REAGENT or RESOURCE	SOURCE	IDENTIFIER
Biological samples
Snap frozen medulloblastoma	CCLG Biobank/ Biological study and collaborating centres	See Table S1
Chemicals, peptides, and recombinant proteins
Trizol	Thermo Fisher	15596026
1 X Low TE Buffer (10 mM Tris-HCL, ph 7.5-8.0, 0.1 mM EDTA)	Thermo Fisher	120900915
100% Ethanol, molecular biology grade	Sigma-Aldrich	E7023
Nuclease-free Water	Thermo Fisher	AM9930
Critical commercial assays
RNeasy MinElute Cleanup Kit	Qiagen	74204
DNeasy Blood and Tissue Kit	Qiagen	69504
Agilent SureSelect XT2	Agilent	G9621A
Agilent SureSelect XTHS (Low Input)	Agilent	G9703A
Agilent SureSelect Custom DNA Target Enrichment Probes Tier 1 (500Kb)	Agilent	5190-4813
Afilent SureSelect XT Human All Exon v6 + UTR	Agilent	5190-8881
AMPPure XP Kit	Beckman Coulter	A63880
Herculase II Fusion DNA Polymerase	Agilent	600677
Dynabeads MyOne Streptavidin T1	Thermo Fisher	65601
Qubit dsDNA HS Assay Kit	Thermo Fisher	Q32851
Deposited data
Medulloblastoma methylation array dataset E-MTAB-10754	This paper	Array Express: E-MTAB-10754
Medulloblastoma RNA-seq dataset E-MTAB-10767	This paper	Array Express: E-MTAB-10767
Medulloblastoma Methylation array dataset GSE130051	Sharma et al. 2019	GEO accession: GSE130051
Medulloblastoma Methylation array dataset GSE93646	Schwalbe et al., 2017	GEO accession: GSE93646
Medulloblastoma scRNA-seq dataset GSE119926	Hovestadt et al., 2019	GEO accession: GSE119926
Human fetal cerebellum scRNA-seq dataset	Human Cell Atlas (https://www.covid19cellatlas.org/aldinger20)	dbGAP accession: phs001908.v2.p1
Software and algorithms
R v3.5.3 & v4.0.2 & R base packages	https://www.r-project.org	N/A
Bioconductor	http://bioconductor.org	NA
Kallisto v0.46.0	Bray, N., Pimentel, H., Melsted, P. et al. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34, 525–527 (2016). https://doi.org/10.1038/nbt.3519	N/A
RNA-STAR v2.7.0e	Alexander Dobin, Carrie A. Davis, Felix Schlesinger, Jorg Drenkow, Chris Zaleski, Sonali Jha, Philippe Batut, Mark Chaisson, Thomas R. Gingeras, STAR: ultrafast universal RNA-seq aligner, Bioinformatics, Volume 29, Issue 1, January 2013, Pages 15–21, https://doi.org/10.1093/bioinformatics/bts635	NA
HTSeq v0.9.1	G Putri, S Anders, PT Pyl, JE Pimanda, F Zanini Analysing high-throughput sequencing data in Python with HTSeq 2.0 https://doi.org/10.1093/bioinformatics/btac166 (2022)	N/A
SAMtools v1.9,	Heng Li, Bob Handsaker, Alec Wysoker, Tim Fennell, Jue Ruan, Nils Homer, Gabor Marth, Goncalo Abecasis, Richard Durbin, 1000 Genome Project Data Processing Subgroup, The Sequence Alignment/Map format and SAMtools, Bioinformatics, Volume 25, Issue 16, 15 August 2009, Pages 2078–2079, https://doi.org/10.1093/bioinformatics/btp352	N/A
Picard v2.2.4	https://github.com/broadinstitute/picard	N/A
QEdit	https://github.com/BioinfoUNIBA/QEdit	N/A
Genome Analysis Toolkit (GATK) version 3.7	McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA. (2010). The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res, 20:1297-303. https://doi.org/10.1101/gr.107524.110.	N/A
Ensembl Variant Effect Predictor (VEP)	McLaren, W., Gil, L., Hunt, S.E. et al. The Ensembl Variant Effect Predictor. Genome Biol 17, 122 (2016). https://doi.org/10.1186/s13059-016-0974-4	N/A
DESeq2_1.22.2	Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, 550. https://doi.org/10.1186/s13059-014-0550-8.	N/A
