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
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      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
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      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.


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      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
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      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 Adenosine Pathway Data
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      Heatmap of Adenosine 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 Adenosine Pathway Data
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      Principal Components of Adenosine 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.

  • Heatmaps
  • PCA

    • Heatmap of Angiogenesis Data
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      Heatmap of Angiogenesis 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 Angiogenesis Data
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      Principal Components of Angiogenesis 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.

  • Heatmaps
  • PCA

    • Heatmap of Autophagy Data
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      Heatmap of Autophagy 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 Autophagy Data
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      Principal Components of Autophagy 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.

  • Heatmaps
  • PCA

    • Heatmap of Cell Cycle Data
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      Heatmap of Cell Cycle 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 Cell Cycle Data
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      Principal Components of Cell Cycle 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.

  • Heatmaps
  • PCA

    • Heatmap of Chemokine Signaling Data
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      Heatmap of Chemokine 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 Chemokine Signaling Data
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      Principal Components of Chemokine 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.

  • Heatmaps
  • PCA

    • Heatmap of Cholesterol Metabolism Data
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      Heatmap of Cholesterol Metabolism 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 Cholesterol Metabolism Data
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      Principal Components of Cholesterol Metabolism 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.

  • Heatmaps
  • PCA

    • Heatmap of Collagen Biosynthesis & Modification Data
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      Heatmap of Collagen Biosynthesis & Modification 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 Collagen Biosynthesis & Modification Data
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      Principal Components of Collagen Biosynthesis & Modification 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.

  • Heatmaps
  • PCA

    • Heatmap of Complement Activation Data
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      Heatmap of Complement 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 Complement Activation Data
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      Principal Components of Complement 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.

  • Heatmaps
  • PCA

    • Heatmap of Cytokine Signaling Data
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      Heatmap of Cytokine 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 Cytokine Signaling Data
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      Principal Components of Cytokine 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.

  • Heatmaps
  • PCA

    • Heatmap of De Novo Lipogenesis Data
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      Heatmap of De Novo Lipogenesis 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 De Novo Lipogenesis Data
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      Principal Components of De Novo Lipogenesis 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.

  • Heatmaps
  • PCA

    • Heatmap of ECM Degradation Data
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      Heatmap of ECM Degradation 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 ECM Degradation Data
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      Principal Components of ECM Degradation 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.

  • Heatmaps
  • PCA

    • Heatmap of ECM Synthesis Data
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      Heatmap of ECM Synthesis 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 ECM Synthesis Data
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      Principal Components of ECM Synthesis 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.

  • Heatmaps
  • PCA

    • Heatmap of EMT Data
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      Heatmap of EMT 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 EMT Data
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      Principal Components of EMT 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.

  • Heatmaps
  • PCA

    • Heatmap of Endotoxin Response Data
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      Heatmap of Endotoxin Response 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 Endotoxin Response Data
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      Principal Components of Endotoxin Response 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.

  • Heatmaps
  • PCA

    • Heatmap of Epigenetic Modification Data
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      Heatmap of Epigenetic Modification 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 Epigenetic Modification Data
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      Principal Components of Epigenetic Modification 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.

  • Heatmaps
  • PCA

    • Heatmap of Fatty Acid Metabolism Data
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      Heatmap of Fatty Acid Metabolism 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 Fatty Acid Metabolism Data
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      Principal Components of Fatty Acid Metabolism 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.

  • Heatmaps
  • PCA

    • Heatmap of Focal Adhesion Kinase Data
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      Heatmap of Focal Adhesion Kinase 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 Focal Adhesion Kinase Data
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      Principal Components of Focal Adhesion Kinase 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.

  • Heatmaps
  • PCA

    • Heatmap of Gluconeogenesis Data
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      Heatmap of Gluconeogenesis 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 Gluconeogenesis Data
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      Principal Components of Gluconeogenesis 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.

  • Heatmaps
  • PCA

    • Heatmap of Granulocyte Activity Data
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      Heatmap of Granulocyte Activity 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 Granulocyte Activity Data
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      Principal Components of Granulocyte Activity 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.

  • Heatmaps
  • PCA

    • Heatmap of Hedgehog Signaling Data
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      Heatmap of Hedgehog 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 Hedgehog Signaling Data
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      Principal Components of Hedgehog 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.

  • Heatmaps
  • PCA

    • Heatmap of Hippo Pathway Data
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      Heatmap of Hippo 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 Hippo Pathway Data
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      Principal Components of Hippo 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.

  • Heatmaps
  • PCA

    • Heatmap of Hypoxia Data
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      Heatmap of Hypoxia 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 Hypoxia Data
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      Principal Components of Hypoxia 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.

  • Heatmaps
  • PCA

    • Heatmap of Inflammasome Data
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      Heatmap of Inflammasome 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 Inflammasome Data
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      Principal Components of Inflammasome 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.

  • Heatmaps
  • PCA

    • Heatmap of Insulin Resistance Data
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      Heatmap of Insulin Resistance 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 Insulin Resistance Data
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      Principal Components of Insulin Resistance 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.

  • Heatmaps
  • PCA

    • Heatmap of Insulin Signaling Data
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      Heatmap of Insulin 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 Insulin Signaling Data
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      Principal Components of Insulin 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.

