signatureSearch: Environment for Gene Expression Searching Combined with Functional Enrichment Analysis
Introduction
Background
The signatureSearch package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods (Duan et al. 2020). These utilities are useful for studying the effects of genetic, chemical and environmental perturbations on biological systems. Specifically, in drug discovery they can be used for identifying novel modes of action (MOA) of bioactive compounds from reference databases such as LINCS containing the genome-wide GESs from tens of thousands of drug and genetic perturbations (Subramanian et al. 2017). A typical GES search (GESS) workflow can be divided into two major steps (Figure 1). First, GESS methods are used to identify perturbagens such as drugs that induce GESs similar to a query GES of interest. The queries can be drug-, disease- or phenotype-related GESs. Since the MOAs of most drugs in the corresponding reference databases are known, the resulting associations are useful to gain insights into pharmacological and/or disease mechanisms, and to develop novel drug repurposing approaches. Second, specialized functional enrichment analysis (FEA) methods using annotations systems, such as Gene Ontologies (GO), pathways or Disease Ontologies (DO), have been developed and implemented in this package to efficiently interpret GESS results. The latter are usually composed of lists of perturbagens (e.g. drugs) ranked by the similarity metric of the corresponding GESS method. Finally, network reconstruction functionalities are integrated for visualizing the final results, e.g. in form of drug-target networks. Figure 1 illustrates the major steps of a typical signature search workflow. For each GESS and FEA step, several alternative methods have been implemented in signatureSearch to allow users to choose the best possible workflow configuration for their research application. The individual search and enrichment methods are introduced in the corresponding sections of this vignette.
Figure 1: Overview of GESS and FEA methods. GES queries are used to search a perturbation-based GES reference database for perturbagens such as drugs inducing GESs similar to the query. To interpret the results, the GESS results are subjected to functional enrichment analysis (FEA) including drug set and target set enrichment analyses (DSEA, TSEA). Both identify functional categories (e.g. GO terms or KEGG pathways) over-represented in the GESS results. Subsequently, drug-target networks are reconstructed for visualization and interpretation.
Motivation and Design
Integrating the above described GESS and FEA methods into an R/Bioconductor package has several advantages. First, it simplifies the development of automated end-to-end workflows for conducting signature searches for many application areas. Second, it consolidates an extendable number of GESS and FEA algorithms into a single environment that allows users to choose the best selection of methods and parameter settings for a given research question. Third, the usage of generic data objects and classes improves maintainability and reproducibility of the provided functionalities, while the integration with the existing R/Bioconductor ecosystem maximizes their extensibility and reusability for other data analysis applications. Fourth, it provides access to several community perturbation reference databases along with options to build custom databases with support for most common gene expression profiling technologies (e.g. microarrays and RNA-Seq).
Figure 2 illustrates the design of the package with respect to its data containers and methods used by the individual signature search workflow steps. Briefly, expression profiles from genome-wide gene expression profiling technologies (e.g. RNA-Seq or microarrays) are used to build a reference database stored as HDF5 file. Commonly, a pre-built database can be used here that is provided by the associated signatureSearchData package. A search with a query signature against a reference database is initialized by declaring all parameter settings in a qSig search object. Users can choose here one of five different search algorithms implemented by signatureSearch. The nature of the query signature along with a chosen search method defines the type of expression data used for searching. For instance, a search with a query up/down gene set combined with the LINCS search method will be performed on sorted gene expression scores (see left panel in Figure 1). To minimize memory requirements and improve time performance, large reference databases are searched in batches with user-definable chunk sizes. The search results are stored in a gessResult object. The latter contains all information required to be processed by the downstream functional enrichment analysis (FEA) methods, here drug set and target set enrichment analysis (TSEA and DSEA) methods. The obtained functional enrichment results are stored as feaResult object that can be passed on to various drug-target network construction and visualization methods implemented in signatureSearch.
Figure 2: Design of
signatureSearch. Gene expression profiles are stored in a
reference database (here HDF5 file). Prebuilt databases for community
GES collections, such as CMAP2 and LINCS, are provided by the affiliated
signatureSearchData package. The GESS query parameters are
defined in a qSig search object where users can choose among
over five GESS methods (CMAP, LINCS, gCMAP, Fisher, and various
correlation methods). Signature search results are stored in a
gessResult object that can be functionally annotated with
different TSEA (dup_hyperG, mGSEA, mabs) and
DSEA (hyperG, GSEA) methods. The enrichment results
are stored as feaResult object that can be used for drug
target-networks analysis and visualization.
History of GES Databases
Lamb et al. (2006) generated one of the first GES databases called CMAP. Initially, it included GESs for 164 drugs screened against four mammalian cell lines (Lamb et al. 2006). A few years later CMAP was extended to CMAP2 containing GESs for 1,309 drugs and eight cell lines. More recently, a much larger GES database was released by the Library of Network-Based Cellular Signatures (LINCS) Consortium (Subramanian et al. 2017). In its initial release, the LINCS database contained perturbation-based GESs for 19,811 drugs tested on up to 70 cancer and non-cancer cell lines along with genetic perturbation experiments for several thousand genes. The number of compound dosages and time points considered in the assays has also been increased by 10-20 fold. The CMAP/CMAP2 databases use Affymetrix Gene Chips as expression platform. To scale from a few thousand to many hundred thousand GESs, the LINCS Consortium uses now the more economic L1000 assay. This bead-based technology is a low cost, high-throughput reduced representation expression profiling assay. It measures the expression of 978 landmark genes and 80 control genes by detecting fluorescent intensity of beads after capturing the ligation-mediated amplification products of mRNAs (Peck et al. 2006). The expression of 11,350 additional genes is imputed from the landmark genes by using as training data a collection of 12,063 Affymetrix gene chips (Edgar, Domrachev, and Lash 2002). The substantial scale-up of the LINCS project provides now many new opportunities to explore MOAs for a large number of known drugs and experimental drug-like small molecules. In 2020, the LINCS 2017 database is expanded to the beta release, here refer to as LINCS2. It contains >80k perturbations and >200 cell lines and over 3M gene expression profiles. This represents roughly a 3-fold expansion on the LINCS 2017 database. The datasets can be accessed at https://clue.io/releases/data-dashboard.
Terminology
In the following text the term Gene Expression Signatures (GESs) can be composed of gene sets (GSs), such as the identifier sets of differentially expressed genes (DEGs), or various types of quantitative gene expression profiles (GEPs) for a subset or all genes measured by a gene expression profiling technology. Some publications refer with the term GES mainly to GSs, or use as extended terminology “qualitative and quantitative GESs” (Chang et al. 2011). For clarity and consistency, this vignette defines GES as a generic term that comprises both GSs and GEPs (Lamb et al. 2006). This generalization is important, because several GESS algorithms are introduced here that depend on reference databases containing GSs in some and GEPs in the majority of cases generated with various statistical methods. To also distinguish the queries (Q) from the entries in the reference databases (DB), they will be referred to as GES-Q and GES-DB entries in general descriptions, and as GS-Q or GEP-Q, and as GS-DB or GEP-DB in specific cases, respectively.
Depending on the extent the expression data have been pre-processed, the following distinguishes four major levels, where the first three and fourth belong into the GEP and GS categories, respectively. These four levels are: (1) normalized intensity or count values from hybridization- and sequencing-based technologies, respectively; (2) log fold changes (LFC) usually with base 2, Z-scores or p-values obtained from analysis routines of DEGs; (3) rank transformed versions of the GEPs obtained from the results of level 1 or 2; and (4) GSs extracted from the highest and lowest ranks under level 3. Typically, the corresponding GSs are the most up- or down-regulated DEGs observed among two biological states, such as comparisons among untreated vs. drug treatment or disease state. The order the DEG identifier labels are stored may reflect their ranks or have no meaning. When unclear, the text specifies which of the four pre-processing levels were used along with additional relevant details.
Getting Started
Package Install
As Bioconductor package signatureSearch can be installed
with the BiocManager::install() function.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("signatureSearch")
BiocManager::install("girke-lab/signatureSearch", build_vignettes=FALSE) # Installs from githubNext the package needs to be loaded into a user’s R session.
The following lists essential help files and opens the vignette of the package.
Reference Databases
The helper package signatureSearchData provides access to
pre-built GES databases, including CMAP2, LINCS and LINCS2, that are
stored on Bioconductor’s ExperimentHub as HDF5 files. Users
can download these databases as follows.
library(ExperimentHub); library(rhdf5)
eh <- ExperimentHub()
cmap <- eh[["EH3223"]]; cmap_expr <- eh[["EH3224"]]
lincs <- eh[["EH3226"]]; lincs_expr <- eh[["EH3227"]]
lincs2 <- eh[["EH7297"]]
h5ls(lincs2)This will store the paths to the downloaded database files in the
corresponding variables. The reference databases store the following
information: (1) lincs contains moderated z-scores from
differential expression (DE) analysis of 12,328 genes from 8,140
compound treatments of 30 cell lines corresponding to a total of 45,956
signatures; (2) lincs_expr contains gene expression
intensity values from 5,925 compound treatments of 30 cell lines
corresponding to a total of 38,824 signatures; (3) cmap
contains log2
fold changes of 12,437 genes from 1,281 compound treatments of 5 cell
lines corresponding to a total of 3,478 signatures; (4)
cmap_expr contains mean expression values from 1,309 drug
treatments of 4 cell lines corresponding to a total of 3,587 signatures.
To minimize redundancy in the lincs and
lincs_expr databases, they were assembled from GESs
corresponding to a compound dosage and treatment time of 10μM and 24h, respectively. If
necessary one can create here easily database instances for all LINCS
measurements. However, this will make the search results overwhelmingly
complex which we wanted to avoid here; (5) lincs2 contains
moderated z-scores from DE analysis of 12,328 genes from 30,456 compound
treatments of 58 cell lines corresponding to a total of 136,460
signatures. To minimize redundancy of perturbagens having many
signatures in different dosage and treatment time within the same cell
line, the ‘exemplar’ signature for each perturbagen in each cell line
was assembled. These signatures are annotated from CLUE group and are
generally picked based on TAS (Transcriptional Activity Score), such
that the signature with the highest TAS is chosen as exemplar. The
LINCS2 database is exactly the same as the reference database used for
the Query Tool in CLUE website.
For details how the CMAP2, LINCS and LINCS2 databases were
constructed, please refer to the vignette of the
signatureSearchData package. The command
browseVignettes("signatureSearchData") will open this
vignette from R.
Custom databases can be built with the build_custom_db
function. Here the user provides custom genome-wide gene expression data
(e.g. for drug, disease or genetic perturbations) in a
data.frame or matrix. The gene expression data
can be most types of the pre-processed gene expression values described
under section 1.4.
Signature Searches (GESS)
This tutorial section introduces the GESS algorithms implemented in signatureSearch. Currently, this includes five search algorithms, while additional ones will be added in the future. Based on the data types represented in the query and database, they can be classified into set- and correlation-based methods (see Figure 1). The first 4 methods described below are set-based, whereas the last one is a correlation-based method. We refer to a search method as set-based if at least one of the two data components (query and/or database) is composed of an identifier set (e.g. gene labels) that may be ranked or unranked. In contrast to this, correlation-based methods require quantitative values, usually of the same type such as normalized intensities, for both the query and the database entries. An advantage of the set-based methods is that their queries can be the highest and lowest ranking gene sets derived from a genome-wide profiling technology that may differ from the one used to generate the reference database. However, the precision of correlation methods often outperforms set-based methods. On the other hand, due to the nature of the expected input, correlation-based methods are usually only an option when both the query and database entries are based on the same or at least comparable technologies. In other words, set-based methods are more technology agnostic than correlation-based methods, but may not provide the best precision performance.
