Gene Signature Lab: Exploring integrative LINCS (iLINCS) Data and Signatures Analysis Portal & Other LINCS Resources Jarek Meller, PhD BD2K-LINCS Data Coordination and Integration Center University of Cincinnati Gene Signature Lab, Comp. Genomics Course, IGB 607
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Gene Signature Lab:Exploring integrative LINCS (iLINCS) Data and Signatures
Analysis Portal & Other LINCS Resources
Jarek Meller, PhDBD2K-LINCS Data Coordination and Integration CenterUniversity of Cincinnati
Negative correlation with “disease transcriptional
signature”
Potential of the drug to “reverse” the disease
process
LINCS Cube
Perturbations
cell
type
s
Cancer cell lines
iPS cells
Primary cells
Chemical perturbagens (~30,000 x doses)
Genetic perturbations (~30,000 x shRNAs)
Microenvironment perturbations
Disease
Transcriptomic (L1000, RNA-seq)
Proteomic
Phosphoproteomic
Morphoplogical
Proliferation, apoptosis, …
http://LincsProject.org
• NOTE that small molecules with negatively correlating signatures with respect to an individual tumor signature (characterized by some mutations and some up- and down-regulated genes) could potentially be used to identify drugs to treat that particular tumor!
• This can be viewed as ‘reversing’ the signature of the tumor
• This and other applications can be greatly facilitated by highly integrative and intuitive tools that enable seamless interaction with Big Omics Data, such as LINCS iLINCS
5
Towards Using CMAP/LINCS as Resources for Personalized Precision Medicine
iLINCS: Linking Datasets and Signatures with Online Analysis
Analyzing and mining perturbation and disease signatures
Constructing and analyzing signatures from transcriptomics and proteomics datasets
What are my genes/proteins doing in other datasets?
iLINCS.org, Mario Medvedovic et al., University of Cincinnati
iLINCS.org, Mario Medvedovic et al., University of Cincinnati
iLINCS Team
iLINCS.org, Mario Medvedovic et al., University of Cincinnati
iLINCS Demo II: ER Signature in Cell Lines vs. Breast Tumors
• Go to http://www.ilincs.org/ilincs/
• Select ‘Datasets’ workflow by either clicking on ‘Datasets’ in the top bar or data sets icon below icon
• Select ‘LINCS Data sets’ and select the last data set ‘Oregon Health Sciences 54 mRNA-seqsamples from cell lines’ (click on ‘Analyze’ button to the right)
• Click on ‘Generate a Signature’
• Select ‘Grouping variable’ as ER
• Define groups as ‘+’ and ‘-’ (ER positive and ER negative cell lines)
• Click ‘Create signature’
• Select ‘Use differentially expressed genes to analyze another set’ (work around) and choose the same Oregon Health Sciences data set and select ‘Statistical analysis of genes’ and select ER again as the grouping variable, open heatmap
• Do the same, but this time find the TCGA BRCA data set and generate heatmap
iLINCS.org, Mario Medvedovic et al., University of Cincinnati
Cell lines cluster largely by ER status; unassigned cell lines can be predicted to
have either negative or positive ER status.
Note that genes were selected to make that happen – this is not a truly
unsupervised approach.
iLINCS.org, Mario Medvedovic et al., University of Cincinnati
Going back to the page with ER signature:Step-by-step instructions one more time …
• Go to http://www.ilincs.org/ilincs/
• Select ‘Datasets’ workflow by either clicking on ‘Datasets’ in the top bar or data sets icon below icon
• Select ‘LINCS Data sets’ and select the last data set ‘Oregon Health Sciences 54 mRNA-seq samples from cell lines’ (click on ‘Analyze’ button to the right)
• Click on ‘Generate a Signature’
• Select ‘Grouping variable’ as ER
• Define groups as ‘+’ and ‘-’ (ER positive and ER negative cell lines)