minfi_1.28.4	Fortin J, Triche TJ, Hansen KD (2017). “Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi.” Bioinformatics, 33(4). https://doi.org/10.1093/bioinformatics/btw691.	N/A
NMF_0.23.0	Gaujoux R, Seoighe C (2010). “A flexible R package for nonnegative matrix factorization.” BMC Bioinformatics, 11(1), 367. ISSN 1471-2105, https://doi.org/10.1186/1471-2105-11-367, https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-367.	N/A
limma_3.38.3	Ritchie et al, 2015, Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses for RNA-sequencing and microarray studies.” Nucleic Acids Research, 43(7), e47. https://doi.org/10.1093/nar/gkv007.	N/A
sva_3.36.0	https://bioconductor.org/packages/release/bioc/html/sva.html	N/A
tximport_1.10.1	Soneson et al., 2015, Soneson C, Love MI, Robinson MD (2015). “Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences.” F1000Research, 4. https://doi.org/10.12688/f1000research.7563.1.	N/A
tximportData_1.10.0	Soneson et al., 2015, Soneson C, Love MI, Robinson MD (2015). “Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences.” F1000Research, 4. https://doi.org/10.12688/f1000research.7563.1.	N/A
caret_6.0–86	cran package: https://cran.r-project.org/web/packages/available_packages_by_name.html	N/A
DMRcate_1.18.0	Peters et al., 2015, Peters TJ, Buckley MJ, Statham AL, Pidsley R, Samaras K, Lord RV, Clark SJ, Molloy PL (2015). “De novo identification of differentially methylated regions in the human genome.” Epigenetics & Chromatin, 8, 6. http://www.epigeneticsandchromatin.com/content/8/1/6.	N/A
Rtsne_0.15	cran package: https://github.com/jkrijthe/Rtsne	N/A
biomaRt_2.38.0	Durinck S, Spellman P, Birney E, Huber W (2009). “Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt.” Nature Protocols, 4, 1184–1191.	N/A
ggplot2_3.3.2	Wickham, H., 2016. ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag New York. Available at: https://ggplot2.tidyverse.org	N/A
SingleCellExperiment_1.4.1	Amezquita R, Lun A, Becht E, Carey V, Carpp L, Geistlinger L, Marini F, Rue-Albrecht K, Risso D, Soneson C, Waldron L, Pages H, Smith M, Huber W, Morgan M, Gottardo R, Hicks S (2020). “Orchestrating single-cell analysis with Bioconductor.” Nature Methods, 17, 137–145. https://www.nature.com/articles/s41592-019-0654-x.	N/A
Seurat_3.2.0	Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, III WMM, Hao Y, Stoeckius M, Smibert P, Satija R (2019). “Comprehensive Integration of Single-Cell Data.” Cell, 177, 1888-1902. https://doi.org/10.1016/j.cell.2019.05.031, https://doi.org/10.1016/j.cell.2019.05.031.	N/A
survival_3.2–7	https://cran.r-project.org/web/packages/survival/index.html	N/A
tidyverse_1.3.0	Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686.	N/A
mclust_5.4.6	Scrucca et al., 2016, Scrucca L, Fop M, Murphy TB, Raftery AE (2016). “mclust 5: clustering, classification and density estimation using Gaussian finite mixture models.” The R Journal, 8(1), 289–317. https://doi.org/10.32614/RJ-2016-021.	N/A
fgsea_1.8.0	Korotkevich G, Sukhov V, Sergushichev A (2019). “Fast gene set enrichment analysis.” bioRxiv. https://doi.org/10.1101/060012, http://biorxiv.org/content/early/2016/06/20/060012.	N/A
vcdExtra_0.7–1	https://cran.r-project.org/web/packages/vcdExtra/index.html	N/A
survminer_0.4.8	https://cran.r-project.org/web/packages/survminer/index.html	N/A
GSVA_1.30.0	Hänzelmann S, Castelo R, Guinney J (2013). “GSVA: gene set variation analysis for microarray and RNA-Seq data.” BMC Bioinformatics, 14, 7. https://doi.org/10.1186/1471-2105-14-7, http://www.biomedcentral.com/1471-2105/14/7.	N/A