Test Query and Database
To minimize the run time of the following test code, a small toy
database has been assembled from the LINCS database containing a total
of 100 GESs from human SKB (muscle) cells. Of the 100 GESs in this toy
database, 95 were random sampled and 5 were cherry-picked. The latter
five are GESs from HDAC inhibitor treatments including the known drugs:
vorinostat, rhamnetin, trichostatin A, pyroxamide, and HC toxin. To
further reduce the size of the toy database, the number of its genes was
reduced from 12,328 to 5,000 by random sampling. The query signature
used in the sample code below is the vorinostat GES drawn from the toy
database. For simplicity and to minimize the build time of this
vignette, the following sample code uses a pre-generated instance of
this toy database stored under the extdata directory of
this package. The detailed code for generating the toy database and
other custom instances of the LINCS database is given in the
Supplementary Material section of this vignette.
The following imports the toy GES database into a
SummarizedExperiment container (here
sample_db). In addition, the test query set of up/down DEGs
(here upset and downset) is extracted from the
vorinostat GES entry in the database.
db_path <- system.file("extdata", "sample_db.h5", package = "signatureSearch")
# Load sample_db as `SummarizedExperiment` object
library(SummarizedExperiment); library(HDF5Array)
sample_db <- SummarizedExperiment(HDF5Array(db_path, name="assay"))
rownames(sample_db) <- HDF5Array(db_path, name="rownames")
colnames(sample_db) <- HDF5Array(db_path, name="colnames")
# get "vorinostat__SKB__trt_cp" signature drawn from toy database
query_mat <- as.matrix(assay(sample_db[,"vorinostat__SKB__trt_cp"]))
query <- as.numeric(query_mat); names(query) <- rownames(query_mat)
upset <- head(names(query[order(-query)]), 150)
head(upset)## [1] "230" "5357" "2015" "2542" "1759" "6195"## [1] "22864" "9338" "54793" "10384" "27000" "10161"CMAP Search Method
Lamb et al. (2006) introduced the gene
expression-based search method known as Connectivity Map (CMap) where a
GES database is searched with a query GES for similar entries (Lamb et al. 2006). Specifically, the GESS
method from Lamb et al. (2006), here
termed as CMAP, uses as query the two label sets of the most
up- and down-regulated genes from a genome-wide expression experiment,
while the reference database is composed of rank transformed expression
profiles (e.g. ranks of LFC or z-scores). The actual GESS
algorithm is based on a vectorized rank difference calculation. The
resulting Connectivity Score expresses to what degree the query up/down
gene sets are enriched on the top and bottom of the database entries,
respectively. The search results are a list of perturbagens such as
drugs that induce similar or opposing GESs as the query. Similar GESs
suggest similar physiological effects of the corresponding perturbagens.
As discussed in the introduction, these GES associations can be useful
to uncover novel MOAs of drugs or treatments for diseases. Although
several variants of the CMAP algorithm are available in other
software packages including Bioconductor, the implementation provided by
signatureSearch follows the original description of the
authors as closely as possible. This allows to reproduce in our tests
the search results from the corresponding CMAP2 web service of the Broad
Institute.
In the following code block, the qSig function is used
to generate a qSig object by defining (i) the query
signature, (ii) the GESS method and (iii) the path to the reference
database. Next, the query signature is used to search the reference
database with the chosen GESS method. The type of the query signature
and the reference database needs to meet the requirement of the search
algorithm. In the chosen example the GESS method is CMAP, where
the query signature needs to be the labels of up and down regulated
genes, and the reference database contains rank transformed genome-wide
expression profiles. Alternatively, the database can contain the
genome-wide profiles themselves. In this case they will be transformed
to gene ranks during the search. Note, in the given example the
db_path variable stores the path to the toy database
containing 100 GESs composed of z-scores.
## 150 / 150 genes in up set share identifiers with reference database## 150 / 150 genes in down set share identifiers with reference database## # A tibble: 100 × 11
## pert cell type trend raw_score scaled_score N_upset N_downset t_gn_sym
## <chr> <chr> <chr> <chr> <dbl> <dbl> <int> <int> <chr>
## 1 vorinost… SKB trt_… up 1.94 1 150 150 HDAC1; …
## 2 rescinna… SKB trt_… down -0.295 -1 150 150 ACE
## 3 zuclopen… SKB trt_… down -0.287 -0.972 150 150 ADRA1A;…
## 4 evoxine SKB trt_… down -0.244 -0.828 150 150 <NA>
## 5 scouleri… SKB trt_… down -0.242 -0.821 150 150 ADRA1D
## 6 ganglios… SKB trt_… down -0.239 -0.809 150 150 <NA>
## 7 warfarin SKB trt_… down -0.234 -0.794 150 150 ARSE; C…
## 8 trichost… SKB trt_… up 1.50 0.775 150 150 HDAC1; …
## 9 endecaph… SKB trt_… down -0.224 -0.760 150 150 <NA>
## 10 HC-toxin SKB trt_… up 1.44 0.745 150 150 HDAC1
## # ℹ 90 more rows
## # ℹ 2 more variables: MOAss <chr>, PCIDss <chr>The search result is stored in a gessResult object (here
named cmap) containing the following components: search
result table, query signature, name of the GESS method and path to the
reference database. The result accessor function can be
used to extract the search result table from the gessResult
object. This table contains the search results for each perturbagen
(here drugs) in the reference database ranked by their signature
similarity to the query. For the CMAP method, the similarity
metrics are raw_score and scaled_score. The
raw score represents the bi-directional enrichment score
(Kolmogorov-Smirnov statistic) for a given up/down query signature.
Under the scaled_score column, the raw_score
has been scaled to values from 1 to -1 by dividing positive scores and
negative scores with the maximum positive score and the absolute value
of the minimum negative score, respectively. The remaining columns in
the search result table contain the following information.
pert: name of perturbagen (e.g. drug) in the
reference database; cell: acronym of cell type;
type: perturbation type, e.g. compound treatment
is trt_cp; trend: up or down when reference
signature is positively or negatively connected with the query
signature, respectively; N_upset or N_downset:
number of genes in the query up or down sets, respectively;
t_gn_sym: gene symbols of the corresponding drug
targets.
LINCS Search Method
Subramanian et al. (2017) introduced a
more complex GESS algorithm, here referred to as LINCS. While
related to CMAP, there are several important differences among
the two approaches. First, LINCS weights the query genes based
on the corresponding differential expression scores of the GEPs in the
reference database (e.g. LFC or z-scores). Thus, the reference
database used by LINCS needs to store the actual score values
rather than their ranks. Another relevant difference is that the
LINCS algorithm uses a bi-directional weighted
Kolmogorov-Smirnov enrichment statistic (ES) as similarity metric. To
the best of our knowledge, the LINCS search functionality
in signatureSearch provides the first downloadable
standalone software implementation of this algorithm.
In the following example the qSig object for the
LINCS method is initialized the same way as the corresponding
CMAP object above with the exception that “LINCS” needs to be
specified under the gess_method argument.
## 150 / 150 genes in up set share identifiers with reference database## 150 / 150 genes in down set share identifiers with reference databaselincs <- gess_lincs(qsig_lincs, sortby="NCS", tau=FALSE, workers=1,
addAnnotations = TRUE, GeneType = "reference")
result(lincs)## # A tibble: 100 × 14
## pert cell type trend WTCS WTCS_Pval WTCS_FDR NCS NCSct N_upset
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 vorinostat SKB trt_… up 1 0 0 2.57 2.57 150
## 2 trichostatin… SKB trt_… up 0.863 5.96e-6 0.000180 2.22 2.22 150
## 3 HC-toxin SKB trt_… up 0.857 6.22e-6 0.000180 2.20 2.20 150
## 4 pyroxamide SKB trt_… up 0.625 1.63e-5 0.000180 1.60 1.60 150
## 5 zuclopenthix… SKB trt_… down -0.321 8.29e-5 0.000319 -1.19 -1.19 150
## 6 rescinnamine SKB trt_… down -0.319 9.60e-5 0.000356 -1.18 -1.18 150
## 7 APHA-compoun… SKB trt_… up 0.445 2.42e-5 0.000180 1.14 1.14 150
## 8 epothilone SKB trt_… down -0.308 2.40e-4 0.000801 -1.14 -1.14 150
## 9 scopolamine-… SKB trt_… up 0.416 2.54e-5 0.000180 1.07 1.07 150
## 10 I-070759 SKB trt_… up 0.408 2.58e-5 0.000180 1.05 1.05 150
## # ℹ 90 more rows
## # ℹ 4 more variables: N_downset <int>, t_gn_sym <chr>, MOAss <chr>,
## # PCIDss <chr>The gess_* functions also support appending the compound
annotation table (if provided) to the GESS result for the
pert column (pert_id column if
refdb is set as lincs2) that stores compounds
in the drug slot of
<drug>__<cell>__<factor> format of
treatments in the reference database. The following uses LINCS method
searching against the newest LINCS2 database and passing the LINCS2
compound information table as an example.
data("lincs_pert_info2")
qsig_lincs2 <- qSig(query=list(upset=upset, downset=downset),
gess_method="LINCS", refdb="lincs2")
# When the compound annotation table is not provided
lincs2 <- gess_lincs(qsig_lincs2, tau=FALSE, sortby="NCS", workers=2)
# When the compound annotation table is provided
lincs2 <- gess_lincs(qsig_lincs2, tau=TRUE, sortby="NCS", workers=1,
cmp_annot_tb=lincs_pert_info2, by="pert_id",
cmp_name_col="pert_iname") # takes about 15 minutes
result(lincs2) %>% print(width=Inf) The search results are stored in a gessResult object as
under the CMAP example above. The similarity scores stored in
the LINCS result table are summarized here. WTCS:
Weighted Connectivity Score; WTCS_Pval: nominal p-value of
WTCS; WTCS_FDR: false discovery rate of
WTCS_Pval; NCS: normalized connectivity score;
NCSct: NCS summarized across cell types; Tau:
enrichment score standardized for a given database. The latter is only
included in the result table if tau=TRUE in a
gess_lincs function call. The example given is run with
tau=FALSE, because the tau values are only meaningful when
the complete LINCS database is used which is not the case for the toy
database. TauRefSize: size of reference perturbations for
computing Tau.
The following provides a more detailed description of the similarity scores computed by the LINCS method. Additional details are available in the Supplementary Material Section of the Subramanian et al. (2017) paper.
WTCS: The Weighted Connectivity Score is a
bi-directional ES for an up/down query set. If the ES values of an up
set and a down set are of different signs, then WTCS is (ESup-ESdown)/2,
otherwise, it is 0. WTCS values range from -1 to 1. They are positive or
negative for signatures that are positively or inversely related,
respectively, and close to zero for signatures that are unrelated.
WTCS_Pval and WTCS_FDR: The nominal p-value
of the WTCS and the corresponding false discovery rate (FDR) are
computed by comparing the WTCS against a null distribution of WTCS
values obtained from a large number of random queries (e.g.
1000).
NCS: To make connectivity scores comparable across cell
types and perturbation types, the scores are normalized. Given a vector
of WTCS
values w resulting from a
query, the values are normalized within each cell line c and perturbagen type t to obtain the Normalized
Connectivity Score (NCS) by dividing
the WTCS
value by the signed mean of the WTCS
values within the subset of signatures in the reference database
corresponding to c and t.
NCSct: The NCS is summarized across cell types as
follows. Given a vector of NCS values for
perturbagen p, relative to
query q, across all cell lines
c in which p was profiled, a cell-summarized
connectivity score is obtained using a maximum quantile statistic. It
compares the 67 and 33 quantiles of NCSp, c
and retains whichever is of higher absolute magnitude.
Tau: The standardized score Tau compares an observed
NCS to a
large set of NCS values that
have been pre-computed for a specific reference database. The query
results are scored with Tau as a standardized measure ranging from 100
to -100. A Tau of 90 indicates that only 10% of reference perturbations
exhibit stronger connectivity to the query. This way one can make more
meaningful comparisons across query results.
gCMAP Search Method
The Bioconductor gCMAP (Sandmann et al. 2014) package provides access to a related but not identical implementation of the original CMAP algorithm proposed by Lamb et al. (2006). It uses as query a rank transformed GEP and the reference database is composed of the labels of up and down regulated DEG sets. This is the opposite situation of the CMAP method, where the query is composed of the labels of up and down regulated DEGs and the database contains rank transformed GESs.
