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Fig. 1 | Source Code for Biology and Medicine

Fig. 1

From: MM2S: personalized diagnosis of medulloblastoma patients and model systems

Fig. 1

Overview of the MM2S package and its applications for MB subtypes of patient tumour samples and MB mouse models. A test sample (circled black star) representing normalized gene expression from human or mouse datasets is run using either of the MM2S.human or MM2S.mouse prediction functions, respectively. The MM2S prediction algorithm uses an ssGSEA and KNN-based approach to determine the MB subtype of a given sample, by looking at its 5 closest MB neighbors in 3-dimensional space. A selected number of functions can render the MM2S output in terms of sample-centric or subtype-centric views. The PredictionsHeatmap provides a heatmap representation of MM2S confidence predictions, for each sample, across all MB subtypes (WNT, SHH, Group, Group4, as well as Normal samples). Darker colors indicate a higher confidence and greater probability that a given sample belongs to a respective subtype. The PCARender function presents PCA plots of tested samples (purple) against the human training set (colored by subtype). This shows, in 3-dimensional space, the nearest MB samples to a given test sample, which indicates how the finalized subtype was assigned using the KNN algorithm. Subtype-centric views include PredictionsDistributionPie, which presents a pie charts of the major subtypes predicted across all the samples tested. PredictionsDistributionBoxplot highlights overall strength (in terms of MM2S confidence interval) of subtype predictions that were identified across all samples tested

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