Hmisc_4.4–1	https://cran.r-project.org/web/packages/Hmisc/index.html	N/A
lumi_2.34.0	Du, P., Kibbe, W.A., Lin, S.M. (2008). “lumi: a pipeline for processing Illumina microarray.” Bioinformatics	N/A
e1071_1.7–3	https://cran.r-project.org/web/packages/e1071/index.html	N/A
mlbench_2.1–1	https://cran.r-project.org/web/packages/mlbench/index.html	N/A
randomForest_4.6–14	https://cran.r-project.org/web/packages/randomForest/index.html	N/A
DoseFinding_0.9–17	https://cran.r-project.org/web/packages/DoseFinding/index.html	N/A
car_3.0–10	https://cran.r-project.org/web/packages/car/index.html	N/A
gplots_3.1.0	https://cran.r-project.org/web/packages/gplots/index.html	N/A
RColorBrewer_1.1–2	https://cran.r-project.org/web/packages/RColorBrewer/index.html	N/A
pheatmap_1.0.12	https://cran.r-project.org/web/packages/pheatmap/index.html	N/A
ggridges_0.5.2	https://cran.r-project.org/web/packages/ggridges/index.html	N/A
ggrepel_0.8.2	https://cran.r-project.org/web/packages/ggrepel/index.html	N/A
ggnewscale_0.4.3	https://cran.r-project.org/web/packages/ggnewscale/index.html	N/A
Gviz_1.26.5	Hahne F, Ivanek R (2016). “Statistical Genomics: Methods and Protocols.” In Mathé E, Davis S (eds.), chapter Visualizing Genomic Data Using Gviz and Bioconductor, 335–351. Springer New York, New York, NY. ISBN 978-1-4939-3578-9, https://doi.org/10.1007/978-1-4939-3578-9_16, https://doi.org/10.1007/978-1-4939-3578-9_16.	N/A
patchwork_1.0.1	https://cran.r-project.org/web/packages/patchwork/index.html	N/A
SingleCellExperiment_1.10.1	Amezquita R, Lun A, Becht E, Carey V, Carpp L, Geistlinger L, Marini F, Rue-Albrecht K, Risso D, Soneson C, Waldron L, Pages H, Smith M, Huber W, Morgan M, Gottardo R, Hicks S (2020). “Orchestrating single-cell analysis with Bioconductor.” Nature Methods, 17, 137–145. https://www.nature.com/articles/s41592-019-0654-x.	N/A
Seurat_4.0.1	Satija R, Farrell JA, Gennert D, Schier AF, Regev A (2015). “Spatial reconstruction of single-cell gene expression data.” Nature Biotechnology, 33, 495-502. https://doi.org/10.1038/nbt.3192, https://doi.org/10.1038/nbt.3192.	N/A
bestNormalize_1.7.0	Peterson, 2021. “Finding Optimal Normalizing Transformations via bestNormalize.” The R Journal, 13(1), 310–329. https://doi.org/10.32614/RJ-2021-041.	N/A
ggnewscale_0.4.5	https://cran.r-project.org/web/packages/ggnewscale/index.html	N/A
scales_1.1.1	https://cran.r-project.org/web/packages/scales/index.html	N/A
ggpattern_0.1.3	https://cran.r-project.org/web/packages/ggpattern/index.html	N/A
ggtern_3.3.0	Hamilton NE, Ferry M (2018). “ggtern: Ternary Diagrams Using ggplot2.” Journal of Statistical Software, Code Snippets, 87(3), 1–17. https://doi.org/10.18637/jss.v087.c03.	N/A
MASS_7.3–51.6	Venables and Ripley, 2002, Modern Applied Statistics with S, Fourth edition. Springer, New York. ISBN 0-387-95457-0, https://www.stats.ox.ac.uk/pub/MASS4/.	N/A
ggridges_0.5.3	https://cran.r-project.org/web/packages/ggridges/index.html	N/A
plotly_4.9.3	Sievert C (2020). Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC. ISBN 9781138331457, https://plotly-r.com.	N/A
ggplot2_3.3.3	Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.	N/A
monocle3_0.2.2	Trapnell, C., Cacchiarelli, D., Grimsby, J. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 32, 381–386 (2014). https://doi.org/10.1038/nbt.2859	N/A
survMisc	https://cran.r-project.org/web/packages/survMisc/index.html	N/A