In case of the gCMAP GESS method, the GEP-Q is a matrix with
a single column representing gene ranks from a biological state of
interest, here vorinostat treatment in SKB cells. The corresponding gene
labels are stored in the row name slot of the matrix. Instead of ranks
one can provide scores (e.g. z-scores) as in the example given
below. In such a case the scores will be internally transformed to
ranks. The reference database consists of gene label sets that were
extracted from the toy databases by applying a higher and
lower filter, here set to 1 and
-1, respectively.
qsig_gcmap <- qSig(query = query_mat, gess_method = "gCMAP", refdb = db_path)
gcmap <- gess_gcmap(qsig_gcmap, higher=1, lower=-1, workers=1, addAnnotations = TRUE)
result(gcmap)## # A tibble: 100 × 11
## pert cell type trend effect nSet nFound signed t_gn_sym MOAss PCIDss
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <lgl> <chr> <chr> <chr>
## 1 vorinostat SKB trt_… up 1 1098 1098 TRUE HDAC1; … HDAC… 5311
## 2 warfarin SKB trt_… down -1 114 114 TRUE ARSE; C… Vita… 54678…
## 3 D-609 SKB trt_… down -0.715 125 125 TRUE <NA> <NA> 45479…
## 4 TC-2559 SKB trt_… down -0.685 377 377 TRUE CHRNA4 Acet… 98231…
## 5 rescinnam… SKB trt_… down -0.609 726 726 TRUE ACE ACE … 52809…
## 6 amiodarone SKB trt_… down -0.584 706 706 TRUE ABCG2; … Pota… 2157
## 7 trichosta… SKB trt_… up 0.576 1430 1430 TRUE HDAC1; … CDK … 444732
## 8 zuclopent… SKB trt_… down -0.554 483 483 TRUE ADRA1A;… Dopa… 53115…
## 9 progester… SKB trt_… down -0.553 242 242 TRUE AKR1C1;… Prog… 5994
## 10 evoxine SKB trt_… down -0.533 461 461 TRUE <NA> Furo… 673465
## # ℹ 90 more rowsAs in the other search methods, the gCMAP results are stored
in a gessResult object. The columns in the corresponding
search result table, that are specific to the gCMAP method,
contain the following information. effect: scaled
bi-directional enrichment score corresponding to the
scaled_score under the CMAP result;
nSet: number of genes in the reference gene sets after
applying the higher and lower cutoff; nFound: number of
genes in the reference gene sets that are present in the query
signature; signed: whether the gene sets in the reference
database have signs, e.g. representing up and down regulated
genes when computing scores.
Fisher Search Method
Fisher’s exact test (Graham J. G. Upton 1992) can also be used to search a GS-DB for entries that are similar to a GS-Q. In this case both the query and the database are composed of gene label sets, such as DEG sets.
In the following example, both the query and the
refdb used under the qSig call are genome-wide
GEPs, here z-scores. The actual gene sets required for the Fisher’s
exact test are obtained by setting the higher and
lower cutoffs to 1 and -1, respectively.
qsig_fisher <- qSig(query = query_mat, gess_method = "Fisher", refdb = db_path)
fisher <- gess_fisher(qSig=qsig_fisher, higher=1, lower=-1, workers=1, addAnnotations = TRUE)
result(fisher)## # A tibble: 100 × 14
## pert cell type trend pval padj effect LOR nSet nFound
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 vorinostat SKB trt_… over 0 0 37.5 Inf 1098 1098
## 2 trichostat… SKB trt_… over 2.81e-177 1.41e-175 28.4 2.06 1430 705
## 3 HC-toxin SKB trt_… over 1.73e-132 5.77e-131 24.5 1.74 1804 746
## 4 SPB02303 SKB trt_… under 3.18e- 25 7.94e- 24 -10.4 -1.05 963 99
## 5 MDL-28170 SKB trt_… under 2.67e- 24 5.33e- 23 -10.2 -0.767 1823 260
## 6 clofibric-… SKB trt_… under 2.10e- 19 3.49e- 18 -9.01 -0.660 1943 300
## 7 pyroxamide SKB trt_… over 2.27e- 16 3.24e- 15 8.21 0.770 641 225
## 8 TUL-XX023 SKB trt_… under 3.03e- 13 3.79e- 12 -7.29 -0.731 885 116
## 9 formoterol SKB trt_… under 1.68e- 12 1.87e- 11 -7.06 -0.496 2238 389
## 10 I-070759 SKB trt_… over 2.18e- 12 2.18e- 11 7.02 0.537 1222 359
## # ℹ 90 more rows
## # ℹ 4 more variables: signed <lgl>, t_gn_sym <chr>, MOAss <chr>, PCIDss <chr>The columns in the result table specific to the Fisher
method include the following information. pval: p-value of
the Fisher’s exact test; padj: p-value adjusted for
multiple hypothesis testing using R’s p.adjust function
with the Benjamini & Hochberg (BH) method; effect:
z-score based on the standard normal distribution; LOR: log
odds ratio.
If the query contains the labels of up and down
regulated genes then the two sets can be provided as a list. Internally,
they will be combined into a single unsigned set, while the reference
database is processed the same way as in the previous example.
Correlation-based Search Method
Correlation-based similarity metrics, such as Spearman or Pearson coefficients, can also be used as GESS methods. As non-set-based methods, they require quantitative gene expression values for both the query and the database entries, that usually need to be of the same type to obtain meaningful results, such as normalized intensities or read counts from microarrays or RNA-Seq experiments, respectively. For correlation searches to work, it is important that both the query and reference database contain the same type of gene identifiers. The expected data structure of the query is a matrix with a single numeric column and the gene labels (e.g. Entrez Gene IDs) in the row name slot. For convenience, the correlation-based searches can either be performed with the full set of genes represented in the database or a subset of them. The latter can be useful to focus the computation for the correlation values on certain genes of interest such as a DEG set or the genes in a pathway of interest. For comparing the performance of different GESS methods, it can also be advantageous to subset the genes used for a correlation-based search to same set used in a set-based search, such as the up/down DEGs used in a LINCS GESS. This way the search results of correlation- and set-based methods can be more comparable because both are provided with equivalent information content.
CORall
The following example runs a correlation-based search with the
Spearman method using all genes present in the reference database. The
GESs used for both the query and refdb are
z-scores. The gess_cor function also supports Pearson and
Kendall correlation coefficients by assigning the corresponding names to
the method argument. For details, users want to consult the
help file of the gess_cor function.
qsig_sp <- qSig(query=query_mat, gess_method="Cor", refdb=db_path)
sp <- gess_cor(qSig=qsig_sp, method="spearman", workers=1, addAnnotations = TRUE)
result(sp)## # A tibble: 100 × 8
## pert cell type trend cor_score t_gn_sym MOAss PCIDss
## <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
## 1 vorinostat SKB trt_cp up 1 HDAC1; HDAC10;… HDAC… 5311
## 2 trichostatin-a SKB trt_cp up 0.702 HDAC1; HDAC10;… CDK … 444732
## 3 HC-toxin SKB trt_cp up 0.634 HDAC1 HDAC… 3571
## 4 pyroxamide SKB trt_cp up 0.325 HDAC1; HDAC3; … HDAC… 4996
## 5 APHA-compound-8 SKB trt_cp up 0.152 HDAC8 HDAC… 10379…
## 6 benzonatate SKB trt_cp up 0.151 SCN5A Loca… 7699
## 7 rescinnamine SKB trt_cp down -0.136 ACE ACE … 52809…
## 8 evoxine SKB trt_cp down -0.134 <NA> Furo… 673465
## 9 scopolamine-n-oxide SKB trt_cp up 0.125 <NA> <NA> 30006…
## 10 warfarin SKB trt_cp down -0.123 ARSE; CYP2C8; … Vita… 54678…
## # ℹ 90 more rowsThe column specific to the correlation-based search methods contains
the following information. cor_score: correlation
coefficient based on the method defined in the gess_cor
function call.
CORsub
To perform a correlation-based search on a subset of genes represented in the database, one can simply provide the chosen gene subset in the query. During the search the database entries will be subsetted to the genes provided in the query signature. The given example uses a query GES that is subsetted to the genes with the 150 highest and lowest z-scores.
# Subset z-scores of 150 up and down gene sets from
# "vorinostat__SKB__trt_cp" signature.
query_mat_sub <- as.matrix(query_mat[c(upset, downset),])
qsig_spsub <- qSig(query = query_mat_sub, gess_method = "Cor", refdb = db_path)
spsub <- gess_cor(qSig=qsig_spsub, method="spearman", workers=1, addAnnotations = TRUE)
result(spsub)## # A tibble: 100 × 8
## pert cell type trend cor_score t_gn_sym MOAss PCIDss
## <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
## 1 vorinostat SKB trt_cp up 1 HDAC1; HDAC10;… HDAC… 5311
## 2 trichostatin-a SKB trt_cp up 0.885 HDAC1; HDAC10;… CDK … 444732
## 3 HC-toxin SKB trt_cp up 0.856 HDAC1 HDAC… 3571
## 4 pyroxamide SKB trt_cp up 0.646 HDAC1; HDAC3; … HDAC… 4996
## 5 APHA-compound-8 SKB trt_cp up 0.442 HDAC8 HDAC… 10379…
## 6 benzonatate SKB trt_cp up 0.333 SCN5A Loca… 7699
## 7 MD-040 SKB trt_cp up 0.307 <NA> <NA> 73707…
## 8 K784-3187 SKB trt_cp up 0.297 <NA> <NA> 36894…
## 9 scopolamine-n-oxide SKB trt_cp up 0.297 <NA> <NA> 30006…
## 10 fasudil SKB trt_cp up 0.288 CDC42BPB; MYLK… Rho … 3547
## # ℹ 90 more rowsSummary of Search Results
Although the toy database is artificially small, one can use the above search results for a preliminary performance assessment of the different GESS methods in ranking drugs based on known modes of action (MOA). Four of the five cherry-picked HDAC inhibitors (vorinostat, trichostatin-a, HC-toxin, pyroxamide) were ranked among the top 10 ranking drugs in the search results of the LINCS, Fisher and Spearman correlation methods. If generalizable, this result implies a promising performance of these search methods for grouping drugs by their MOA categories. In addition, the LINCS and Spearman methods were able to rank another HDAC inhibitor, APHA-compound-8, at the top of their search results, indicating a better sensitivity of these two methods compared to the other methods.
GESS Result Visualization
The gess_res_vis function allows to summarize the
ranking scores of selected perturbagens for GESS results across cell
types along with cell type classifications, such as normal and tumor
cells. In the following plot (Figure 3) the perturbagens are drugs
(along x-axis) and the ranking scores are LINCS’ NCS values (y-axis).
For each drug the NCS values are plotted for each cell type as
differently colored dots, while their shape indicates the cell type
class. Note, the code for generating the plot is not evaluated here
since the toy database used by this vignette contains only treatments
for one cell type (here SKB cells). This would result in a not very
informative plot. To illustrate the full potential of the
gess_res_vis function, the following code section applies
to a search where the vorinostat signature was used to
query with the gess_lincs method the full LINCS database.
Subsequently, the search result is processed by the
gess_res_vis function to generate the plot shown in Figure
3.
vor_qsig_full <- qSig(query = list(upset=upset, downset=downset),
gess_method="LINCS", refdb="lincs")
vori_res_full <- gess_lincs(qSig=vor_qsig_full, sortby="NCS", tau=TRUE)
vori_tb <- result(vori_res_full)
drugs_top10 <- unique(result(lincs)$pert)[1:10]
drugs_hdac <- c("panobinostat","mocetinostat","ISOX","scriptaid","entinostat",
"belinostat","HDAC3-selective","tubastatin-a","tacedinaline","depudecin")
drugs = c(drugs_top10, drugs_hdac)
gess_res_vis(vori_tb, drugs = drugs, col = "NCS")
Figure 3: Summary of NCS scores across cell types
(y-axis) for selected drugs (x-axis). The plot shown here is based on a
search result where the vorinostat signature was used to query the
entire LINCS database with the gess_lincs method. The drugs
included here are the 10 top ranking drugs of the search result plus 10
cherry-picked drugs that are all HDAC inhibitors. Additional details are
provided in the text of this sub-section.
