GenePattern 2.0

Reich, Michael; Liefeld, Ted; Gould, Joshua; Lerner, Jim; Tamayo, Pablo; Mesirov, Jill P.

doi:10.1038/ng0506-500

Cited by 1,887 publications

(1,537 citation statements)

References 2 publications

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“…We compared the SWIFT competitive template approach with alternative algorithms that could compare cluster data between groups of samples, using the available implementations on the GenePattern 35 server or in R:Bioconductor 36. Using the Aging 1 dataset, competitive SWIFT analysis was benchmarked against seven clustering and comparison algorithms.…”

Section: Resultsmentioning

confidence: 99%

Competitive SWIFT cluster templates enhance detection of aging changes

Rebhahn

Roumanes

et al. 2015

Cytometry Pt A

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Clustering‐based algorithms for automated analysis of flow cytometry datasets have achieved more efficient and objective analysis than manual processing. Clustering organizes flow cytometry data into subpopulations with substantially homogenous characteristics but does not directly address the important problem of identifying the salient differences in subpopulations between subjects and groups. Here, we address this problem by augmenting SWIFT—a mixture model based clustering algorithm reported previously. First, we show that SWIFT clustering using a “template” mixture model, in which all subpopulations are represented, identifies small differences in cell numbers per subpopulation between samples. Second, we demonstrate that resolution of inter‐sample differences is increased by “competition” wherein a joint model is formed by combining the mixture model templates obtained from different groups. In the joint model, clusters from individual groups compete for the assignment of cells, sharpening differences between samples, particularly differences representing subpopulation shifts that are masked under clustering with a single template model. The benefit of competition was demonstrated first with a semisynthetic dataset obtained by deliberately shifting a known subpopulation within an actual flow cytometry sample. Single templates correctly identified changes in the number of cells in the subpopulation, but only the competition method detected small changes in median fluorescence. In further validation studies, competition identified a larger number of significantly altered subpopulations between young and elderly subjects. This enrichment was specific, because competition between templates from consensus male and female samples did not improve the detection of age‐related differences. Several changes between the young and elderly identified by SWIFT template competition were consistent with known alterations in the elderly, and additional altered subpopulations were also identified. Alternative algorithms detected far fewer significantly altered clusters. Thus SWIFT template competition is a powerful approach to sharpen comparisons between selected groups in flow cytometry datasets. © 2015 The Authors. Published Wiley Periodicals Inc.

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Section: Resultsmentioning

confidence: 99%

Competitive SWIFT cluster templates enhance detection of aging changes

Rebhahn

Roumanes

et al. 2015

Cytometry Pt A

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“…We ran ssGSEA from GenePattern (Reich et al., 2006) to compute the enrichment score of a given pathway in each sample. Using this tool, the gene expression values for a given sample were rank‐normalized, and an enrichment score was produced using the empirical cumulative distribution functions (ECDF) of the genes in the gene set and the remaining genes.…”

Section: Methodsmentioning

confidence: 99%

The SCN9A channel and plasma membrane depolarization promote cellular senescence through Rb pathway

et al. 2018

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SummaryOncogenic signals lead to premature senescence in normal human cells causing a proliferation arrest and the elimination of these defective cells by immune cells. Oncogene‐induced senescence (OIS) prevents aberrant cell division and tumor initiation. In order to identify new regulators of OIS, we performed a loss‐of‐function genetic screen and identified that the loss of SCN9A allowed cells to escape from OIS. The expression of this sodium channel increased in senescent cells during OIS. This upregulation was mediated by NF‐κB transcription factors, which are well‐known regulators of senescence. Importantly, the induction of SCN9A by an oncogenic signal or by p53 activation led to plasma membrane depolarization, which in turn, was able to induce premature senescence. Computational and experimental analyses revealed that SCN9A and plasma membrane depolarization mediated the repression of mitotic genes through a calcium/Rb/E2F pathway to promote senescence. Taken together, our work delineates a new pathway, which involves the NF‐κB transcription factor, SCN9A expression, plasma membrane depolarization, increased calcium, the Rb/E2F pathway and mitotic gene repression in the regulation of senescence. This work thus provides new insight into the involvement of ion channels and plasma membrane potential in the control of senescence.

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“…24–26 The ES was then compared between the two clusters using a Wilcoxon test. Since we observed a strong and significant enrichment of immune pathways, especially lymphocyte-related pathways, in cluster 1 compared to cluster 2 (Figure 1B; supplementary file 1), cluster 1 was named ‘immunological’.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, based on differential gene expression between OPL subtypes, we computed a gene expression score for the OPL classification, using the single sample gene set enrichment analysis (ssGSEA) 24,25 from Gene Pattern. 26 We compared these methods of classification in terms of the accuracy percentage (= [number of samples misclassified/number of samples well-classified]*100).…”

Section: Methodsmentioning

confidence: 99%

“…The ssGSEA was used to compute an enrichment score for each pathway in each sample 24,25 which was run from GenePattern. 26 Unlike GSEA which analyzes differential pathways between two phenotypical groups, the ssGSEA tool allows for computing an enrichment score (ES) of a given gene set in each sample. The gene expression values for a given sample are rank-normalized, and an ES is produced using the empirical cumulative distribution functions of the genes in the gene set and the remaining genes.…”

Section: Methodsmentioning

confidence: 99%

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Immunological and classical subtypes of oral premalignant lesions

et al. 2018

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Oral squamous cell carcinoma (OSCC) is a major cause of cancer-associated morbidity and mortality and may develop from oral premalignant lesions (OPL). An improved molecular classification of OPL may help refining prevention strategies. We identified two main OPL gene-expression subtypes, named immunological and classical, in 86 OPL (discovery dataset). A gene expression-based score was then developed to classify OPL samples from three independent datasets, including 17 (GSE30784),13 (GSE10174) and 15 (GSE85195) OPLs, into either one of the two gene-expression subtypes. Using the single sample gene set enrichment analysis, enrichment scores for immune-related pathways were different between the two OPL subtypes. In OPL from the discovery set, loss of heterozygosities (LOH) at 3p14, 17p13, TP53, 9p21 and 8p22 and miRNA gene expression profiles were analyzed. Deconvolution of the immune infiltrate was performed using the Microenvironment Cell Populations-counter tool. A multivariate analysis revealed that decreased miRNA-142-5p expression (P = 0.0484) and lower T-cell, monocytic and myeloid dendritic cells (MDC) immune infiltration (T-cells, P = 0.0196; CD8 T cells, P = 0.0129; MDC, P = 0.0481; and monocytes, P = 0.0212) were associated with oral cancer development in the immunological subtype only. In contrast, LOH at 3p14 (P = 0.0241), 17p13 (P = 0.0348) and TP53 (P = 0.004) were associated with oral cancer development in the classical subtype only. In conclusion, we identified 2 subtypes of OPLs, namely immune and classical, which may benefit from different and specific personalized prevention interventions.

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GenePattern 2.0

Cited by 1,887 publications

References 2 publications

Competitive SWIFT cluster templates enhance detection of aging changes

Competitive SWIFT cluster templates enhance detection of aging changes

The SCN9A channel and plasma membrane depolarization promote cellular senescence through Rb pathway

Immunological and classical subtypes of oral premalignant lesions

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