Quick Start: MyoScore

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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.

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.