Quick Start: MyoScore

Overview

MyoScore (Genetic Muscle Health Score) quantifies skeletal muscle health from bulk RNA-seq raw count data across five genetically-driven dimensions:

Dimension	Weight	GWAS Basis	Meaning
Strength	25.2%	Grip strength, walking pace	Muscle function
Mass	17.7%	Fat-free mass (whole body, limbs)	Muscle quantity
LeanMuscle	24.3%	Thigh fat infiltration MRI	Low fat = healthy
Youth	24.2%	Telomere length	Biological age
Resilience	8.7%	Myopathy diagnosis, CK levels	Disease resistance

Higher score = healthier muscle (0-100 scale).

Installation

# From GitHub
devtools::install_github("hua-xie/MyoScore")

Basic Usage

Calculate MyoScore from a count matrix

library(MyoScore)

# From a CSV file (genes as rows, samples as columns)
scores <- myoscore_score("path/to/raw_counts.csv")

# From a matrix in R
scores <- myoscore_score(count_matrix)

# For tab-separated files
scores <- myoscore_score("counts.tsv", sep = "\t")

The output is a data.frame with 6 columns per sample:

Strength_score, Mass_score, LeanMuscle_score, Youth_score, Resilience_score (individual dimensions, 0-100)
MyoScore (weighted composite, 0-100)

Explore the gene weights

library(MyoScore)
#> MyoScore v1.0.1
#>   A genetically-informed transcriptomic scoring system
#>   for quantifying human skeletal muscle health.
#>   5 dimensions: Strength, Mass, LeanMuscle, Youth, Resilience
#>   Use myoscore_score() to calculate scores from raw counts.
data(myoscore_genes)
head(myoscore_genes)
#>         ID    weight direction_v3 dimension
#> 1     NFS1 10.385270            1  Strength
#> 6     NEK4  7.877092            1  Strength
#> 10    RFT1  7.672116            1  Strength
#> 12 PLEKHM1  7.509400           -1  Strength
#> 13    MAPT  7.430800            1  Strength
#> 14    RBM6  7.390450            1  Strength
table(myoscore_genes$dimension)
#> 
#> LeanMuscle       Mass Resilience   Strength      Youth 
#>        147        219        157         31         37

Preprocessing only

# Just normalize without scoring
log2cpm <- myoscore_preprocess(count_matrix)

Single dimension

log2cpm <- myoscore_preprocess(count_matrix)
youth <- myoscore_score_dimension(log2cpm, dimension = "Youth")

Visualization

Radar chart

# Requires: install.packages("fmsb")

# Overall mean radar
myoscore_plot_radar(scores)

# Grouped by condition
myoscore_plot_radar(scores, groups = metadata$condition)

Grouped boxplot

# Requires: install.packages("ggplot2")

# Compare MyoScore across groups
myoscore_plot_boxplot(scores, groups = metadata$condition)

# All dimensions
myoscore_plot_boxplot(scores, groups = metadata$condition, which = "all")

Input 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.

Color Palette

myoscore_colors("dimensions")
#>   Strength       Mass LeanMuscle      Youth Resilience 
#>  "#50327b"  "#46508b"  "#f4e030"  "#72c95e"  "#31848f"
myoscore_colors("spectrum")
#> unhealthy      mild   neutral  positive   healthy 
#> "#50327b" "#46508b" "#31848f" "#72c95e" "#f4e030"

Citation

Revealing myopathy spectrum: integrating transcriptional and clinical features of human skeletal muscles with varying health conditions. Communications Biology, 2024.