The cellNtestPlot function allows to summarize the
number of perturbagens tested in cell types along with cell type primary
site information, such as blood or muscle. The following bar plot
(Figure 4) show the number of tested compounds (along x-axis) in 30 cell
types (y-axis), the text in the strips show the primary sites of cells
in the LINCS database.
# cellNtestPlot(refdb="lincs")
# ggsave("vignettes/images/lincs_cell_ntest.png", width=6, height=8)
knitr::include_graphics("images/lincs_cell_ntest.png")
Figure 4: Number of tested compounds (along x-axis) in 30 cell types (y-axis) in LINCS database. The text in the strips show the primary sites of cells.
The following table shows the LINCS cell information
Batch Processing of GESSs
The above 5 GESS methods support searching reference database
parallelly by defining the workers parameter, the default
is 1. It means that when submitting one query, the parallelization
happens on the GES database level where one splits up a single query
process into searching several chunks of the database in parallel.
Multiple GES queries can also be processed sequentially or in parallel
mode. Parallel evaluations can substantially reduce processing times.
The parallelization techniques covered in this vignette, are based on
utilities of the BiocParallel and batchtools
packages. For demonstration purposes the following example uses a small
batch query containing several GESs. First, this batch query is
processed sequentially without any parallelization using a simple
lapply loop. Next, the same query is processed in parallel
mode using multiple CPU cores of a single machine. The third option
demonstrates how this query can be processed in parallel mode on
multiple machines of a computer cluster with a workload management
(queueing) system (e.g. Slurm or Torque).
Sequential Processing
The following processes a small toy batch query with the
LINCS method sequentially in an lapply loop.
The object batch_queries is a list containing the two
sample GESs q1 and q2 that are composed of
entrez gene identifiers. The search results are written to tab-delimited
tabular files under a directory called batch_res. The name
and path of this directory can be changed as needed.
library(readr)
batch_queries <- list(q1=list(upset=c("23645", "5290"), downset=c("54957", "2767")),
q2=list(upset=c("27101","65083"), downset=c("5813", "84")))
refdb <- system.file("extdata", "sample_db.h5", package="signatureSearch")
gess_list <- lapply(seq_along(batch_queries), function(i){
qsig_lincs <- qSig(query = batch_queries[[i]],
gess_method="LINCS", refdb=refdb)
lincs <- gess_lincs(qsig_lincs, sortby="NCS", tau=TRUE)
if(!dir.exists("batch_res")){
dir.create("batch_res")
}
write_tsv(result(lincs), paste0("batch_res/lincs_res_", i, ".tsv"))
return(result(lincs))
})Parallelization with Multiple CPU Cores
The GESSs from the previous example can be accelerated by taking
advantage of multiple CPU cores available on a single computer system.
The parallel evaluation happens in the below bplapply loop
defined by the BiocParallel package. For this approach, all
processing instructions are encapsulated in a function named
f_bp that will be executed in the bplapply
loop. As before, the search results are written to tab-delimited tabular
files under a directory called batch_res. The name and path
of this directory can be changed as needed. For more background
information on this and the following parallelization options, users
want to consult the vignette of the BiocParallel
package.
library(BiocParallel)
f_bp <- function(i){
qsig_lincs <- qSig(query = batch_queries[[i]],
gess_method="LINCS", refdb=refdb)
lincs <- gess_lincs(qsig_lincs, sortby="NCS", tau=TRUE)
if(!dir.exists("batch_res")){
dir.create("batch_res")
}
write_tsv(result(lincs), paste0("batch_res/lincs_res_", i, ".tsv"))
return(result(lincs))
}
gess_list <- bplapply(seq_along(batch_queries), f_bp, BPPARAM = MulticoreParam(workers = 2))Parallelization with Multiple Computer Systems
In addition to utilizing multiple CPU cores of a single machine, one can further accelerate the processing by taking advantage of multiple computer systems (nodes) available on a computer cluster, where a queueing systems takes care of the load balancing.
In the following example, Njobs sets the number of
independent processes to be run on the cluster, and ncpus
defines the number of CPU cores to be used by each process. The chosen
example will run 2 processes each utilizing 4 CPU cores. If
batch_queries contains sufficient GESs and the
corresponding computing resources are available on a cluster, then the
given example process will utilize in total 8 CPU cores. Note, the given
sample code will work on most queueing systems as it is based on
utilities from the batchtools package. The latter supports
template files (*.tmpl) for defining the run parameters of
different schedulers. To run this code, one needs to have both a
conf file (see .batchtools.conf.R samples here) and a
template file (see *.tmpl samples here)
for the queueing system available on a cluster. The following example
uses the sample conf and template files for
the Slurm scheduler.
For additional details on parallelizing computations on clusters,
users want to consult the vignettes of the batchtools and
BiocParallel packages.
library(batchtools)
batch_queries <- list(q1=list(upset=c("23645", "5290"), downset=c("54957", "2767")),
q2=list(upset=c("27101","65083"), downset=c("5813", "84")))
refdb <- system.file("extdata", "sample_db.h5", package="signatureSearch")
f_bt <- function(i, batch_queries, refdb){
library(signatureSearch)
library(readr)
qsig_lincs <- qSig(query = batch_queries[[i]],
gess_method="LINCS", refdb=refdb)
lincs <- gess_lincs(qsig_lincs, sortby="NCS", tau=TRUE)
if(!dir.exists("batch_res")){
dir.create("batch_res")
}
write_tsv(result(lincs), paste0("batch_res/lincs_res_", i, ".tsv"))
return(result(lincs)) # or return()
}Copy the conf and template files for Slurm
to current working directory.
file.copy(system.file("extdata", ".batchtools.conf.R", package="signatureSearch"), ".")
file.copy(system.file("extdata", "slurm.tmpl", package="signatureSearch"), ".")Create the registry and submit jobs.
reg <- makeRegistry(file.dir="reg_batch", conf.file=".batchtools.conf.R")
# reg <- loadRegistry(file.dir="reg_batch", conf.file=".batchtools.conf.R", writeable=TRUE)
Njobs <- 1:2
ids <- batchMap(fun=f_bt, Njobs, more.args = list(
batch_queries=batch_queries, refdb=refdb))
submitJobs(ids, reg=reg, resources=list(
partition="intel", walltime=120, ntasks=1, ncpus=4, memory=10240))
getStatus()
waitForJobs() # Wait until all jobs are completed
res1 <- loadResult(1)
unlink(c(".batchtools.conf.R", "slurm.tmpl"))Functional Enrichment (FEA)
GESS results are lists of perturbagens (here drugs) ranked by their signature similarity to a GES-Q of interest. Interpreting these search results with respect to the cellular networks and pathways affected by the top ranking drugs is difficult. To overcome this challenge, the knowledge of the target proteins of the top ranking drugs can be used to perform functional enrichment analysis (FEA) based on community annotation systems, such as Gene Ontologies (GO), pathways (e.g. KEGG, Reactome), drug MOAs or Pfam domains. For this, the ranked drug sets are converted into target gene/protein sets to perform Target Set Enrichment Analysis (TSEA) based on a chosen annotation system. Alternatively, the functional annotation categories of the targets can be assigned to the drugs directly to perform Drug Set Enrichment Analysis (DSEA). Although TSEA and DSEA are related, their enrichment results can be distinct. This is mainly due to duplicated targets present in the test sets of the TSEA methods, whereas the drugs in the test sets of DSEA are usually unique. Additional reasons include differences in the universe sizes used for TSEA and DSEA.
Importantly, the duplications in the test sets of the TSEA are due to
the fact that many drugs share the same target proteins. Standard
enrichment methods would eliminate these duplications since they assume
uniqueness in the test sets. Removing duplications in TSEA would be
inappropriate since it would erase one of the most important pieces of
information of this approach. To solve this problem, we have developed
and implemented in the signatureSearch package a weighting
method for duplicated targets, where the weighting is proportional to
the frequency of the targets in the test set.
To perform TSEA and DSEA, drug-target annotations are essential. They
can be obtained from several sources, including DrugBank, ChEMBL,
STITCH, and the Touchstone dataset from
the LINCS project (Wishart et al. 2018; Gaulton
et al. 2017; Kuhn et al. 2010; Subramanian et al. 2017). Most
drug-target annotations provide UniProt identifiers for the target
proteins. They can be mapped, if necessary via their encoding genes, to
the chosen functional annotation categories, such as GO or KEGG. To
minimize bias in TSEA or DSEA, often caused by promiscuous binders, it
can be beneficial to remove drugs or targets that bind to large numbers
of distinct proteins or drugs, respectively. To conduct TSEA and DSEA
efficiently, signatureSearch and its helper package
signatureSearchData provide several convenience utilities
along with drug-target lookup resources for automating the mapping from
drug sets to target sets to functional
categories.
Note, most FEA tests involving proteins in their test sets are
performed on the gene level in signatureSearch. This way
one can avoid additional duplications due to many-to-one relationships
among proteins and their encoding genes. For this, the corresponding
functions in signatureSearch will usually translate target
protein sets into their encoding gene sets using identifier mapping
resources from R/Bioconductor, such as the org.Hs.eg.db
annotation package. Because of this as well as simplicity, the text in
the vignette and help files of this package will refer to the targets of
drugs almost interchangeably as proteins or genes, even though the
former are the direct targets and the latter only the indirect targets
of drugs.
TSEA
The following introduces how to perform TSEA on drug-based GESS
results using as functional annotation systems GO, KEGG and Reactome
pathways. The enrichment tests are performed with three widely used
algorithms that have been modified in signatureSearch to
take advantage of duplication information present in the test sets used
for TSEA. The relevance of these target duplications is explained above.
The specialized enrichment algorithms include Duplication Adjusted
Hypergeometric Test (dup_hyperG), Modified Gene
Set Enrichment Analysis (mGSEA) and MeanAbs
(mabs).
Hypergeometric Test
The classical hypergeometric test assumes uniqueness in its gene/protein test sets. Its p-value is calculated according to equation
In case of GO term enrichment analysis the individual variables are assigned the following components. N is the total number of genes/proteins contained in the entire annotation universe; D is the number of genes annotated at a specific GO node; n is the total number of genes in the test set; and x is the number of genes in the test set annotated at a specific GO node. To maintain the duplication information in the test sets used for TSEA, the values of n and x in the above equation are adjusted by the frequency of the target proteins in the test set. Effectively, the approach removes the duplications, but maintains their frequency information in form of weighting values.
