The Library of Integrative Network-based Cellular Signatures (LINCS) is an NIH Common Fund program. The idea is to perturb different types of human cells with many different types of perturbations such as: drugs and other small molecules; genetic manipulations such as knockdown or overexpression of single genes; manipulation of the extracellular microenvironment conditions, for example, growing cells on different surfaces, and more. These perturbations are applied to various types of human cells including induced pluripotent stem cells from patients, differentiated into various lineages such as neurons or cardiomyocytes. Then, to better understand the molecular networks that are affected by these perturbations, changes in level of many different variables are measured including: mRNAs, proteins, and metabolites, as well as cellular phenotypic changes such as changes in cell morphology. The BD2K-LINCS Data Coordination and Integration Center (DCIC) is commissioned to organize, analyze, visualize and integrate this data with other publicly available relevant resources. In this course we briefly introduce the DCIC and the various Centers that collect data for LINCS. We then cover metadata and how metadata is linked to ontologies. We then present data processing and normalization methods to clean and harmonize LINCS data. This follow discussions about how data is served as RESTful APIs. Most importantly, the course covers computational methods including: data clustering, gene-set enrichment analysis, interactive data visualization, and supervised learning. Finally, we introduce crowdsourcing/citizen-science projects where students can work together in teams to extract expression signatures from public databases and then query such collections of signatures against LINCS data for predicting small molecules as potential therapeutics.
The Harmonizome Concept
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來自 Icahn School of Medicine at Mount Sinai 的課程
Big Data Science with the BD2K-LINCS Data Coordination and Integration Center
10 個評分
Icahn School of Medicine at Mount Sinai
10 個評分
從本節課中
The Harmonizome
This module describes a project that integrates many resources that contain knowledge about genes and proteins. The project is called the Harmonizome, and it is implemented as a web-server application available at: http://amp.pharm.mssm.edu/Harmonizome/
與講師見面
Avi Ma’ayan, PhDDirector, Mount Sinai Center for Bioinformatics
Professor, Department of Pharmacological Sciences
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So one of the things that we are supposed to do as the BD2K-LINCS Data Coordination
and Integration Center, is to find solutions for
putting all the big data that is collected in biomedical research and
specifically molecular data, at the genome wide scale, and
trying to make sense of it, and developing tools that can be used
to further extract knowledge from all this data.
So we made great strides in this direction, and
recently published two large reviews that lay out our plan
on how we can leverage all of this data and put it together.
So in the first review, we listed most comprehensive resources
of experimental data that is collected in the field, and
categorized the data into seven subsections.
The first is Drug and Gene Knockdown, followed by Genome-Wide Expression,
and this is what the LINCS Program is all about.
The Connectivity Map that we discussed,
as well as the work that is done by many of the LINCS centers,
as well as the data that is deposited in the Gene Expression Omnibus.
The next category is Transcription Factors and
Histone Modification Profiled by ChlP-Seq.
There are two large scale NIH projects called ENCODE and
Epigenomics Road Map Project that systematically use
ChlP-Seq analysis, to profile the binding of proteins
onto the DNA in different human cell types and conditions.
The next type of data is cell viability data after single gene knockdowns,
or drug pertubations of many different human cell.
The next type of data is knockout, or
mutation data and their association with disease.
We now have more and more gene expression data from individual patients,
and different tissues of those patients.
The GTEx project provide such data combined with genomic sequencing
project like the cancer genome atlas, provide large collection
of different data at different regulatory layers from cancer patients.
Including genomics, transcriptomics, and
proteomics from individual tumors across many different types of cancers for
large cohorts of hundreds of patients for each cancer.
There is also accumulated knowledge of protein, protein interactions,
metabolic and self signalling pathways that are continually extracted from
the literature, or are now being able to be profiled with high content screens.
Finally, there is accumulated knowledge about drugs and
toxic chemicals that cause adverse event and toxicity.
Those provide links between small molecules and the human phenotype.
All this data can be converted to what we call attribute tables,
networks, single-entity node networks, for example, gene,
gene association networks, or functional association networks.
Gene set libraries that we will discuss when we discuss enrichment analysis.
Bi-partite graphs that connect genes to entities,
and those are just different views of the same data.
So by collecting many of those data sets and obstructing them to those attribute
tables, bi-partite graph, gene sets and single node networks,
the challenge of integrating all of those resources becomes easier and possible.
The ensemble of bi-partite graphs, gene sets and networks,
allow us to form connection between biological entities
that typically are not identifiable by standard methods.
And those could be of great interest to biomedical researchers,
because they can identify interesting relationships
that are not obviously found when you're looking at one data set alone.
Graph theory algorithms and machine learning methods can now be applied
to draw noble inferences from this integrated data.
So the data integration opens an opportunity to discover new connections
among drugs, genes, diseases, tissues, and other biological entities,
and this is becoming gradually more clear as we advance in the course.
So let's look at those data structures that can be used for
this unification of representation and analysis.
So the first data structures that is relatively obvious are bipartite graphs.
In this data structure, you have two types of nodes.
In our case, those are genes and their biological properties,
or functions, that are associated with those genes.
The most typical way that data is represented in biology is
through attribute tables.
And in most cases, we have genes as the rows, and then the different conditions
that the biological conditions that measure the state of those genes,
or those variables, either gene expression or protein expression,
can be the columns or the conditions, and this could be measurements
of different cell types, different drugs applied to those cell types.
And the numbers in those tables don't have to be zeroes and one, they can be
overall absolute change, or differential change when compared to the control.
We can always put a threshold, and decide when there is a connection.
In other cases, the binary representation is the only possibility.
This is for example, if we knock a gene in a mouse, and
we look at the possible phenotypes that the knockout can cause.
The representation of set libraries is
basically the same, and it's just a transformation of the data,
where now the terms of each library, or the label of each library,
is the common function for the genes, and the genes are members of each set.
The label for example, could be a pathway and
the genes in the pathway comprise the actual set.
In this example, we show how this can be transposed where the genes are the labels,
and the terms can become the set members.
From this data we can also construct a network.
So if the nodes in the network are the genes,
they can be connected based on their common shared attributes,
and these are called functional association networks.
The attributes can also be connected to form attribute attribute networks and
these for example, can be a network of disease terms.
An adjacency matrix is a representation of a network using a matrix.
So a general matrix and set operation can be used to analyze, manipulate,
and integrate those datasets conforming to these data structures.
So, as we've seen throughout the course, data analysis and
integration methods can be used on those data structures and
these include clustering, classification, or machine learning,
bench marking, graphical models, or we can detect biases.
These topics are reviewed in general in more detail throughout the course.
One thing that we haven't spoke about in the course is those skewed
biases in the representation of genes, and
in one of the reviews we show that different type of data
has different types of biases where genes are more commonly represented.
So if we look at a literature based data, the most well studied genes like
P53 have many references, citations and connections.
Different genes are very commonly identified
when we apply ChIP-seq methods.
The same is true of gene expression data, or proteomics.
We are debated to care links, DCIC, already integrated and
analyzed many of those data set, and we now have a database called
the Harmonizome, and it can be accessed on this website.
So from this website, you can download over a hundred process data set
extracted from over 60 publicly available resources.
In the rest of our API presentations,
you will learn how to access this resource programatically.
We hope that you can find this resource useful also for
applying various data analysis on the process data sets to shorten
processing time, and accelerate big data to knowledge discoveries.
In the next three lectures, Andrew Rollard from our lab,
will describe how he processed and analyzed several of those
resources in order to construct the Harmonizome database.
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