This module generates a series of high level plots that describe the data overall and may be useful for identifying anomalous data and/or covariates.

  • Heatmaps
  • PCA
  • Study design
  • Other QC

    • Heatmap of Raw Data
      More Plot Information  Download detected/undetected calls data

      Heatmap of Raw Data

      Heatmap of the raw counts. The plot is meant to provide an overview of how robust the raw expression levels are across samples and gene sets. Datasets that entirely lack higher level expressions (e.g. counts > 100) may indicate experimental failure or low input. The detected/undetected calls links to a .csv file stating whether each probe is above background, with 0/1 indicating below/above background. If the user has not specified a detection threshold, probes are called detected if they have more than double the counts of the median negative control.

    • Heatmap of All Data
      More Plot Information

      Heatmap of All Data

      Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

    • Principal Components of All Data
      More Plot Information

      Principal Components of All Data

      Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

    • Outliers
      More Plot Information

      Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
      20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
      20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
      Outliers

      Table identifying outliers in the first four PCs of the data.


    • More Plot Information

      Pairwise comparisons of all covariates in the analysis. The type of plot is dependent on the types of variables compared; A categorical vs. categorical covariate plot is shown as a bar chart of counts (Y axis). Continuous vs. categorical covariates generate a boxplot with whiskers denoting 1.5 IQR. Continuous vs. continuous covariates are compared via a scatter plot. Variables that are correlated with a biological variable of interest are potential confounders that may influence downstream analyses. Additionally, bar plots and histograms show the distributions of categorical and continuous variables, respectively.

    • Variance vs. Mean normalized signal plot across all targets/probes
      More Plot Information  Mean and Variance statistics across all genes

      Variance vs. Mean normalized signal plot across all targets/probes

      Each gene's variance in the log-scaled, normalized data is plotted against its mean value across all samples. Highly variable genes are indicated by gene name. Housekeeping genes are color coded according to their use in (or omission from) normalization.

    • p-value distribution plots
      More Plot Information

      p-value distribution plots

      For each covariate included in the analysis, a histogram of p-values testing each gene's univariate association with the chosen covariate is displayed. Covariates with largely flat histograms have minimal association with gene expression; covariates with histograms with significantly more mass on the left are either associated with the expression of many genes or are confounded with a covariate that is associated with the expression. Low p-values indicate strong evidence for an association.

  • Heatmaps
  • PCA

    • Heatmap of Chemokine receptors bind chemokines Data
      More Plot Information

      Heatmap of Chemokine receptors bind chemokines Data

      Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

    • Principal Components of Chemokine receptors bind chemokines Data
      More Plot Information

      Principal Components of Chemokine receptors bind chemokines Data

      Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

    • Outliers
      More Plot Information

      Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
      20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
      20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
      Outliers

      Table identifying outliers in the first four PCs of the data.

  • Heatmaps
  • PCA

    • Heatmap of Costimulation by the CD28 family Data
      More Plot Information

      Heatmap of Costimulation by the CD28 family Data

      Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

    • Principal Components of Costimulation by the CD28 family Data
      More Plot Information

      Principal Components of Costimulation by the CD28 family Data

      Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

    • Outliers
      More Plot Information

      Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
      20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
      20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
      Outliers

      Table identifying outliers in the first four PCs of the data.

  • Heatmaps
  • PCA

    • Heatmap of DAP12 interactions Data
      More Plot Information

      Heatmap of DAP12 interactions Data

      Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

    • Principal Components of DAP12 interactions Data
      More Plot Information

      Principal Components of DAP12 interactions Data

      Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

    • Outliers
      More Plot Information

      Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
      20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
      20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
      Outliers

      Table identifying outliers in the first four PCs of the data.

  • Heatmaps
  • PCA

    • Heatmap of DAP12 signaling Data
      More Plot Information

      Heatmap of DAP12 signaling Data

      Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

    • Principal Components of DAP12 signaling Data
      More Plot Information

      Principal Components of DAP12 signaling Data

      Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

    • Outliers
      More Plot Information

      Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
      20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
      20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
      Outliers

      Table identifying outliers in the first four PCs of the data.