With GO
The following example code uses the n top ranking drugs (here n = 10) from the LINCS GESS result
as input to the tsea_dup_hyperG method. Internally, the
latter converts the drug set to a target set, and then computes for it
enrichment scores for each MF GO term based on the hypergeometric
distribution. The enrichment results are stored in a
feaResult object. It contains the organism information of
the annotation system, and the ontology type of the GO annotation
system. If the annotation system is KEGG, the latter will be “KEGG”. The
object also stores the input drugs used for the enrichment test, as well
as their target information.
drugs <- unique(result(lincs)$pert[1:10])
dup_hyperG_res <- tsea_dup_hyperG(drugs=drugs, universe="Default",
type="GO", ont="MF", pvalueCutoff=0.05,
pAdjustMethod="BH", qvalueCutoff=0.1,
minGSSize=10, maxGSSize=500)
dup_hyperG_res## #
## # Functional Enrichment Analysis
## #
## #...@organism Homo sapiens
## #...@ontology MF
## #...@drugs chr [1:10] "vorinostat" "trichostatin-a" "hc-toxin" "pyroxamide" ...
## #...@targets chr [1:41] "HDAC1" "HDAC10" "HDAC11" "HDAC2" "HDAC3" "HDAC4" "HDAC5" ...
## #...71 enriched terms found
## # A tibble: 71 × 10
## ont ID Description GeneRatio BgRatio pvalue p.adjust qvalue itemID
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 MF GO:001… NAD-depend… 26/41 16/193… 0 0 0 HDAC1…
## 2 MF GO:003… histone de… 26/41 11/193… 0 0 0 HDAC1…
## 3 MF GO:003… NAD-depend… 26/41 11/193… 0 0 0 HDAC1…
## 4 MF GO:003… NAD-depend… 26/41 17/193… 0 0 0 HDAC1…
## 5 MF GO:000… histone de… 26/41 44/193… 9.55e-63 1.65e-60 5.49e-61 HDAC1…
## 6 MF GO:003… protein de… 26/41 45/193… 2.26e-62 3.41e-60 1.14e-60 HDAC1…
## 7 MF GO:001… deacetylas… 26/41 58/193… 2.03e-58 2.72e-56 9.08e-57 HDAC1…
## 8 MF GO:001… hydrolase … 26/41 89/193… 1.85e-52 1.86e-50 6.20e-51 HDAC1…
## 9 MF GO:001… hydrolase … 26/41 147/19… 4.54e-46 3.42e-44 1.14e-44 HDAC1…
## 10 MF GO:004… histone de… 20/41 112/19… 7.39e-35 5.24e-33 1.75e-33 HDAC1…
## # ℹ 61 more rows
## # ℹ 1 more variable: Count <int>The result accessor function can be used to extract a
tabular result from the feaResult object. The rows of this
result table contain the functional categories (e.g. GO terms
or KEGG pathways) ranked by the corresponding enrichment statistic.
## # A tibble: 71 × 10
## ont ID Description GeneRatio BgRatio pvalue p.adjust qvalue itemID
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 MF GO:001… NAD-depend… 26/41 16/193… 0 0 0 HDAC1…
## 2 MF GO:003… histone de… 26/41 11/193… 0 0 0 HDAC1…
## 3 MF GO:003… NAD-depend… 26/41 11/193… 0 0 0 HDAC1…
## 4 MF GO:003… NAD-depend… 26/41 17/193… 0 0 0 HDAC1…
## 5 MF GO:000… histone de… 26/41 44/193… 9.55e-63 1.65e-60 5.49e-61 HDAC1…
## 6 MF GO:003… protein de… 26/41 45/193… 2.26e-62 3.41e-60 1.14e-60 HDAC1…
## 7 MF GO:001… deacetylas… 26/41 58/193… 2.03e-58 2.72e-56 9.08e-57 HDAC1…
## 8 MF GO:001… hydrolase … 26/41 89/193… 1.85e-52 1.86e-50 6.20e-51 HDAC1…
## 9 MF GO:001… hydrolase … 26/41 147/19… 4.54e-46 3.42e-44 1.14e-44 HDAC1…
## 10 MF GO:004… histone de… 20/41 112/19… 7.39e-35 5.24e-33 1.75e-33 HDAC1…
## # ℹ 61 more rows
## # ℹ 1 more variable: Count <int>The columns in the result table, extracted from the
feaResult object, contain the following information. Note,
some columns are only present in the result tables of specific FEA
methods. ont: in case of GO one of BP, MF, CC, or ALL;
ID: GO or KEGG IDs; Description: description
of functional category; pvalue: raw p-value of enrichment
test; p.adjust: p-value adjusted for multiple hypothesis
testing based on method specified under pAdjustMethod;
qvalue: q value calculated with R’s qvalue
function to control FDR; itemID: IDs of items (genes for
TSEA, drugs for DSEA) overlapping among test and annotation sets;
setSize: size of the functional category;
GeneRatio: ratio of genes in the test set that are
annotated at a specific GO node or KEGG pathway; BgRatio:
ratio of background genes that are annotated at a specific GO node or
KEGG pathway. Count: number of overlapped genes between the
test set and the specific functional annotation term.
With KEGG
The same enrichment test can be performed for KEGG pathways as follows.
dup_hyperG_k_res <- tsea_dup_hyperG(drugs=drugs, universe="Default", type="KEGG",
pvalueCutoff=0.5, pAdjustMethod="BH", qvalueCutoff=0.5,
minGSSize=10, maxGSSize=500)
result(dup_hyperG_k_res)## # A tibble: 40 × 9
## ID Description GeneRatio BgRatio pvalue p.adjust qvalue itemID Count
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <int>
## 1 hsa050… Alcoholism 27/41 188/88… 2.60e-36 1.25e-34 4.93e-35 3065/… 27
## 2 hsa046… Neutrophil… 26/41 193/88… 5.17e-34 1.24e-32 4.90e-33 3065/… 26
## 3 hsa052… Viral carc… 26/41 205/88… 2.71e-33 4.34e-32 1.71e-32 3065/… 26
## 4 hsa045… Gap juncti… 12/41 92/8868 4.59e-15 5.50e-14 2.17e-14 1812/… 12
## 5 hsa050… Huntington… 17/41 311/88… 8.43e-15 8.09e-14 3.19e-14 3065/… 17
## 6 hsa041… Phagosome 11/41 159/88… 8.65e-11 6.92e-10 2.73e-10 7846/… 11
## 7 hsa050… Amyotrophi… 14/41 371/88… 4.97e-10 3.41e- 9 1.34e- 9 10013… 14
## 8 hsa048… Motor prot… 11/41 197/88… 8.69e-10 5.21e- 9 2.06e- 9 7846/… 11
## 9 hsa051… Pathogenic… 11/41 203/88… 1.20e- 9 6.38e- 9 2.52e- 9 7846/… 11
## 10 hsa050… Parkinson … 12/41 271/88… 1.87e- 9 8.96e- 9 3.54e- 9 1812/… 12
## # ℹ 30 more rows## Mapping 'itemID' column in the FEA enrichment result table from Entrez ID to gene Symbol
set_readable(result(dup_hyperG_k_res))## # A tibble: 40 × 9
## ID Description GeneRatio BgRatio pvalue p.adjust qvalue itemID Count
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <int>
## 1 hsa050… Alcoholism 27/41 188/88… 2.60e-36 1.25e-34 4.93e-35 HDAC1… 27
## 2 hsa046… Neutrophil… 26/41 193/88… 5.17e-34 1.24e-32 4.90e-33 HDAC1… 26
## 3 hsa052… Viral carc… 26/41 205/88… 2.71e-33 4.34e-32 1.71e-32 HDAC1… 26
## 4 hsa045… Gap juncti… 12/41 92/8868 4.59e-15 5.50e-14 2.17e-14 DRD1/… 12
## 5 hsa050… Huntington… 17/41 311/88… 8.43e-15 8.09e-14 3.19e-14 HDAC1… 17
## 6 hsa041… Phagosome 11/41 159/88… 8.65e-11 6.92e-10 2.73e-10 TUBA1… 11
## 7 hsa050… Amyotrophi… 14/41 371/88… 4.97e-10 3.41e- 9 1.34e- 9 HDAC6… 14
## 8 hsa048… Motor prot… 11/41 197/88… 8.69e-10 5.21e- 9 2.06e- 9 TUBA1… 11
## 9 hsa051… Pathogenic… 11/41 203/88… 1.20e- 9 6.38e- 9 2.52e- 9 TUBA1… 11
## 10 hsa050… Parkinson … 12/41 271/88… 1.87e- 9 8.96e- 9 3.54e- 9 DRD1/… 12
## # ℹ 30 more rowsThe content of the columns extracted from the feaResult
object is described under section 4.1.1.1.
With Reactome
The same enrichment test can be performed for Reactome pathways as follows.
dup_rct_res <- tsea_dup_hyperG(drugs=drugs, type="Reactome",
pvalueCutoff=0.5, qvalueCutoff=0.5, readable=TRUE)
result(dup_rct_res)## # A tibble: 196 × 9
## ID Description GeneRatio BgRatio pvalue p.adjust qvalue itemID Count
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <int>
## 1 R-HSA-… p75NTR neg… 9/41 6/11146 0 0 0 HDAC1… 9
## 2 R-HSA-… Notch-HLH … 26/41 28/111… 5.91e-66 5.88e-64 4.05e-65 HDAC1… 26
## 3 R-HSA-… NOTCH1 Int… 26/41 48/111… 4.17e-55 2.77e-53 1.90e-54 HDAC1… 26
## 4 R-HSA-… Signaling … 26/41 58/111… 3.33e-52 8.29e-51 5.70e-52 HDAC1… 26
## 5 R-HSA-… Signaling … 26/41 58/111… 3.33e-52 8.29e-51 5.70e-52 HDAC1… 26
## 6 R-HSA-… Constituti… 26/41 58/111… 3.33e-52 8.29e-51 5.70e-52 HDAC1… 26
## 7 R-HSA-… Signaling … 26/41 58/111… 3.33e-52 8.29e-51 5.70e-52 HDAC1… 26
## 8 R-HSA-… Constituti… 26/41 58/111… 3.33e-52 8.29e-51 5.70e-52 HDAC1… 26
## 9 R-HSA-… Signaling … 26/41 74/111… 9.74e-49 2.15e-47 1.48e-48 HDAC1… 26
## 10 R-HSA-… Signaling … 26/41 235/11… 3.13e-34 6.22e-33 4.28e-34 HDAC1… 26
## # ℹ 186 more rowsmGSEA Method
The original GSEA method proposed by Subramanian et al. (2005) uses predefined gene sets S defined by functional annotation systems such as GO and KEGG. The goal is to determine whether the genes in S are randomly distributed throughout a ranked test gene list L (e.g. all genes ranked by LFC) or enriched at the top or bottom of the test list. This is expressed by an Enrichment Score (ES) reflecting the degree to which a set S is overrepresented at the extremes of L.
For TSEA, the query is a target set where duplicated entries need to be maintained. To perform GSEA with duplication support, here referred to as mGSEA, the target set is transformed to a score ranked target list Ltar of all targets provided by the corresponding annotation system. For each target in the query target set, its frequency is divided by the number of targets in the target set, which is the weight of that target. For targets present in the annotation system but absent in the target test set, their scores are set to 0. Thus, every target in the annotation system will be assigned a score and then sorted decreasingly to obtain Ltar.
In case of TSEA, the original GSEA method cannot be used directly since a large portion of zeros exists in Ltar. If the scores of the genes in set S are all zeros, NR (sum of scores of genes in set S) will be zero, which cannot be used as the denominator. In this case, ES is set to -1. If only some genes in set S have scores of zeros then NR is set to a larger number to decrease the weight of the genes in S that have non-zero scores.
The reason for this modification is that if only one gene in gene set S has a non-zero score and this gene ranks high in Ltar, the weight of this gene will be 1 resulting in an ES(S) close to 1. Thus, the original GSEA method will score the gene set S as significantly enriched. However, this is undesirable because in this example only one gene is shared among the target set and the gene set S. Therefore, giving small weights to genes in S that have scores of zero would decrease the weight of the genes in S that have scores other than zero, thereby decreasing the false positive rate. To favor truly enriched GO terms and KEGG pathways (gene set S) at the top of Ltar, only gene sets with positive ES are selected.
