Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Minor tweaks to paper. #164

Merged
merged 1 commit into from
May 8, 2021
Merged

Minor tweaks to paper. #164

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 6 additions & 6 deletions inst/paper.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -39,15 +39,15 @@ cancer and other diseases. These methods have already been applied to a number o
indeed several integrative disease
studies [@zhang2014proteogenomic; @cancer2014comprehensive; @ren2016integration; @hassan2020integration]. In addition to
the genome-wide measurements of different genetic characterizations, the growing public knowledge-base of functional
annotations [@rnacentral2016rnacentral, @derrien2012gencode], experimentally-verified
interactions [@chou2015mirtarbase, @yuan2013npinter, @chou2017mirtarbase, @oughtred2019biogrid], and gene-disease
associations [@huang2018hmdd, @pinero2016disgenet, @chen2012lncrnadisease] also provides the prior-knowledge essential
annotations [@rnacentral2016rnacentral; @derrien2012gencode], experimentally-verified
interactions [@chou2015mirtarbase; @yuan2013npinter; @chou2017mirtarbase; @oughtred2019biogrid], and gene-disease
associations [@huang2018hmdd; @pinero2016disgenet; @chen2012lncrnadisease] also provides the prior-knowledge essential
for system-level analyses. Leveraging these data sources allow for a systematic investigation of disease mechanisms at
multiple molecular and regulatory layers; however, such task remains nontrivial due to the complexity of multi-omics
data.

While researchers have developed several mature tools to access or analyze a particular single omic data
type [@wolf2018scanpy, @stuart2019integrative], the current state of integrative data platforms for multi-omics data is
type [@wolf2018scanpy; @stuart2019integrative], the current state of integrative data platforms for multi-omics data is
lacking due to three reasons. First, pipelines for data integration carry out a sequential tasks that does not process
multi-omics datasets holistically. Second, the vast size and heterogeneity of the data poses a challenge on the
necessary data storage and computational processing. And third, implementations of data pipelines are close-ended for
Expand Down Expand Up @@ -79,7 +79,7 @@ elsewhere for down-stream analysis.
# The OpenOmics library

OpenOmics consists of two core modules: multi-omics integration and annotation interface. An overview visualization of
the OpenOmics system architecture is provided in Figure \autoref{architecture}.
the OpenOmics system architecture is provided in \autoref{architecture}.

## Multi-omics integration

Expand Down Expand Up @@ -157,7 +157,7 @@ Table 1: Public annotation databases and availability of data in the Human genom

# System design

This chapter describes the various implementation details behind the scalable processing and efficient data storage, and
This section describes the various implementation details behind the scalable processing and efficient data storage, and
the design choices in the development operations.

While the in-memory Pandas dataframes utilized in our data structures are fast, they have size and speed limitations
Expand Down