  • Heatmaps
  • PCA

    • Heatmap of Degradation of the extracellular matrix Data
      More Plot Information

      Heatmap of Degradation of the extracellular matrix Data

      Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

    • Principal Components of Degradation of the extracellular matrix Data
      More Plot Information

      Principal Components of Degradation of the extracellular matrix Data

      Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

    • Outliers
      More Plot Information

      Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
      20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
      20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
      Outliers

      Table identifying outliers in the first four PCs of the data.

  • Heatmaps
  • PCA

    • Heatmap of Downstream signal transduction Data
      More Plot Information

      Heatmap of Downstream signal transduction Data

      Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

    • Principal Components of Downstream signal transduction Data
      More Plot Information

      Principal Components of Downstream signal transduction Data

      Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

    • Outliers
      More Plot Information

      Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
      20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
      20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
      Outliers

      Table identifying outliers in the first four PCs of the data.

  • Heatmaps
  • PCA

    • Heatmap of FCERI mediated MAPK activation Data
      More Plot Information

      Heatmap of FCERI mediated MAPK activation Data

      Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

    • Principal Components of FCERI mediated MAPK activation Data
      More Plot Information

      Principal Components of FCERI mediated MAPK activation Data

      Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

    • Outliers
      More Plot Information

      Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
      20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
      20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
      Outliers

      Table identifying outliers in the first four PCs of the data.

  • Heatmaps
  • PCA

    • Heatmap of G alpha (i) signalling events Data
      More Plot Information

      Heatmap of G alpha (i) signalling events Data

      Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

    • Principal Components of G alpha (i) signalling events Data
      More Plot Information

      Principal Components of G alpha (i) signalling events Data

      Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

    • Outliers
      More Plot Information

      Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
      20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
      20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
      Outliers

      Table identifying outliers in the first four PCs of the data.

  • Heatmaps
  • PCA

    • Heatmap of G alpha (q) signalling events Data
      More Plot Information

      Heatmap of G alpha (q) signalling events Data

      Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

    • Principal Components of G alpha (q) signalling events Data
      More Plot Information

      Principal Components of G alpha (q) signalling events Data

      Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

    • Outliers
      More Plot Information

      Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
      20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
      20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
      Outliers

      Table identifying outliers in the first four PCs of the data.

  • Heatmaps
  • PCA

    • Heatmap of G beta.CL.gamma signalling through PI3Kgamma Data
      More Plot Information

      Heatmap of G beta.CL.gamma signalling through PI3Kgamma Data

      Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

    • Principal Components of G beta.CL.gamma signalling through PI3Kgamma Data
      More Plot Information

      Principal Components of G beta.CL.gamma signalling through PI3Kgamma Data

      Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

    • Outliers
      More Plot Information

      Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
      20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
      20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
      Outliers

      Table identifying outliers in the first four PCs of the data.

  • Heatmaps
  • PCA

    • Heatmap of Generic Transcription Pathway Data
      More Plot Information

      Heatmap of Generic Transcription Pathway Data

      Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

    • Principal Components of Generic Transcription Pathway Data
      More Plot Information

      Principal Components of Generic Transcription Pathway Data

      Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

    • Outliers
      More Plot Information

      Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
      20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
      20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
      Outliers

      Table identifying outliers in the first four PCs of the data.

  • Heatmaps
  • PCA

    • Heatmap of GPVI-mediated activation cascade Data
      More Plot Information

      Heatmap of GPVI-mediated activation cascade Data

      Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

    • Principal Components of GPVI-mediated activation cascade Data
      More Plot Information

      Principal Components of GPVI-mediated activation cascade Data

      Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

    • Outliers
      More Plot Information

      Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
      20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
      20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
      Outliers

      Table identifying outliers in the first four PCs of the data.

  • Heatmaps
  • PCA

    • Heatmap of Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell Data
      More Plot Information

      Heatmap of Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell Data

      Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

    • Principal Components of Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell Data
      More Plot Information

      Principal Components of Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell Data

      Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

    • Outliers
      More Plot Information

      Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
      20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
      20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
      Outliers

      Table identifying outliers in the first four PCs of the data.

  • Heatmaps
  • PCA

    • Heatmap of Interleukin-1 signaling Data
      More Plot Information

      Heatmap of Interleukin-1 signaling Data

      Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

    • Principal Components of Interleukin-1 signaling Data
      More Plot Information

      Principal Components of Interleukin-1 signaling Data

      Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

    • Outliers
      More Plot Information

      Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
      20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
      20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
      Outliers

      Table identifying outliers in the first four PCs of the data.