With GO
The following performs TSEA with the mGSEA method using the
same drug test set as in the above tsea_dup_hyperG function
call. The arguments of the tsea_mGSEA function are
explained in its help file that can be opened from R with
?tsea_mGSEA.
mgsea_res <- tsea_mGSEA(drugs=drugs, type="GO", ont="MF", exponent=1,
nPerm=1000, pvalueCutoff=1, minGSSize=5)
result(mgsea_res)## # A tibble: 93 × 11
## ont ID Description setSize enrichmentScore NES pvalue p.adjust qvalues
## <chr> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 MF GO:0… histone de… 11 1.00 3.91 0.001 0.628 0.628
## 2 MF GO:0… NAD-depend… 11 1.00 3.91 0.001 0.628 0.628
## 3 MF GO:0… NAD-depend… 16 0.839 3.66 0.029 0.725 0.725
## 4 MF GO:0… NAD-depend… 17 0.812 3.60 0.032 0.725 0.725
## 5 MF GO:0… histone de… 44 0.441 2.46 0.06 0.725 0.725
## 6 MF GO:0… protein de… 45 0.433 2.41 0.062 0.725 0.725
## 7 MF GO:0… deacetylas… 58 0.356 1.95 0.081 0.725 0.725
## 8 MF GO:0… NF-kappaB … 28 0.265 1.34 0.064 0.725 0.725
## 9 MF GO:0… hydrolase … 89 0.250 1.29 0.115 0.725 0.725
## 10 MF GO:0… potassium … 10 0.182 0.683 0.418 0.725 0.725
## # ℹ 83 more rows
## # ℹ 2 more variables: leadingEdge <chr>, ledge_rank <chr>The content of the columns extracted from the feaResult
object is described under section 4.1.1.1. The additional columns
specific to the GSEA algorithm are described here.
enrichmentScore: ES from the GSEA algorithm
(Subramanian et al. 2005). The score is
calculated by walking down the gene list L, increasing a running-sum
statistic when we encounter a gene in S and decreasing when it is not. The
magnitude of the increment depends on the gene scores. The ES is the maximum deviation
from zero encountered in the random walk. It corresponds to a weighted
Kolmogorov-Smirnov-like statistic.
NES: Normalized enrichment score. The positive and
negative enrichment scores are normalized separately by permutating the
composition of the gene list L
nPerm times, and dividing the enrichment score by the mean
of the permutation ES
with the same sign.
pvalue: The nominal p-value of the ES is calculated using a
permutation test. Specifically, the composition of the gene list L is permuted and the ES of the gene set is
recomputed for the permutated data generating a null distribution for
the ES. The p-value of the observed ES is then calculated
relative to this null distribution.
leadingEdge: Genes in the gene set S (functional
category) that appear in the ranked list L at, or before, the point where the
running sum reaches its maximum deviation from zero. It can be
interpreted as the core of a gene set that accounts for the enrichment
signal.
ledge_rank: Ranks of genes in ‘leadingEdge’ in gene list
L.
With KEGG
The same enrichment test can be performed for KEGG pathways as follows.
mgsea_k_res <- tsea_mGSEA(drugs=drugs, type="KEGG", exponent=1,
nPerm=1000, pvalueCutoff=1, minGSSize=2)
result(mgsea_k_res)## # A tibble: 48 × 10
## ID Description setSize enrichmentScore NES pvalue p.adjust qvalues
## <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 hsa05034 Alcoholism 188 0.132 0.286 0.424 0.845 0.845
## 2 hsa04540 Gap junction 92 0.129 0.448 0.25 0.845 0.845
## 3 hsa04613 Neutrophil ex… 193 0.124 0.263 0.436 0.845 0.845
## 4 hsa05203 Viral carcino… 205 0.117 0.241 0.452 0.845 0.845
## 5 hsa05031 Amphetamine a… 69 0.0943 0.374 0.401 0.845 0.845
## 6 hsa04213 Longevity reg… 62 0.0905 0.380 0.408 0.845 0.845
## 7 hsa04330 Notch signali… 62 0.0905 0.380 0.408 0.845 0.845
## 8 hsa05220 Chronic myelo… 77 0.0736 0.281 0.48 0.845 0.845
## 9 hsa04919 Thyroid hormo… 122 0.0700 0.200 0.477 0.845 0.845
## 10 hsa03083 Polycomb repr… 83 0.0685 0.251 0.52 0.845 0.845
## # ℹ 38 more rows
## # ℹ 2 more variables: leadingEdge <chr>, ledge_rank <chr>The content of the columns extracted from the feaResult
object is described under sections 4.1.1.1 and 4.1.2.1.
With Reactome
The same enrichment test can be performed for Reactome pathways as follows.
mgsea_rct_res <- tsea_mGSEA(drugs=drugs, type="Reactome", pvalueCutoff=1,
readable=TRUE)
result(mgsea_rct_res)## # A tibble: 199 × 10
## ID Description setSize enrichmentScore NES pvalue p.adjust qvalues
## <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 R-HSA-1936… p75NTR neg… 6 0.750 2.20 0.018 0.745 0.745
## 2 R-HSA-3500… Notch-HLH … 28 0.603 2.76 0.071 0.745 0.745
## 3 R-HSA-1908… Microtubul… 18 0.554 2.45 0.04 0.745 0.745
## 4 R-HSA-9701… STAT3 nucl… 11 0.529 1.99 0.028 0.745 0.745
## 5 R-HSA-1908… Transport … 19 0.525 2.33 0.044 0.745 0.745
## 6 R-HSA-3899… Post-chape… 22 0.453 2.02 0.056 0.745 0.745
## 7 R-HSA-2122… NOTCH1 Int… 48 0.412 1.87 0.112 0.745 0.745
## 8 R-HSA-3899… Formation … 25 0.399 1.81 0.064 0.745 0.745
## 9 R-HSA-9005… Loss of fu… 14 0.368 1.51 0.049 0.745 0.745
## 10 R-HSA-9005… Pervasive … 14 0.368 1.51 0.049 0.745 0.745
## # ℹ 189 more rows
## # ℹ 2 more variables: leadingEdge <chr>, ledge_rank <chr>MeanAbs Method
The input for the MeanAbs method is Ltar, the same as for mGSEA. In this enrichment statistic, mabs(S), of a gene set S is calculated as mean absolute scores of the genes in S (Fang, Tian, and Ji 2012). In order to adjust for size variations in gene set S, 1000 random permutations of Ltar are performed to determine mabs(S, π). Subsequently, mabs(S) is normalized by subtracting the median of the mabs(S, π) and then dividing by the standard deviation of mabs(S, π) yielding the normalized scores Nmabs(S). Finally, the portion of mabs(S, π) that is greater than mabs(S) is used as nominal p-value. The resulting nominal p-values are adjusted for multiple hypothesis testing using the Benjamini-Hochberg method (Benjamini and Hochberg 1995).
With GO
The following performs TSEA with the mabs method using the
same drug test set as the examples given under the dup_hyperG
and tsea_mGSEA sections. The arguments of the
tsea_mabs function are explained in its help file that can
be opened from R with ?tsea_mabs.
mabs_res <- tsea_mabs(drugs=drugs, type="GO", ont="MF", nPerm=1000,
pvalueCutoff=0.05, minGSSize=5)
result(mabs_res)## # A tibble: 83 × 10
## ont ID Description setSize mabs Nmabs pvalue p.adjust qvalues itemID
## <chr> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 MF GO:00… histone de… 11 0.0576 31.2 0 0 0 HDAC1…
## 2 MF GO:00… NAD-depend… 11 0.0576 31.2 0 0 0 HDAC1…
## 3 MF GO:00… NAD-depend… 16 0.0396 31.1 0 0 0 HDAC1…
## 4 MF GO:00… NAD-depend… 17 0.0373 31.0 0 0 0 HDAC1…
## 5 MF GO:00… histone de… 44 0.0144 29.8 0 0 0 HDAC1…
## 6 MF GO:00… protein de… 45 0.0141 29.7 0 0 0 HDAC1…
## 7 MF GO:00… deacetylas… 58 0.0109 28.9 0 0 0 HDAC1…
## 8 MF GO:00… NF-kappaB … 28 0.00784 23.2 0 0 0 HDAC1…
## 9 MF GO:00… hydrolase … 89 0.00713 27.4 0 0 0 HDAC1…
## 10 MF GO:00… potassium … 10 0.00488 10.7 0.002 0.00709 0.00152 HDAC4
## # ℹ 73 more rowsThe content of the columns extracted from the feaResult
object is explained under section 4.1.1.1. The columns specific to the
mabs algorithm are described below.
mabs: Given a scored ranked gene list L, mabs(S)
represents the mean absolute score of the genes in set S.
Nmabs: mabs(S)
normalized.
With KEGG
The same enrichment test can be performed for KEGG pathways as follows.
mabs_k_res <- tsea_mabs(drugs=drugs, type="KEGG", nPerm=1000,
pvalueCutoff=0.2, minGSSize=5)
result(mabs_k_res)## # A tibble: 44 × 9
## ID Description setSize mabs Nmabs pvalue p.adjust qvalues itemID
## <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 hsa05034 Alcoholism 188 0.00350 18.1 0 0 0 10013…
## 2 hsa04613 Neutrophil ext… 193 0.00329 17.3 0 0 0 10013…
## 3 hsa04540 Gap junction 92 0.00318 12.1 0 0 0 10376…
## 4 hsa05203 Viral carcinog… 205 0.00309 16.3 0 0 0 10013…
## 5 hsa05031 Amphetamine ad… 69 0.00247 8.98 0 0 0 1812/…
## 6 hsa04213 Longevity regu… 62 0.00236 8.37 0 0 0 3065/…
## 7 hsa04330 Notch signalin… 62 0.00236 7.24 0.001 0.00185 6.88e-4 3065/…
## 8 hsa05220 Chronic myeloi… 77 0.00190 7.63 0 0 0 3065/…
## 9 hsa04919 Thyroid hormon… 122 0.00180 8.45 0 0 0 3065/…
## 10 hsa03083 Polycomb repre… 83 0.00176 6.55 0.001 0.00185 6.88e-4 3065/…
## # ℹ 34 more rowsWith Reactome
The same enrichment test can be performed for Reactome pathways as follows.
mabs_rct_res <- tsea_mabs(drugs=drugs, type="Reactome", pvalueCutoff=1,
readable=TRUE)
result(mabs_rct_res)## # A tibble: 199 × 9
## ID Description setSize mabs Nmabs pvalue p.adjust qvalues itemID
## <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 R-HSA-193670 p75NTR neg… 6 0.0366 22.6 0 0 0 HDAC1…
## 2 R-HSA-350054 Notch-HLH … 28 0.0226 27.9 0 0 0 HDAC6…
## 3 R-HSA-9701898 STAT3 nucl… 11 0.0200 25.2 0 0 0 HDAC1…
## 4 R-HSA-190840 Microtubul… 18 0.0136 21.5 0 0 0 TUBA1…
## 5 R-HSA-2122947 NOTCH1 Int… 48 0.0132 26.4 0 0 0 HDAC6…
## 6 R-HSA-190872 Transport … 19 0.0128 21.3 0 0 0 TUBA1…
## 7 R-HSA-9022702 MECP2 regu… 8 0.0122 17.0 0 0 0 HDAC1
## 8 R-HSA-9005891 Loss of fu… 14 0.0122 19.5 0 0 0 HDAC1…
## 9 R-HSA-9005895 Pervasive … 14 0.0122 19.5 0 0 0 HDAC1…
## 10 R-HSA-9675151 Disorders … 14 0.0122 19.5 0 0 0 HDAC1…
## # ℹ 189 more rowsDSEA
Instead of translating ranked lists of drugs into target sets, as for
TSEA, the functional annotation categories of the targets can be
assigned to the drugs directly to perform Drug Set Enrichment Analysis
(DSEA) instead. Since the
drug lists from GESS results are usually unique, this strategy overcomes
the duplication problem of the TSEA approach. This way classical
enrichment methods, such as GSEA or tests based on the hypergeometric
distribution, can be readily applied without major modifications to the
underlying statistical methods. As explained above, TSEA and DSEA
performed with the same enrichment statistics are not expected to
generate identical results. Rather they often complement each other’s
strengths and weaknesses.
Hypergeometric Test
The following performs DSEA with signatureSearch's
hypergeometric test function called dsea_hyperG using the
same drug test set as the examples given under in the TSEA section. The
arguments are explained in its help file that can be opened from R with
?dsea_hyperG.
