The Lung-Tumor Microenvironment Interactome

Gene Of Interest:

Limit search to:

Expression Correlation Analysis

Display correlations between expression
cells
with all other genes in each cell type.


GSEA Analysis

Show enrichment of gene sets in
cells based on

Select gene sets for GSEA:

Pearson Correlation Heatmap

(Click on a cell to view a scatterplot and significance)

Lung ADENO GSEA Results

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Lung SCC GSEA Results

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Lung Cancer Single Cell Atlas

Data from Lambrechts et al. Nature Medicine 2018

Select Gene To Display:

About LungTMI


LTMI is a tool to explore cross talk in the lung-tumor microenvironment through correlation and functional enrichment analysis. We performed RNA-seq profiling of human primary non-small-cell lung tumors in both bulk and flow-sorted malignant cells, endothelial cells, immune cells, and fibroblasts. We mapped the cell-specific differential expression of prognostically-associated secreted factors and cell surface genes, and computationally reconstructed cross-talk between these cell types.


The Lung-Tumor Microenvironment Interactome

The Lung-Tumor Microenvironment Interactome application is based on analysis from Gentles et al. (20XX). We performed RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, and fibroblasts from human primary non-small-cell lung tumors (Lung Adenocarcinoma, Lung Squamous Cell Carcinoma). We mapped the cell-specific differential expression of prognostically-associated secreted factors and cell surface genes, and computationally reconstructed cross-talk between these cell types. This application allows users to complete the same analyses on custom genes and cell types. The following tutorial reproduces one of the analyses from Gentles et al.


1. Select a gene of interest

  • Select GREM1 by searching for it in the sidebar. Observe its characteristics and expression in different cell types.
  • Not all genes are initially displayed in the dropdown menu. To select your gene of interest, search for its prefix and it will be displayed as an option.
  • To filter the list of displayed genes based on prognistic, surfacome, and expression data, select criteria using the checkboxes.
  • Notably, GREM1 is prognostic in Adenocarcinoma, expressed on the surfaceome, and is expressed highly on one cell type (Fibroblasts).


  • 2. Select a cell type

  • Select Fibroblasts to calculate the pearson correlations between GREM1 expression in fibroblasts and every other gene/celltype pair.
  • Observe the heatmap of expression correlations and sort by a cell type to find the genes that are most correlated with GREM1 in fibroblasts.
  • Click on a heatmap cell to view a scatterplot of the expressions and significance of the correlation.
  • To download a .tsv file of all correlations for the gene of interest and cell type, click the 'download tsv' button.


  • 3. Run GSEA analysis

  • Select a comparison celltype for GSEA (Malignant)
  • We will select the malignant population to determine which gene sets and biological pathways are enriched in malignant cells when GREM1 is overexpressed in fibroblasts.
  • Select one of the MSIGDB gene set collections. We select the msigdb c2 + c5 bioprocess category as this was used in Gentles et al.
  • Navigate to the GSEA tab, and wait for the analysis to complete
  • Observe the significantly enriched gene sets in adeno and squamous carcinoma
  • To download the full GSEA results, click the 'download tsv' button
  • GEO Data Accession

    Data can be downloaded at GEO Accession: GSE111907
    Access PRECOG Data Here

    Contact Information

    For questions about the analysis performed on this site, contact:

    Andrew Gentles

    Assistant Professor (Research) of Medicine (Biomedical Informatics) and, by courtesy, of Biomedical Data Science

    Stanford University School of Medicine, Stanford CA 94305

    andrewg@stanford.edu


    For questions about the publication associated with this site, contact:

    Sylvia K. Plevritis

    Professor of Biomedical Data Science and of Radiology

    Stanford University School of Medicine, Stanford CA 94305

    sylvia.plevritis@stanford.edu


    Max Diehn

    Associate Professor of Radiation Oncology (Radiation Therapy)

    Stanford University School of Medicine, Stanford CA 94305

    diehn@stanford.edu


    For questions about the code behind this site, contact:

    Armon Azizi
    Terms of Use