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

Gene Signature Lab, Comp. Genomics Course, IGB 607

Outline

• A couple of quick reminders: CMAP & LINCS

• Interacting with Big Omics Data using iLINCS (Medvedovic et al.)• Part I: deriving and interpreting genomic signatures

• P53

• ER

• Part II: searching for drug targets and drugs

• Exploring other LINCS-related tools (Enrichr, L1000CDS2, Ma’ayan

et al.)

Gene Signature Lab, Comp. Genomics Course, IGB 607

LINCS: Extending Connectivity Map

J Lamb et al. Science 2006;313:1929-1935

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

Gene Signature Lab, Comp. Genomics Course, IGB 607

iLINCS Demo I: p53 Signature in 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 ‘All Data sets’ and ‘TCGA’ (click on ‘Choose’ button to the right); select the 3rd data set from the top (919 BRCAs)

9

Getting started …

iLINCS.org, Mario Medvedovic et al., University of Cincinnati

Exploratory analysis …

DownloadData

Explore Heatmap

iLINCS.org, Mario Medvedovic et al., University of Cincinnati

Note that NAs can be effectively classified

iLINCS.org, Mario Medvedovic et al., University of Cincinnati

Let us generate p53 signature …

Gene Signature Lab, Comp. Genomics Course, IGB 607

P53 signature can be used to reclassify wt and mutants …

JM - http://folding.chmcc.org 14

Work around to generate the correct heatmap:Use the signature to re-analyze the same data set.

Work around to generate the correct heatmap …

Work around to generate the correct heatmap …

Avi Ma’yan et al., Mount Sinai School of Medicine

Gene Signature Lab, Comp. Genomics Course, IGB 607

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‘Big’ p53 signature

Dataset Analysis Workflow

LINCS RNA-seq dataset

TCGA RNA-seq BC datasetConnected TF binding and L1000 KD

signaturesLINCS RPPA dataset

Enrichment analysis via Enricher

Pathway analysis vis SPIA algorithm

Differential gene expression signature

Small molecule CD signatures L1000CDS2

iLINCS.org, Mario Medvedovic et al., University of Cincinnati

iLINCS Datasets

3,600 Datasets

TCGA Transcriptomics

ENCODE TF Binding Data

P100 + GCP Proteomics

…

iLINCS.org, Mario Medvedovic et al., University of Cincinnati

Signatures WorkflowFinding signatures

Analyze

Connected Signatures

iLINCS.org, Mario Medvedovic et al., University of Cincinnati

iLINCS Signatures

iLINCS.org, Mario Medvedovic et al., University of Cincinnati

Genes WorkflowFinding genes

Signatures workflow

Dataset workflow

iLINCS.org, Mario Medvedovic et al., University of Cincinnati

Gene Signature Lab, Comp. Genomics Course, IGB 607

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)

• Click ‘Create signature’

• Click ‘Enrichr’ to perform enrichment analysis

Going back to the page with ER signature …

Gene Signature Lab, Comp. Genomics Course, IGB 607

Avi Ma’yan et al., Mount Sinai School of Medicine

Gene Signature Lab, Comp. Genomics Course, IGB 607

iLINCS Demo III: Reversing ER Signature

1

2

3

Searching by Gene Knockdown Signatures

Group Analysis of Raloxifen Signatures

Concordant vs. Discordant Signatures

Searching for Novel ER(-pathway) Inhibitors (concordance>0.4)

Caveats: Potentially Small Overlap with L1000 Gene Set for User Defined Signatures

Caveats: Current Sparse Cube

http://lincsproject.org/

Transcriptomics

HMS LINCS

Proteomics

Microenvironment

DTox

NeuroLINCS

Gene Signature Lab, Comp. Genomics Course, IGB 607

Take home messages:

i) Potential gold mine for hypothesis generation and mechanistic insights

ii) Use with utmost caution, do not over-interpret, validateiii) Please be somewhat patient with the tools – they keep

getting better …

For the rest f the lab, try to reproduce as much as possible one more time.