https://github.com/broadinstitute/ssgsea2.0

Single sample Gene Set Enrichment analysis (ssGSEA) and PTM Enrichment Analysis (PTM-SEA)

https://github.com/broadinstitute/ssgsea2.0

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Single sample Gene Set Enrichment analysis (ssGSEA) and PTM Enrichment Analysis (PTM-SEA)

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ssGSEA2.0/PTM-SEA
Resources for gene-centric single sample Gene Set Enrichment
Analysis (ssGSEA) of gene expression data (e.g. mRNAs,
proteins) and site-centric PTM Signature Enrichment Analysis
(PTM-SEA) [1] of phosphoproteomics data sets using the PTM signatures
database (PTMsigDB) [1].

Disclaimer
The primary purpose of this repository is to supplement our manuscript
in which we describe PTM-SEA and PTMsigDB. While
ssGSEA2.0 presents an updated version of the original ssGSEA R
implementation, we want to acknowledge that this is not the
primary repository for ssGSEA. The official codebase for ssGSEA
can be found here, and the
official GenePattern module to perform ssGSEA can be accessed here.


ssGSEA 2.0
This is an updated version of the original ssGSEA [2,3]
R-implementation. Depending on the input dataset and chosen database
(gene sets or PTM signatures), the software performs either ssGSEA or
PTM-SEA, respectively. The Molecular Signatures Database (MSigDB) [4]
provides a large collection of curated gene sets. Gene sets are stored
as plain text in GMT
format. A current version of MSigDB gene set collections can be found in
the db/msigdb subfolder. MSigDB gene sets are realeased
under Creative
Commons Attribution 4.0 International License. The license terms can
be found in thedb/msigdb folder.
File formats supported by ssGSEA2.0/PTM-SEA are Gene Cluster Text GCT
v1.2 or GCT
v1.3 files. Morpheus
provides a convenient way to convert your data tables into GCT
format.
For more information about the GSEA method and MSigDB please visit http://software.broadinstitute.org/gsea/.


PTMsigDB v2.0.0
Please check out our new website for PTMsigDB. We have
updated PTMsigDB to
version v2.0.0 in which we provide better and more consistent annotation
of each PTM site. We have also inlcuded a disease category
comprising of signatures associated to certain diseases curated from the
table Disease-associated_sites available at PhosphoSitePlus
(PSP) [5].
The PTM signatures database (PTMsigDB) is a database comprised of
modification site-specific signatures of perturbations, kinase
activities and signaling pathways curated from more than 2,500
publications which provides the foundation to perform PTM-SEA. A unique
advantage of PTMsigDB over other pathway databases is the annotation of
each PTM site with its reported direction of change upon a specific
perturbation or signaling event which is incorporated into the scoring
scheme of PTM-SEA. The foundation of PTMsigDB is PhosphoSitePlus
(PSP) [5], a comprehensive systems biology resource for PTMs, which
provides high-quality curation and annotation of PTMs at the individual
residue level. A collection of PTM sites, whose levels are collectively
regulated in a curated pathway or upon a perturbation, are defined as a
signature set. Signature sets in PTMsigDB can be separated into
different categories: 1) Perturbation signatures
derived from treatment of cells with perturbagens such as small
molecules or growth factors; 2) Signature sets of molecular
signaling pathways; 3) Kinase-substrate
signatures; and 4) Disease-associated signature
sets.
To ensure a high degree of compatibility to phosphorylation datasets
generated by different software packages and searched against different
protein sequence databases, PTMsigDB represents signatures using three
different identifiers to represent phosphorylation sites: 1) PSP
site group ID; 2) UniProt-centric ID; 3)
Flanking sequence (Table 1). While the PSP site group
ID provides an unambiguous representation of PTM sites within protein
families and across species [5], using this type of identifier restricts
the analysis to PTM sites present in PSP. We generally recommend to
using the flanking sequence as site identifier, since these are more
invariant to updates made to protein sequence databases.










Database format
Site accession
Example in PTMsigDB
Example in dataset
Download




UniProt-centric
Uniprot_acc;site-type;direction
Q06609;Y315-p;u
Q06609;Y315-p
human
mouse
rat


Flanking sequence
+/-7aa flanking seq-type;direction
ETRICKIYDSPCLPE-p;u
ETRICKIYDSPCLPE-p
human
mouse
rat


PSP site group id
site_grp_id-type;direction
448324-p;u
448324-p
human
mouse
rat



Table 1: PTM site representation in PTMsigDB. The direction of change
for a PTM site in a signature is indicated by ;u (up-regulation) or ;d
(down-regulation). Please note that the annotation of directionality is
a feature of PTMsigDB (column: Example in PTMsigDB) and must
not be included when generating compatible site identifier for a
particular dataset (column: Example in dataset).


PTM-SEA
PTM-Signature Enrichment Analysis (PTM-SEA) is a modified version of
ssGSEA to perform site-specific signature analysis by scoring PTMsigDB’s
bi-directional signature-sets. The input to PTM-SEA is a single
site-centric data matrix, m, stored in GCT
v1.2 or GCT
v1.3 format and PTM signatures database (PTMsigDB). Each row in
m represents a single phosphorylation site confidently
localized to a specific amino acid residue, with measured abundances
across samples specified in columns in m. Multiple
phosphorylation sites detected on the same peptide have to be converted
into separate site-specific entities for every site. While some
proteomics software packages, such as MaxQuant [6], readily produce
single site-centric PTM reports, the use of other software packages
might require additional preprocessing steps.


