The Molecular Degree of Perturbation allows you to quantify the heterogeneity of transcriptome data samples. The
mdp takes data containing at least two classes (control and test) and assigns a score to all samples based on how perturbed they are compared to the controls. Gene perturbation scores are calculated for each gene within each class. The algorithm is based on the Molecular Distance to Health which was first implemented in Pankla et al. 2009. It expands on this algorithm by adding the options to calculate the z-score using the modified z-score (using median absolute deviation), change the z-score zeroing threshold, and look at genes that are most perturbed in the test versus control classes.
Load expression and pheno data and run:
library(mdp) data(example_data) # expression data has gene names in the rows data(example_pheno) # pheno data needs a Sample and Class column mdp.results <- mdp(data=example_data, pdata=example_pheno, control_lab = "baseline") #> Calculating Z score #> Calculating gene scores #> Calculating sample scores #> Suggesting outliers samples #> printing
The sample scores can be accessed from the
sample_scores element of the mdp results.
sample_scores_list <- mdp.results$sample_scores # select sample scores calculated using the perturbed genes sample_scores <- sample_scores_list[["perturbedgenes"]] head(sample_scores) #> Sample Score Class zscore_class outlier #> GSM429248 GSM429248 0.2856816 Asymptomatic -0.4841953 0 #> GSM429250 GSM429250 0.2868380 Asymptomatic -0.4752288 0 #> GSM429256 GSM429256 0.1556385 Asymptomatic -1.4925066 0 #> GSM429258 GSM429258 0.3825081 Asymptomatic 0.2665658 0 #> GSM429260 GSM429260 0.3800601 Asymptomatic 0.2475851 0 #> GSM429266 GSM429266 0.3895684 Asymptomatic 0.3213090 0 sample_plot(sample_scores,control_lab = "baseline", title="perturbed")
mdp works by calulating the z-score relative to the control samples, taking the absolute value of this matrix and setting all vlaues below a threshold (2 as a default) to 0. Expression values that are not 0 are perturbed. You can access this thresholded z-score matrix by,
zscore <- mdp.results$zscore
For each gene in each class, a gene score is calculated, which is the average thresholded z-score value for that gene. A gene frequency is also calculated, which is the frequency that the gene is perturbed in a class.
gene_scores <- mdp.results$gene_scores gene_freq <- mdp.results$gene_freq head(gene_scores) #> Symbol baseline Symptomatic Asymptomatic #> HBA2 HBA2 0.1005193 0.5358329 0.0000000 #> HBA1 HBA1 0.1096699 0.2279062 0.0000000 #> ACTB ACTB 0.2282284 0.5991647 0.2208631 #> UBB UBB 0.1189312 0.0000000 0.0000000 #> HBB HBB 0.1284082 0.3200307 0.0000000 #> IFITM2 IFITM2 0.0000000 0.0000000 0.0000000
mdp ranks genes according to the difference between their gene score in the test versus the control samples. The
fraction_genes option for the
mdp function allows you to control what top fraction of these ranked genes will count as the
perturbed_genes. You can obtain a list of the perturbed genes from the mdp results,
perturbed_genes <- mdp.results$perturbed_genes
Sample scores can also be calculated using genes that are within certain genesets. The
mdp will accept genesets that are in the form of a list (see example below). You can read in a .gmt file of genesets using the
fgsea::gmtPathways function from the
file_address <- system.file("extdata", "ReactomePathways.gmt", package = "mdp") pathways <- fgsea::gmtPathways(file_address) mdp.results <- mdp(data=example_data, pdata=example_pheno, control_lab = "baseline",pathways=pathways) #> Calculating Z score #> Calculating gene scores #> Calculating sample scores #> Suggesting outliers samples #> printing
For each pathway, the signal-to-noise ratio of the test versus control sample scores will be calculated. You can access these results in the
pathways element of the
head(mdp.results$pathways) #> Geneset Sig2noise #> 2 perturbedgenes 1.0065522 #> 7 Interferon alpha/beta signaling 0.8140508 #> 8 Interleukin-6 signaling 0.6002146 #> 3 Antigen processing-Cross presentation 0.5473415 #> 4 Apoptosis 0.3819693 #> 11 PI3K Cascade 0.3348710 sample_scores <- mdp.results$sample_scores[["Interferon alpha/beta signaling"]] sample_plot(sample_scores,control_lab = "baseline", title="Interferon a/b")