With GO
As functional annotation system the following DSEA example uses GO.
drugs <- unique(result(lincs)$pert[1:10])
hyperG_res <- dsea_hyperG(drugs=drugs, type="GO", ont="MF")
result(hyperG_res)## # A tibble: 51 × 10
## ont ID Description GeneRatio BgRatio pvalue p.adjust qvalue itemID
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 MF GO:0035… Krueppel-a… 4/8 27/225… 1.13e-10 5.89e-8 1.73e-8 vorin…
## 2 MF GO:0031… histone de… 5/8 199/22… 2.78e- 9 3.04e-7 8.93e-8 vorin…
## 3 MF GO:0032… NAD-depend… 5/8 199/22… 2.78e- 9 3.04e-7 8.93e-8 vorin…
## 4 MF GO:0017… NAD-depend… 5/8 261/22… 1.08e- 8 3.38e-7 9.93e-8 vorin…
## 5 MF GO:0034… NAD-depend… 5/8 266/22… 1.19e- 8 3.38e-7 9.93e-8 vorin…
## 6 MF GO:0031… nucleosoma… 4/8 88/225… 1.49e- 8 3.88e-7 1.14e-7 vorin…
## 7 MF GO:0001… core promo… 4/8 94/225… 1.95e- 8 4.61e-7 1.35e-7 vorin…
## 8 MF GO:0004… histone de… 5/8 318/22… 2.91e- 8 6.11e-7 1.79e-7 vorin…
## 9 MF GO:0033… protein de… 5/8 323/22… 3.15e- 8 6.11e-7 1.79e-7 vorin…
## 10 MF GO:0031… nucleosome… 4/8 106/22… 3.17e- 8 6.11e-7 1.79e-7 vorin…
## # ℹ 41 more rows
## # ℹ 1 more variable: Count <int>With KEGG
The same DSEA test can be performed for KEGG pathways as follows.
hyperG_k_res <- dsea_hyperG(drugs = drugs, type = "KEGG",
pvalueCutoff = 1, qvalueCutoff = 1,
minGSSize = 10, maxGSSize = 2000)
result(hyperG_k_res)## # A tibble: 43 × 9
## ID Description GeneRatio BgRatio pvalue p.adjust qvalue itemID Count
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <int>
## 1 hsa03082 ATP-depende… 4/8 134/20… 1.14e-7 3.46e-6 2.12e-6 vorin… 4
## 2 hsa04330 Notch signa… 4/8 146/20… 1.61e-7 3.46e-6 2.12e-6 vorin… 4
## 3 hsa03083 Polycomb re… 4/8 198/20… 5.47e-7 7.09e-6 4.34e-6 vorin… 4
## 4 hsa05034 Alcoholism 6/8 1131/2… 6.60e-7 7.09e-6 4.34e-6 vorin… 6
## 5 hsa04110 Cell cycle 5/8 815/20… 4.69e-6 4.03e-5 2.47e-5 vorin… 5
## 6 hsa05031 Amphetamine… 5/8 916/20… 8.31e-6 5.96e-5 3.65e-5 vorin… 5
## 7 hsa05203 Viral carci… 5/8 1175/2… 2.80e-5 1.57e-4 9.60e-5 vorin… 5
## 8 hsa05016 Huntington … 5/8 1185/2… 2.92e-5 1.57e-4 9.60e-5 vorin… 5
## 9 hsa04613 Neutrophil … 5/8 1237/2… 3.60e-5 1.64e-4 1.00e-4 vorin… 5
## 10 hsa04213 Longevity r… 4/8 578/20… 3.82e-5 1.64e-4 1.00e-4 vorin… 4
## # ℹ 33 more rowsThe content of the columns extracted from the feaResult object is described under section 4.1.1.1.
GSEA Method
The following performs DSEA with the GSEA method using as
test set drug labels combined with scores. Instead of using only the
drug labels in the test set, the GSEA method requires the
labels as well as the scores used for ranking the drug list in the GESS
result. The scores are usually the similarity metric used to rank the
results of the corresponding GESS method, here the NCS values from the
LINCS method. The arguments of the dsea_GSEA function are
explained in its help file that can be opened from R with
?dsea_GSEA.
With GO
As functional annotation system the following DSEA example uses GO.
dl <- abs(result(lincs)$NCS); names(dl) <- result(lincs)$pert
dl <- dl[dl>0]
dl <- dl[!duplicated(names(dl))]
gsea_res <- dsea_GSEA(drugList=dl, type="GO", ont="MF", exponent=1, nPerm=1000,
pvalueCutoff=0.2, minGSSize=5)## | | | 0% | |======================================================================| 100%## # A tibble: 54 × 11
## ont ID Description setSize enrichmentScore NES pvalue p.adjust
## <chr> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 MF GO:0004407 histone deac… 5 0.951 2.88 9.99e-4 0.0137
## 2 MF GO:0016811 hydrolase ac… 5 0.951 2.88 9.99e-4 0.0137
## 3 MF GO:0017136 NAD-dependen… 5 0.951 2.88 9.99e-4 0.0137
## 4 MF GO:0019213 deacetylase … 5 0.951 2.88 9.99e-4 0.0137
## 5 MF GO:0031078 histone deac… 5 0.951 2.88 9.99e-4 0.0137
## 6 MF GO:0032041 NAD-dependen… 5 0.951 2.88 9.99e-4 0.0137
## 7 MF GO:0033558 protein deac… 5 0.951 2.88 9.99e-4 0.0137
## 8 MF GO:0034979 NAD-dependen… 5 0.951 2.88 9.99e-4 0.0137
## 9 MF GO:0003714 transcriptio… 5 0.931 2.81 9.99e-4 0.0137
## 10 MF GO:0016810 hydrolase ac… 6 0.863 2.78 9.99e-4 0.0137
## # ℹ 44 more rows
## # ℹ 3 more variables: qvalues <dbl>, leadingEdge <chr>, ledge_rank <chr>With KEGG
The same DSEA test can be performed for KEGG pathways as follows.
gsea_k_res <- dsea_GSEA(drugList=dl, type="KEGG", exponent=1, nPerm=1000,
pvalueCutoff=1, minGSSize=5)## | | | 0% | |======================================================================| 100%## # A tibble: 71 × 10
## ID Description setSize enrichmentScore NES pvalue p.adjust qvalues
## <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 hsa03083 Polycomb rep… 5 0.913 2.72 9.99e-4 0.0177 0.0142
## 2 hsa05031 Amphetamine … 8 0.785 2.64 9.99e-4 0.0177 0.0142
## 3 hsa04613 Neutrophil e… 7 0.807 2.62 2.00e-3 0.0177 0.0142
## 4 hsa05169 Epstein-Barr… 6 0.838 2.61 2.00e-3 0.0177 0.0142
## 5 hsa04110 Cell cycle 7 0.803 2.61 2.00e-3 0.0177 0.0142
## 6 hsa05034 Alcoholism 10 0.731 2.59 9.99e-4 0.0177 0.0142
## 7 hsa04350 TGF-beta sig… 6 0.832 2.59 3.00e-3 0.0236 0.0189
## 8 hsa05202 Transcriptio… 7 0.772 2.50 2.00e-3 0.0177 0.0142
## 9 hsa04213 Longevity re… 7 0.762 2.47 2.00e-3 0.0177 0.0142
## 10 hsa05016 Huntington d… 8 0.718 2.42 4.00e-3 0.0258 0.0206
## # ℹ 61 more rows
## # ℹ 2 more variables: leadingEdge <chr>, ledge_rank <chr>Since the annotation system are drug-to-functional category mappings, the “leadingEdge” column contains identifiers of drugs instead of targets.
Comparing FEA Results
The comp_fea_res function re-ranks the functional
categories across the different FEA methods by using the mean rank of
each functional category across the 5 FEA methods. Here the functional
categories are re-ranked by their mean rank values in increasing order.
Since the functional categories are not always present in all enrichment
results, the mean rank of a functional category is corrected by an
adjustment factor that is the number of enrichment result methods used
divided by the number of occurrences of a functional category. For
instance, if a functional category is only present in the result of one
method, its mean rank will be increased accordingly. The following plots
use the pvalue column in the result tables for this ranking
approach. Alternative columns can be chosen under the
rank_stat argument. After re-ranking only the top ranking
functional categories are shown, here 20. Their number can be changed
under the Nshow argument.
table_list = list("dup_hyperG" = result(dup_hyperG_res),
"mGSEA" = result(mgsea_res),
"mabs" = result(mabs_res),
"hyperG" = result(hyperG_res),
"GSEA" = result(gsea_res))
comp_fea_res(table_list, rank_stat="pvalue", Nshow=20)
Figure 5: Ranking comparison of top GO terms across FEA methods. The dots in the plot represent the p-values of the 20 top ranking MF GO terms (y-axis) with heat color coding according to the color gradient in the legend on the right. The GO terms have been ordered from the top to the bottom of the plot by increasing mean rank values calculated for each GO term across the 5 FEA methods (x-axis).
table_list = list("dup_hyperG" = result(dup_hyperG_k_res),
"mGSEA" = result(mgsea_k_res),
"mabs" = result(mabs_k_res),
"hyperG" = result(hyperG_k_res),
"GSEA" = result(gsea_k_res))
comp_fea_res(table_list, rank_stat="pvalue", Nshow=20)
Figure 6: Ranking comparison among top KEGG pathways across FEA methods. The plot depicts the same information as the previous plot but using KEGG pathways instead of GO terms as functional annotation system.
Summary of FEA Results
The enrichment rankings of the functional categories (GO and KEGG) show a reasonable degree of agreement among the five FEA methods. For instance, all five showed a high degree of enrichment for histone deacetylase pathways that are indeed targeted by one of the query drugs, here vorinostat. Since each method has its strengths and weaknesses, the usage of a consensus approach could be considered by combining the rankings of functional categories from all or several FEA methods.
Drug-Target Network Visualization
Functional modules of the above GESS and FEA results can be rendered
as interactive drug-target networks using the dtnetplot
function from signatureSearch. For this, a character vector
of drug names along with an identifier of a chosen functional category
are passed on to the drugs and set arguments,
respectively. The resulting plot depicts the corresponding drug-target
interaction network. Its interactive features allow the user to zoom in
and out of the network, and to select network components in the
drop-down menu located in the upper left corner of the plot.
With GO
The following example demonstrates how to construct a drug-target network for two GO categories that are enriched in the results obtained in the FEA section of this vignette. This way one can visualize drug sets in the context of the cellular networks and pathways they affect.
Network for NAD-dependent histone deacetylase activity (GO:0032041)
dtnetplot(drugs = drugs(dup_hyperG_res), set = "GO:0032041", ont = "MF",
desc="NAD-dependent histone deacetylase activity (H3-K14 specific)")## No targets found in all databases for 2 drugs:
## scopolamine-n-oxide / i-070759Figure 7: Drug-target network for NAD-dependent histone deacetylase activity. The given network graph illustrates the interactions among drugs and their target proteins in the chosen pathway. Only drugs with at least one target protein are included. The nodes in the network represent drugs and targets depicted as squares and circles, respectively. The interactions among drugs and targets are indicated by non-weighted lines (yes/no relationship). The color of the nodes is defined by the number of their connections to other nodes.
Network for NF-kappaB binding (GO:0051059)
## No targets found in all databases for 2 drugs:
## scopolamine-n-oxide / i-070759Figure 8: Drug-target network for NF-kappaB binding. The details of the plot are given in the legend of Figure 7.
With KEGG
The same drug-target network plots can be rendered for KEGG pathways as follows.
Network for KEGG pathway: Alcoholism (hsa05034)
## No targets found in all databases for 2 drugs:
## scopolamine-n-oxide / i-070759Figure 9: Drug-target network for Alcoholism. The details of the plot are given in the legend of Figure 7.
Network for KEGG pathway: Longevity regulating pathway (hsa04213)
## No targets found in all databases for 2 drugs:
## scopolamine-n-oxide / i-070759Figure 10: Drug-target network for Longevity regulating pathway. The details of the plot are given in the legend of Figure 7.
Run Workflow
The runWF function supports running the entire GESS/FEA
workflow automatically when providing the query drug and cell type, as
well as selecting the reference database (e.g. lincs or
path to the custom reference database), defining the specific GESS and
FEA methods. When the query drug and cell type were provided, the query
GES was internally drawn from the reference database. The N (defined by
the N_gess_drugs argument) top ranking hits in the GESS
tables were then used for FEA where three different annotation systems
were used: GO Molecular Function (GO MF), GO Biological Process (GO BP)
and KEGG pathways.
