colocalization summary based on a particular outcome of interest, 
+making it easier to interpret the results for specific traits. 
+For example, if you are interested in the colocalization events involving the traits `Y1` and `Y2`, you can use the following code:
+
+
+```{r summary-colocboost}
+# Get summary table of colocalization
+cos_interest_outcome <- get_cos_summary(res, interest_outcome = c("Y1", "Y2"))
+```
+
+
+
+## 2. Get strong colocalization signals
+
+In `cos_summary`, for each 95% CoS, the `cos_npc` column provides a normalized probability of colocalization and
+`min_npc_outcome` column provides the minimum normalized probability among colocalized traits.
+Those two metrices are measured as an empirical evidence of colocalization both in CoS-level and in trait-level.
+To obtain the best minimal colocalization configuration can be defined by using both `cos_npc` and `npc_outcome`
+See detailed usage of this function in [link](https://statfungen.github.io/colocboost/reference/get_strong_colocalization.html).
+
+
+```{r run-strong-colocalization}
+filter_res <- get_strong_colocalization(res, cos_npc_cutoff = 0.5, npc_outcome_cutoff = 0.2)
+```
+
+The output from `get_strong_colocalization` is the same as output from `colocboost`, which can be directly used in any post inference and visualization.
+
+
+## 3. Details of ColocBoost output
+
+The entire colocalization output from `colocboost` is stored in the `colocboost` object, which contains several components:
+
+- `cos_summary`: A summary table for colocalization events.
+- `vcp`: The variable colocalized probability for each variable.
+- `cos_details`: A object with all information for colocalization results.
+- `data_info`: A object with detailed information from input data.
+- `model_info`: A object with detailed information for colocboost model.
+
+
+
+

vignettes/Summary_Level_Colocalization.Rmd → vignettes/Summary_Statistics_Colocalization.Rmd

@@ -1,8 +1,8 @@
 ---
-title: "Summary Level Data Colocalization"
+title: "Summary Statistics Colocalization"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Summary Level Data Colocalization}
+  %\VignetteIndexEntry{Summary Statistics Colocalization}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---