How can I use these tools?
ssGSEA2.0/PTM-SEA can be run on a local PC/MAC in R or RStudio. In
addition, ssGSEA2.0/PTM-SEA can be access on Broad’s public GenePattern
[7] server. Below we provide instructions how to run
ssSGEA2.0/PTM-SEA.

Example dataset
We provide an example dataset that can be used to test PTM-SEA. The
dataset is based on Supplemental Table 6 in [1].
Single
site-centric phosphoproteome dataset
PTMsigDB


GenePattern
GenePattern
is a powerful platform to deploy and run software or entire analysis
pipelines in a web browser [7]. We have implemented ssGSEA2.0/PTM-SEA as
GenePattern module which can be accessed at the link below. Please note
that access to the public GenePattern server requires a free
registration.
PTM-SEA in GenePattern: https://tinyurl.com/PTM-SEA-GP


R-GUI / RStudio
The script ssgsea-gui.R requires little or no knowledge
of R or on how to use the command line. Input files and databases can be
specified via Windows file dialogs that will be automatically invoked.
The first dialog lets you choose a folder containing input files in GCT
v1.2 or GCT
v1.3 format. The script loops over all GCT files in this directory
and runs ssGSEA on each file separately. The second dialog window lets
the user choose one or multiple gene set databases in GMT
format such as MSigDB. A
current version of MSigDB databases can be found in the db
subfolder.

Windows OS
To run the script source it into a running R-session.

RStudio:
open the file and press ‘Source’ in the upper right part of the editor
window
R-GUI:
drag and drop this file into an R-GUI window



iOS/MAC
In order to invoke file dialogs as decribed above, the XQuartz X Window System is required.
Once installed ssgsea-gui.R can be sourced into an R
session.



R Package
For use in R, Nicole Gay has created an R package that
incorporates ssGSEA2.0, along with required dependencies for both R 3.6
and R >= 4.0. Instruction for use of the library can be found along
with the package on GitHub.


Command line
For integration of ssGSEA2.0/PTM-SEA into your own analysis pipelines
we recommend to use the ssgsea-cli.R script which has been
successfully tested on Windows, Mac and Linux OS. Please see
ssgsea-cli.R --help for instructions.



Misc

ssGSEA2.0/PTM-SEA parameters
Other parameters for ssGSEA/PTM-SEA can be altered inside the
parameters section in ssgsea-gui.R or as arguments on the
command line. The default parameters have been choosen carefully and
should provide reliable results for most use-case scenarios.


Changes to the original ssGSEA R-implementation
Original code written by Pablo Tamayo. Adapted with additional
modifications by D. R. Mani and Karsten Krug. Adaptions include:

support of multiple CPU cores (doParallel
R-package)
support of GCT v1.3 format using functions from cmapR
improved handling of missing values
scoring of directional gene sets (PTMsigDB)
basic error handling
improvements in runtime performance
additional output files like rank plots and parameter files




License
License Agreement for MSigDB v6.0 and above can be found here.


References

Krug, K., Mertins, P., Zhang, B., Hornbeck, P., Raju, R., Ahmad,
R., . Szucs, M., Mundt, F., Forestier, D., Jane-Valbuena, J.,
Keshishian, H., Gillette, M. A., Tamayo, P., Mesirov, J. P., Jaffe, J.
D., Carr, S. A., Mani, D. R. (2019). A curated resource for
phosphosite-specific signature analysis, Molecular &
Cellular Proteomics (in Press). http://doi.org/10.1074/mcp.TIR118.000943
Barbie, D. A., Tamayo, P., Boehm, J. S., Kim, S. Y., Susan, E.,
Dunn, I. F., . Hahn, W. C. (2010). Systematic RNA interference
reveals that oncogenic KRAS- driven cancers require TBK1,
Nature, 462(7269), 108-112. https://doi.org/10.1038/nature08460
Abazeed, M. E., Adams, D. J., Hurov, K. E., Tamayo, P.,
Creighton, C. J., Sonkin, D., et al. (2013). Integrative
Radiogenomic Profiling of Squamous Cell Lung Cancer. Cancer
Research, 73(20), 6289-6298. http://doi.org/10.1158/0008-5472.CAN-13-1616
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert,
B. L., Gillette, M. A., et al. (2005). Gene set enrichment
analysis: a knowledge-based approach for interpreting genome-wide
expression profiles. Proceedings of the National Academy of
Sciences of the United States of America, 102(43), 15545-15550. http://doi.org/10.1073/pnas.0506580102
Hornbeck, P. V., Zhang, B., Murray, B., Kornhauser, J. M.,
Latham, V., & Skrzypek, E. (2015). PhosphoSitePlus, 2014:
mutations, PTMs and recalibrations. Nucleic Acids Research,
43(D1), D512-D520. https://doi.org/10.1093/nar/gku1267
Cox, J., & Mann, M. (2008). MaxQuant enables high
peptide identification rates, individualized p.p.b.-range mass
accuracies and proteome-wide protein quantification. Nature
Biotechnology, 26(12), 1367-1372. https://doi.org/10.1038/nbt.1511
Reich, M., Liefeld, T., Gould, J., Lerner, J., Tamayo, P., &
Mesirov, J. P. (2006). GenePattern 2.0. Nature
Genetics, 38(5), 500-501. https://doi.org/10.1038/ng0506-500

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