Computational protocol: Heterogeneous muscle gene expression patterns in patients with massive rotator cuff tears

Similar protocols

Protocol publication

[…] Raw cycle-threshold values (Ct values) were obtained from all samples and read into a qPCR expression set using the R Bioconductor package HTqPC, and were normalized using the delta-Ct normalization method to obtain gene expression values (RPS18 and ACTB used as reference genes). Note that a maximum Ct of 39 was applied to all genes of interest to allow for statistical comparisons, and that lower values indicate higher expression in this method[].To determine the effect of tissue composition on muscle gene expression, linear regression of normalized gene expression and previously measured and reported histological parameters[] was implemented. The following histological parameters from the previous study[] were evaluated here: relative tissue fractions of muscle, connective tissue, and fat, along with inflammation (macrophage density) and vasculature (α-SMA+ vessel density and size). Coefficients of determination were calculated for linear relationships between expression values and histological parameters, and were considered significantly predictive when r2 >0.2 and Bonferroni-corrected p-values were statistically significant (α = 0.05).Unsupervised hierarchical clustering using Euclidean distance was then applied to the normalized expression values to determine the ability of gene expression patterns to differentiate muscle-containing samples from those without muscle, and to identify potential sub-clustering of muscle-containing samples. Where appropriate, compositional parameters were compared using a two-tailed t-test (α < 0.05) to determine significant differences in average composition of the sub-group versus the remaining biopsy pool. Additionally, principle component analysis (PCA) was performed on the normalized gene expression values using the R package prcomp[], in order to better appreciate sample clustering and to identify the genes with the largest effect on variability between samples. Subsequent differential gene expression sub-analyses were performed based on the groups identified by hierarchical clustering and confirmed by PCA.Differential expression values (delta-delta-Ct)[] were calculated with the limmaCtData wrapper in HTqPCR for the Bioconductor package limma using a moderated t-test[]. Based on the cluster analysis and histological data available for the RSA biopsies, the intact comparisons described below include only intact biopsies that clustered with muscle-containing RSA samples. Differential expression values were computed for: 1) RSA biopsies with muscle present vs. without muscle, 2) pooled RSA biopsies versus the intact group, and 3) each main RSA cluster compared to the intact group. Differential expression values were also computed between clusters to determine if different sub-groups had significantly different gene expression. In all analyses, muscle content was included as a covariate to correct for the demonstrated effects of muscle content on expression profile, and genes with a Benjamini-Hochberg adjusted p-value < 0.05 were considered significantly differentially expressed. All raw data used in this study may be found in the Supporting Information (). […]

Pipeline specifications

Software tools HTqPCR, limma
Application qPCR
Organisms Homo sapiens
Diseases Movement Disorders, Muscular Diseases