R Package
Overview
The MyoScore R package provides a streamlined interface for calculating the Genetic Muscle Health Score (GMHS v3.3) from bulk RNA-seq raw count data. It includes built-in gene weights, preprocessing, scoring, and visualization functions.
INFO
Package: MyoScore v1.0.0 | License: MIT | R: >= 4.0.0
Installation
r
# Install from GitHub
devtools::install_github("Hirriririir/MyoScore")Quick Start
r
library(MyoScore)
# Calculate MyoScore from a CSV file (genes as rows, samples as columns)
scores <- myoscore_score("path/to/raw_counts.csv")
# Or from a matrix already in R
scores <- myoscore_score(count_matrix)
# For tab-separated files
scores <- myoscore_score("counts.tsv", sep = "\t")
# View results
head(scores)
#> Strength_score Mass_score LeanMuscle_score Youth_score Resilience_score MyoScore
#> S1 72.3 65.1 80.2 55.8 68.4 69.2
#> S2 45.1 38.7 42.3 61.2 35.6 44.1Input Requirements
- Format: Raw count matrix (not TPM, FPKM, or normalized values)
- Genes: Gene Symbols as row names (not Ensembl IDs)
- Samples: At least 2 samples (recommend >= 20 for reliable normalization)
- Coverage: Typical bulk RNA-seq datasets contain ~417 of the 1,116 scoring genes (~50% per dimension), which is sufficient for reliable scoring
Functions Reference
myoscore_score() — Main Scoring Function
The primary entry point. Accepts a file path or count matrix and returns per-sample scores.
r
myoscore_score(
input, # File path (CSV/TSV) or count matrix/data.frame
gene_weights = NULL, # Custom gene weights (default: built-in)
sep = ",", # File separator
min_coverage = 0.1, # Minimum gene coverage fraction per dimension
verbose = TRUE # Print progress messages
)Returns: A data.frame with columns: Strength_score, Mass_score, LeanMuscle_score, Youth_score, Resilience_score, MyoScore (all 0-100).
Scoring Pipeline:
- Raw counts normalized to log2(CPM+1)
- Gene-wise z-score standardization across all input samples
- Z-scores multiplied by gene direction and weight, then averaged (weighted mean)
- Min-max normalization to 0-100 per dimension
- Composite score = weighted sum of five dimensions
myoscore_preprocess() — Normalization
Normalize raw counts to log2(CPM+1).
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log2cpm <- myoscore_preprocess(count_matrix, verbose = TRUE)myoscore_score_dimension() — Single Dimension
Calculate the score for a single dimension.
r
log2cpm <- myoscore_preprocess(count_matrix)
youth <- myoscore_score_dimension(log2cpm, dimension = "Youth")Available dimensions: "Strength", "Mass", "LeanMuscle", "Youth", "Resilience".
myoscore_weights() — Dimension Weights
Returns the named numeric vector of dimension weights.
r
myoscore_weights()
#> Strength Mass LeanMuscle Youth Resilience
#> 0.252 0.177 0.243 0.242 0.087myoscore_dimensions() — Dimension Names
r
myoscore_dimensions()
#> [1] "Strength" "Mass" "LeanMuscle" "Youth" "Resilience"myoscore_colors() — Color Palettes
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# Five dimension colors
myoscore_colors("dimensions")
#> Strength Mass LeanMuscle Youth Resilience
#> "#50327b" "#46508b" "#f4e030" "#72c95e" "#31848f"
# Unhealthy-to-healthy spectrum
myoscore_colors("spectrum")
#> unhealthy mild neutral positive healthy
#> "#50327b" "#46508b" "#31848f" "#72c95e" "#f4e030"Visualization
Radar Chart
Requires the fmsb package.
r
# install.packages("fmsb")
# Overall mean radar
myoscore_plot_radar(scores)
# Grouped by condition (faceted panels)
myoscore_plot_radar(scores, groups = metadata$condition)
# Overlaid on single chart
myoscore_plot_radar(scores, groups = metadata$condition, facet = FALSE)
# Single sample with per-dimension colored segments
myoscore_plot_radar(c(72.3, 65.1, 80.2, 55.8, 68.4), title = "Patient A")Parameters:
| Parameter | Default | Description |
|---|---|---|
scores | — | Data.frame from myoscore_score(), or a named numeric vector of 5 values |
groups | NULL | Factor/character vector for group comparison |
colors | auto | Color vector (one per group) |
facet | TRUE | Separate panel per group |
title | NULL | Main title |
show_values | TRUE | Show score values at vertices |
Grouped Boxplot
Uses ggplot2 if available, otherwise falls back to base R.
r
# install.packages("ggplot2")
# Compare MyoScore across groups
myoscore_plot_boxplot(scores, groups = metadata$condition)
# Plot all dimensions
myoscore_plot_boxplot(scores, groups = metadata$condition, which = "all")
# Single dimension
myoscore_plot_boxplot(scores, groups = metadata$condition, which = "Youth")Parameters:
| Parameter | Default | Description |
|---|---|---|
scores | — | Data.frame from myoscore_score() |
groups | — | Factor/character vector of group labels |
which | "MyoScore" | "MyoScore", any dimension name, or "all" |
colors | auto | Color vector |
use_ggplot | TRUE | Use ggplot2 if available |
title | NULL | Main title |
Built-in Data
myoscore_genes
A data.frame with 591 gene-dimension entries (417 unique genes) used in scoring.
r
data(myoscore_genes)
head(myoscore_genes)
# Genes per dimension
table(myoscore_genes$dimension)| Column | Description |
|---|---|
ID | Gene symbol (HGNC) |
weight | Gene weight from TWAS Z-scores |
direction_v3 | +1 = high expression is healthy; -1 = high expression is unhealthy |
dimension | One of the five MyoScore dimensions |
Dependencies
| Package | Type | Purpose |
|---|---|---|
stats, utils, graphics, grDevices | Required | Base R functionality |
ggplot2 (>= 3.4.0) | Suggested | Enhanced boxplots |
fmsb | Suggested | Radar charts |
patchwork | Suggested | Multi-panel layouts |
testthat (>= 3.0.0) | Suggested | Unit testing |
knitr, rmarkdown | Suggested | Vignettes |
Example Workflow
r
library(MyoScore)
# 1. Load and score
scores <- myoscore_score("my_cohort_counts.csv")
# 2. Attach metadata
metadata <- read.csv("my_cohort_metadata.csv")
# 3. Visualize
# Radar chart comparing disease vs control
myoscore_plot_radar(scores, groups = metadata$condition)
# Boxplot of all dimensions
myoscore_plot_boxplot(scores, groups = metadata$condition, which = "all")
# 4. Export results
results <- cbind(metadata, scores)
write.csv(results, "my_cohort_myoscores.csv", row.names = FALSE)Citation
Revealing myopathy spectrum: integrating transcriptional and clinical features of human skeletal muscles with varying health conditions. Communications Biology, 2024. DOI: 10.1038/s42003-024-06096-7