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SOM Datasets

Expression data from cerebral tumors of WHO grade II and III

Molecular profiling of cerebral gliomas distinguishes biologically distinct tumor groups and provides prognostically relevant information beyond histological classification and IDH1/2 mutation status. We performed microarray-based genome- and transcriptome-wide molecular profiling of primary tumor samples from 137 patients with cerebral gliomas, 61 WHO grade II and 76 WHO grade III tumors.

Mapping heterogeneity in patient-derived melanoma cultures by single-cell RNA-seq

Recent technological advances in single-cell genomics make it possible to analyze cellular heterogeneity of tumor samples. Here, we applied single-cell RNA-seq to measure the transcriptomes of 307 single cells cultured from three biopsies of three different patients with a BRAF/NRAS wild type, BRAF mutant/NRAS wild type and BRAF wild type/NRAS mutant melanoma metastasis, respectively. Analysis based on self-organizing maps identified sub-populations defined by multiple gene expression modules involved in proliferation, oxidative phosphorylation, pigmentation and cellular stroma.

Pseudotime dynamics in melanoma single-cell transcriptomes reveals different mechanisms of tumor progression

Single-cell transcriptomics has been used for analysis of heterogeneous populations of cells during developmental processes and for analysis of tumor cell heterogeneity. More recently, analysis of pseudotime (PT) dynamics of heterogeneous cell populations has been established as a powerful concept to study developmental processes. Here we perform PT analysis of 3 melanoma short-term cultures with different genetic backgrounds to study specific and concordant properties of PT dynamics of selected cellular programs with impact on melanoma progression.

SOM analysis of 873 malignant lymphomas

We present a transcriptome map of mature B-cell lymphomas that was obtained by analyzing expression data of 873 biopsy specimens including diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), Burkitt’s lymphoma (BL), mixed FL/DLBCL lymphomas, primary mediastinal large B-cell lymphoma (PMBL), multiple myeloma (MM), IRF4-rearranged large cell lymphoma, MYC-negative high-grade B-cell lymphomas with Chr.

SOM analysis of the blood transcriptome of 190 pneumonia samples and 10 controls

We analyzed the blood transcriptome of sepsis framed within community-acquired pneumonia (CAP) and characterized its molecular and cellular heterogeneity in terms of functional modules of co-regulated genes with impact for the underlying pathophysiological mechanisms. Our results showed that CAP severity is associated with immune suppression owing to T-cell exhaustion and HLA and chemokine receptor deactivation, endotoxin tolerance, macrophage polarization, and metabolic conversion from oxidative phosphorylation to glycolysis.

SOM analysis of the peripheral blood transcriptome

We present an extensive and detailed study on the gene expression landscape of peripheral blood of healthy individuals based on transcriptome data of 3,388 individuals screened in the population study LIFE between 2011 and 2014. For analysis we applied a neural network technique using self-organizing maps (SOM). Our home-made R-program ‘oposSOM’ enables to reduce the dimension of expression data from tens of thousands of genes to a few thousand ‘meta-genes’ and generates portraits of transcriptional activity that allowed us the sample-to-sample comparison of expression patterns.

SOM analysis of the transcriptome of Lynch syndrome

Colorectal cancer (CRC) arising in Lynch syndrome (LS) comprises tumours with constitutional mutations in DNA mismatch repair genes. There is still a lack of whole-genome and transcriptome studies of LS-CRC to address questions about similarities and differences in mutation and gene expression characteristics between LS-CRC and sporadic CRC, about the molecular heterogeneity of LS-CRC, and about specific mechanisms of LS-CRC genesis linked to dysfunctional mismatch repair in LS colonic mucosa and the possible role of immune editing.