  • Heatmaps
  • PCA

    • Heatmap of M1 Activation Data
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      Heatmap of M1 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 M1 Activation Data
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      Principal Components of M1 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.

  • Heatmaps
  • PCA

    • Heatmap of M2 Activation Data
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      Heatmap of M2 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 M2 Activation Data
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      Principal Components of M2 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.

  • Heatmaps
  • PCA

    • Heatmap of MAPK Cell Stress Data
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      Heatmap of MAPK Cell Stress 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 MAPK Cell Stress Data
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      Principal Components of MAPK Cell Stress 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.

  • Heatmaps
  • PCA

    • Heatmap of MHC Class II Antigen Presentation Data
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      Heatmap of MHC Class II Antigen Presentation 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 MHC Class II Antigen Presentation Data
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      Principal Components of MHC Class II Antigen Presentation 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.

  • Heatmaps
  • PCA

    • Heatmap of Myofibroblast Regulation Data
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      Heatmap of Myofibroblast Regulation 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 Myofibroblast Regulation Data
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      Principal Components of Myofibroblast Regulation 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.

  • Heatmaps
  • PCA

    • Heatmap of NF-kb Data
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      Heatmap of NF-kb 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 NF-kb Data
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      Principal Components of NF-kb 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.

  • Heatmaps
  • PCA

    • Heatmap of Neutrophil Degranulation Data
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      Heatmap of Neutrophil Degranulation 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 Neutrophil Degranulation Data
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      Principal Components of Neutrophil Degranulation 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.

  • Heatmaps
  • PCA

    • Heatmap of Notch Data
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      Heatmap of Notch 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 Notch Data
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      Principal Components of Notch 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.

  • Heatmaps
  • PCA

    • Heatmap of Oxidative Stress Data
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      Heatmap of Oxidative Stress 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 Oxidative Stress Data
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      Principal Components of Oxidative Stress 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.

  • Heatmaps
  • PCA

    • Heatmap of PDGF Signaling Data
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      Heatmap of PDGF 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 PDGF Signaling Data
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      Principal Components of PDGF 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.

  • Heatmaps
  • PCA

    • Heatmap of PI3K-Akt Data
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      Heatmap of PI3K-Akt 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 PI3K-Akt Data
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      Principal Components of PI3K-Akt 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.

  • Heatmaps
  • PCA

    • Heatmap of PPAR Signaling Data
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      Heatmap of PPAR 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 PPAR Signaling Data
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      Principal Components of PPAR 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.

  • Heatmaps
  • PCA

    • Heatmap of Phagocytic Cell Function Data
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      Heatmap of Phagocytic Cell Function 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 Phagocytic Cell Function Data
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      Principal Components of Phagocytic Cell Function 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.

  • Heatmaps
  • PCA

    • Heatmap of Platelet Degranulation Data
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      Heatmap of Platelet Degranulation 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 Platelet Degranulation Data
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      Principal Components of Platelet Degranulation 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.

  • Heatmaps
  • PCA

    • Heatmap of Programmed Cell Death Data
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      Heatmap of Programmed Cell Death 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 Programmed Cell Death Data
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      Principal Components of Programmed Cell Death 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.

  • Heatmaps
  • PCA

    • Heatmap of Proteotoxic Stress Data
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      Heatmap of Proteotoxic Stress 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 Proteotoxic Stress Data
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      Principal Components of Proteotoxic Stress 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.

  • Heatmaps
  • PCA

    • Heatmap of SASP Data
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      Heatmap of 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 SASP Data
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      Principal Components of 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.

  • Heatmaps
  • PCA

    • Heatmap of TGF-beta Data
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      Heatmap of TGF-beta 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 TGF-beta Data
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      Principal Components of TGF-beta 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.

  • Heatmaps
  • PCA

    • Heatmap of TLR Signaling Data
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      Heatmap of TLR 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 TLR Signaling Data
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      Principal Components of TLR 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.

  • Heatmaps
  • PCA

    • Heatmap of Th1 Differentiation Data
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      Heatmap of Th1 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 Th1 Differentiation Data
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      Principal Components of Th1 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.

  • Heatmaps
  • PCA

    • Heatmap of Th17 Differentiation Data
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      Heatmap of Th17 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 Th17 Differentiation Data
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      Principal Components of Th17 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.

  • Heatmaps
  • PCA

    • Heatmap of Th2 Differentiation Data
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      Heatmap of Th2 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 Th2 Differentiation Data
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      Principal Components of Th2 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.

  • Heatmaps
  • PCA

    • Heatmap of Type I Interferon Data
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      Heatmap of Type I Interferon 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 Type I Interferon Data
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      Principal Components of Type I Interferon 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.

  • Heatmaps
  • PCA

    • Heatmap of Type II Interferon Data
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      Heatmap of Type II Interferon 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 Type II Interferon Data
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      Principal Components of Type II Interferon 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.

  • Heatmaps
  • PCA

    • Heatmap of Wnt Data
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      Heatmap of Wnt 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 Wnt Data
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      Principal Components of Wnt 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.

  • Heatmaps
  • PCA

    • Heatmap of mTOR Data
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      Heatmap of mTOR 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 mTOR Data
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      Principal Components of mTOR 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.