  • Heatmaps
  • PCA

    • Heatmap of Metabolism of carbohydrates Data
      More Plot Information

      Heatmap of Metabolism of carbohydrates Data

      Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

    • Principal Components of Metabolism of carbohydrates Data
      More Plot Information

      Principal Components of Metabolism of carbohydrates Data

      Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

    • Outliers
      More Plot Information

      Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
      20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
      20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
      Outliers

      Table identifying outliers in the first four PCs of the data.

  • Heatmaps
  • PCA

      • Outliers
        More Plot Information

        Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
        20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
        20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
        Outliers

        Table identifying outliers in the first four PCs of the data.

    • Heatmaps
    • PCA

      • Heatmap of MyD88.CL.Mal cascade initiated on plasma membrane Data
        More Plot Information

        Heatmap of MyD88.CL.Mal cascade initiated on plasma membrane Data

        Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

      • Principal Components of MyD88.CL.Mal cascade initiated on plasma membrane Data
        More Plot Information

        Principal Components of MyD88.CL.Mal cascade initiated on plasma membrane Data

        Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

      • Outliers
        More Plot Information

        Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
        20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
        20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
        Outliers

        Table identifying outliers in the first four PCs of the data.

    • Heatmaps
    • PCA

      • Heatmap of Peptide ligand-binding receptors Data
        More Plot Information

        Heatmap of Peptide ligand-binding receptors Data

        Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

      • Principal Components of Peptide ligand-binding receptors Data
        More Plot Information

        Principal Components of Peptide ligand-binding receptors Data

        Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

      • Outliers
        More Plot Information

        Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
        20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
        20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
        Outliers

        Table identifying outliers in the first four PCs of the data.

    • Heatmaps
    • PCA

      • Heatmap of RAF-independent MAPK13 activation Data
        More Plot Information

        Heatmap of RAF-independent MAPK13 activation Data

        Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

      • Principal Components of RAF-independent MAPK13 activation Data
        More Plot Information

        Principal Components of RAF-independent MAPK13 activation Data

        Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

      • Outliers
        More Plot Information

        Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
        20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
        20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
        Outliers

        Table identifying outliers in the first four PCs of the data.

    • Heatmaps
    • PCA

      • Heatmap of RAFMAP kinase cascade Data
        More Plot Information

        Heatmap of RAFMAP kinase cascade Data

        Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

      • Principal Components of RAFMAP kinase cascade Data
        More Plot Information

        Principal Components of RAFMAP kinase cascade Data

        Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

      • Outliers
        More Plot Information

        Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
        20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
        20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
        Outliers

        Table identifying outliers in the first four PCs of the data.

    • Heatmaps
    • PCA

      • Heatmap of Role of LAT2NTALLAB on calcium mobilization Data
        More Plot Information

        Heatmap of Role of LAT2NTALLAB on calcium mobilization Data

        Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

      • Principal Components of Role of LAT2NTALLAB on calcium mobilization Data
        More Plot Information

        Principal Components of Role of LAT2NTALLAB on calcium mobilization Data

        Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

      • Outliers
        More Plot Information

        Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
        20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
        20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
        Outliers

        Table identifying outliers in the first four PCs of the data.

    • Heatmaps
    • PCA

      • Heatmap of Senescence-Associated Secretory Phenotype (SASP) Data
        More Plot Information

        Heatmap of Senescence-Associated Secretory Phenotype (SASP) Data

        Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

      • Principal Components of Senescence-Associated Secretory Phenotype (SASP) Data
        More Plot Information

        Principal Components of Senescence-Associated Secretory Phenotype (SASP) Data

        Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

      • Outliers
        More Plot Information

        Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
        20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
        20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
        Outliers

        Table identifying outliers in the first four PCs of the data.

    • Heatmaps
    • PCA

      • Heatmap of Signaling by PDGF Data
        More Plot Information

        Heatmap of Signaling by PDGF Data

        Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

      • Principal Components of Signaling by PDGF Data
        More Plot Information

        Principal Components of Signaling by PDGF Data

        Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

      • Outliers
        More Plot Information

        Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
        20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
        20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
        Outliers

        Table identifying outliers in the first four PCs of the data.

    • Heatmaps
    • PCA

      • Heatmap of Signaling by SCF-KIT Data
        More Plot Information

        Heatmap of Signaling by SCF-KIT Data

        Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

      • Principal Components of Signaling by SCF-KIT Data
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        Principal Components of Signaling by SCF-KIT Data

        Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

      • Outliers
        More Plot Information

        Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
        20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
        20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
        Outliers

        Table identifying outliers in the first four PCs of the data.

    • Heatmaps
    • PCA

      • Heatmap of Signalling to p38 via RIT and RIN Data
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        Heatmap of Signalling to p38 via RIT and RIN Data

        Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

      • Principal Components of Signalling to p38 via RIT and RIN Data
        More Plot Information

        Principal Components of Signalling to p38 via RIT and RIN Data

        Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

      • Outliers
        More Plot Information

        Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
        20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
        20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
        Outliers

        Table identifying outliers in the first four PCs of the data.

    • Heatmaps
    • PCA

      • Heatmap of TNFR2 non-canonical NF-kB pathway Data
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        Heatmap of TNFR2 non-canonical NF-kB pathway Data

        Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

      • Principal Components of TNFR2 non-canonical NF-kB pathway Data
        More Plot Information

        Principal Components of TNFR2 non-canonical NF-kB pathway Data

        Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

      • Outliers
        More Plot Information

        Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
        20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
        20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
        Outliers

        Table identifying outliers in the first four PCs of the data.

    • Heatmaps
    • PCA

      • Heatmap of TNFs bind their physiological receptors Data
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        Heatmap of TNFs bind their physiological receptors Data

        Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

      • Principal Components of TNFs bind their physiological receptors Data
        More Plot Information

        Principal Components of TNFs bind their physiological receptors Data

        Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

      • Outliers
        More Plot Information

        Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
        20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
        20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
        Outliers

        Table identifying outliers in the first four PCs of the data.

    • Heatmaps
    • PCA

      • Heatmap of Transcriptional regulation of white adipocyte differentiation Data
        More Plot Information

        Heatmap of Transcriptional regulation of white adipocyte differentiation Data

        Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

      • Principal Components of Transcriptional regulation of white adipocyte differentiation Data
        More Plot Information

        Principal Components of Transcriptional regulation of white adipocyte differentiation Data

        Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

      • Outliers
        More Plot Information

        Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
        20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
        20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
        Outliers

        Table identifying outliers in the first four PCs of the data.

    • Heatmaps
    • PCA

      • Heatmap of VEGFA-VEGFR2 Pathway Data
        More Plot Information

        Heatmap of VEGFA-VEGFR2 Pathway Data

        Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

      • Principal Components of VEGFA-VEGFR2 Pathway Data
        More Plot Information

        Principal Components of VEGFA-VEGFR2 Pathway Data

        Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

      • Outliers
        More Plot Information

        Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
        20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
        20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
        Outliers

        Table identifying outliers in the first four PCs of the data.

    • Heatmaps
    • PCA

      • Heatmap of VEGFR2 mediated cell proliferation Data
        More Plot Information

        Heatmap of VEGFR2 mediated cell proliferation Data

        Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression. This plot is meant to provide a high level exploratory view of the data.

      • Principal Components of VEGFR2 mediated cell proliferation Data
        More Plot Information

        Principal Components of VEGFR2 mediated cell proliferation Data

        Principal component analysis maps high-dimensional datasets onto a smaller number of highly informative dimensions. Here, the first four principal components of the gene expression data are plotted against each other and colored by the values of the selected covariate. This plot may be used to identify clusters in the data and to identify variables associated with prominent signal in the data. Variables that are associated with these leading principal components should be considered in downstream analyses.

      • Outliers
        More Plot Information

        Outlier on PC 1 Outlier on PC 2 Outlier on PC 3 Outlier on PC 4
        20180330_0064-004_0064-0007_04.RCC TRUE FALSE FALSE FALSE
        20180407_0064-009_0064-0120_12.RCC FALSE TRUE FALSE FALSE
        Outliers

        Table identifying outliers in the first four PCs of the data.