The GESS/FEA results will be stored in a list object in R session. A
working environment named by the use case will be created under users
current working directory or under other directory defined by users.
This environment contains a folder where the GESS/FEA result tables were
written to. The working environment also contains a template Rmd
vignette as well as a rendered HTML report, users could make
modifications on the Rmd vignette as they need and re-render it to
generate their HTML report by running
rmarkdown::render("GESS_FEA_report.Rmd") in R session or
Rscript -e "rmarkdown::render('GESS_FEA_report.Rmd')" from
bash commandline.
Additional Databases
Gene Set Databases
The GES-DB introduced above store quantitative gene expression profiles. For the different GESS methods, these profiles are either used in their quantitative form or converted to non-quantitative gene sets. Additionally, signatureSearch can be used to search GES databases containing gene sets, or to use their gene set entries as queries for searching quantitative GES databases. Two examples of gene set databases are the Molecular signatures database (MSigDB) and the Gene Set Knowledgebase (GSKB) (Liberzon et al. 2011, 2015; Culhane et al. 2012; Lai et al. 2016). MSigDB contains well-annotated gene sets representing the universe of the biological processes in human. Version 7.1 of MSigDB contains 25,724 gene sets that are divided into 8 collections. GSKB is a gene set database for pathway analysis in mouse (Lai et al. 2016). It includes more than 40,000 pathways and gene sets compiled from 40 sources, such as Gene Ontology, KEGG, GeneSetDB, and others. The following introduces how to work with these gene set databases in signatureSearch.
Overview
signatureSearch provides utilities to import gene sets in gmt format from MSigDB, GSKB and related gene set-based resources. The imported gene sets can be used either as set-based queries or reference databases, or both. As queries these gene sets can be used in combination with the CMAP, LINCS or Fisher GESSs. In addition, as set databases they can be used with the Fisher and gCMAP GESSs. The following examples illustrate how to import and search gmt files in signatureSearch using sample data from MSigDB and GSKB. First, examples are provided how to use the gene sets from both resources as queries (here Entrez IDs from human) to search the CMAP2 and LINCS databases. Next, several examples are provided where the MSigDB and GSKB collections serve as reference databases for queries with compatible GESS methods.
Gene Sets as Queries
The gmt files can be downloaded from MSigDB (here),
and then imported into a user’s R session with the read_gmt
function.
msig <- read_gmt("msigdb.v7.1.entrez.gmt") # 25,724
db_path <- system.file("extdata", "sample_db.h5", package = "signatureSearch")Subsequently, an imported gene set can be used as query for searching
a reference database with the CMAP, LINCS or
Fisher methods. The following example is for unlabeled
query gene sets that lack information about up- or down-regulation.
gene_set <- msig[["GO_GROWTH_HORMONE_RECEPTOR_BINDING"]]
# CMAP method
cmap_qsig <- qSig(query=list(upset=gene_set), gess_method="CMAP", refdb=db_path)
cmap_res <- gess_cmap(cmap_qsig, workers=1)
# LINCS method
lincs_qsig <- qSig(query=list(upset=gene_set), gess_method="LINCS", refdb=db_path)
lincs_res <- gess_lincs(lincs_qsig, workers=1)
# Fisher methods
fisher_qsig <- qSig(query=list(upset=gene_set), gess_method="Fisher", refdb=db_path)
fisher_res <- gess_fisher(fisher_qsig, higher=1, lower=-1, workers=1)Alternatively, query gene sets with up and down labels can be used as shown below.
gene_set_up <- msig[["GSE17721_0.5H_VS_24H_POLYIC_BMDC_UP"]]
gene_set_down <- msig[["GSE17721_0.5H_VS_24H_POLYIC_BMDC_DN"]]
# CMAP method
cmap_qsig <- qSig(query=list(upset=gene_set_up, downset=gene_set_down),
gess_method="CMAP", refdb=db_path)
cmap_res <- gess_cmap(cmap_qsig, workers=1)
# LINCS method
lincs_qsig <- qSig(query=list(upset=gene_set_up, downset=gene_set_down),
gess_method="LINCS", refdb=db_path)
lincs_res <- gess_lincs(lincs_qsig, workers=1)
# Fisher methods
fisher_qsig <- qSig(query=list(upset=gene_set_up, downset=gene_set_down),
gess_method="Fisher", refdb=db_path)
fisher_res <- gess_fisher(fisher_qsig, higher=1, lower=-1, workers=1)Gene Sets as Database
MSigDB
The gene sets stored in the gmt file can also be used as
reference database. The following examples uses the MSigDB combined with
the gCMAP and Fisher GESS methods. For
simplicity and compatibility with both GESS methods, unlabeled gene sets
are provided to the gmt2h5 function for generating the
reference database. To cap the memory requirements, this function
supports reading and writing the gene sets in batches by defining the
by_nset parameter. Since the example uses the full
database, the generation of the HDF5 file takes some time, but this
needs to be done only once. Please note that for the gCMAP
method, this is a specialty case for the sake of having a simple gene
set database that can be used for both methods. The gCMAP
method also supports labeled gene sets represented as 0, 1, -1
matrix.
gmt2h5(gmtfile="./msigdb.v7.1.entrez.gmt", dest_h5="./msigdb.h5", by_nset=1000,
overwrite=TRUE)
# gCMAP method
query_mat <- getSig(cmp="vorinostat", cell="SKB", refdb=db_path)
gcmap_qsig2 <- qSig(query=query_mat, gess_method="gCMAP", refdb="./msigdb.h5")
gcmap_res2 <- gess_gcmap(gcmap_qsig2, higher=1, workers=1, chunk_size=2000)
# Fisher method
msig <- read_gmt("msigdb.v7.1.entrez.gmt")
gene_set <- msig[["GO_GROWTH_HORMONE_RECEPTOR_BINDING"]]
fisher_qsig2 <- qSig(query=list(upset=gene_set), gess_method="Fisher",
refdb="./msigdb.h5")
fisher_res2 <- gess_fisher(fisher_qsig2, higher=1, workers=1, chunk_size=2000)GSKB
To use the GSKB database from mouse, the corresponding gmt
file needs to be downloaded from here. The following
gCMAP GESS uses Entrez IDs from mouse for both the query
and the reference database. Since the example uses the full database,
the generation of the HDF5 file takes some time, but this needs to be
done only once. Please note that for the gCMAP method, this is
a specialty case for the sake of having a simple gene set database that
can be used for both methods. The gCMAP method also supports
labeled gene sets represented as 0, 1, -1 matrix.
gmt2h5(gmtfile="./mGSKB_Entrez.gmt", dest_h5="./mGSKB.h5", by_nset=1000,
overwrite=TRUE)
# gCMAP method
## Construct a toy query (here matrix)
gskb <- read_gmt("mGSKB_Entrez.gmt") # 41,546
mgenes <- unique(unlist(gskb))
ranks <- rev(seq_along(mgenes))
mquery <- matrix(ranks, ncol=1)
rownames(mquery) <- mgenes; colnames(mquery) <- "MAKE_UP"
gcmap_qsig3 <- qSig(query=mquery, gess_method="gCMAP",
refdb="./mGSKB.h5")
gcmap_res3 <- gess_gcmap(gcmap_qsig3, higher=1, workers=1, chunk_size=2000)
# Fisher method
gene_set <- gskb[["LIT_MM_HOFFMANN_PRE-BI_VS_LARGE-PRE-BII-CELL_DIFF_Entrez"]]
fisher_qsig3 <- qSig(query=list(upset=gene_set), gess_method="Fisher",
refdb="./mGSKB.h5")
fisher_res3 <- gess_fisher(fisher_qsig3, higher=1, workers=1, chunk_size=2000)Supplemental Material
Construction of Toy Database
Access to the LINCS database is provided via the associated
signatureSearchData package hosted on Bioconductor’s
ExperimentHub. The following provides the code for
constructing the toy database used by the sample code of this vignette.
To save time building this vignette, the evaluated components of its
sample code use a pre-generated instance of the toy database that is
stored in the extdata directory of the
signatureSearch package. Thus, the following code section
is not evaluated. It also serves as an example how to construct other
custom instances of the LINCS database. Additional details on this topic
are provided in the vignette of the signatureSearchData
package.
library(rhdf5)
eh <- ExperimentHub::ExperimentHub()
lincs <- eh[["EH3226"]]
hdacs <- c("vorinostat","trichostatin-a","pyroxamide","HC-toxin","rhamnetin")
hdacs_trts <- paste(hdacs, "SKB", "trt_cp", sep="__")
all_trts <- drop(h5read(lincs, "colnames"))
# Select treatments in SKB cell and not BRD compounds
all_trts2 <- all_trts[!grepl("BRD-", all_trts) & grepl("__SKB__", all_trts)]
set.seed(11)
rand_trts <- sample(setdiff(all_trts2, hdacs_trts), 95)
toy_trts <- c(hdacs_trts, rand_trts)
library(SummarizedExperiment); library(HDF5Array)
toy_db <- SummarizedExperiment(HDF5Array(lincs, name="assay"))
rownames(toy_db) <- HDF5Array(db_path, name="rownames")
colnames(toy_db) <- HDF5Array(db_path, name="colnames")
toy_db <- round(as.matrix(assay(toy_db)[,toy_trts]),2)
set.seed(11)
gene_idx <- sample.int(nrow(toy_db),5000)
toy_db2 <- toy_db[gene_idx,]
# The sample_db is stored in the current directory of user's R session
getwd()
createEmptyH5("sample_db.h5", level=9, delete_existing=TRUE)
append2H5(toy_db2, "sample_db.h5")
h5ls("sample_db.h5")Session Info
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] DT_0.33 signatureSearchData_1.20.0
## [3] HDF5Array_1.35.2 rhdf5_2.51.0
## [5] DelayedArray_0.33.2 SparseArray_1.7.2
## [7] S4Arrays_1.7.1 abind_1.4-8
## [9] Matrix_1.7-1 ggplot2_3.5.1
## [11] signatureSearch_1.21.0 org.Hs.eg.db_3.20.0
## [13] AnnotationDbi_1.69.0 SummarizedExperiment_1.37.0
## [15] Biobase_2.67.0 GenomicRanges_1.59.1
## [17] GenomeInfoDb_1.43.2 IRanges_2.41.1
## [19] S4Vectors_0.45.2 BiocGenerics_0.53.3
## [21] generics_0.1.3 MatrixGenerics_1.19.0
## [23] matrixStats_1.4.1 Rcpp_1.0.13-1
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 sys_3.4.3 jsonlite_1.8.9
## [4] magrittr_2.0.3 ggtangle_0.0.5 farver_2.1.2
## [7] rmarkdown_2.29 fs_1.6.5 zlibbioc_1.52.0
## [10] vctrs_0.6.5 memoise_2.0.1 ggtree_3.15.0
## [13] htmltools_0.5.8.1 AnnotationHub_3.15.0 curl_6.0.1
## [16] Rhdf5lib_1.29.0 gridGraphics_0.5-1 sass_0.4.9
## [19] bslib_0.8.0 htmlwidgets_1.6.4 plyr_1.8.9
## [22] cachem_1.1.0 buildtools_1.0.0 igraph_2.1.1
## [25] mime_0.12 lifecycle_1.0.4 pkgconfig_2.0.3
## [28] gson_0.1.0 R6_2.5.1 fastmap_1.2.0
## [31] GenomeInfoDbData_1.2.13 digest_0.6.37 aplot_0.2.3
## [34] enrichplot_1.27.1 colorspace_2.1-1 patchwork_1.3.0
## [37] ExperimentHub_2.15.0 crosstalk_1.2.1 RSQLite_2.3.8
## [40] labeling_0.4.3 filelock_1.0.3 fansi_1.0.6
## [43] httr_1.4.7 compiler_4.4.2 withr_3.0.2
## [46] bit64_4.5.2 BiocParallel_1.41.0 DBI_1.2.3
## [49] R.utils_2.12.3 rappdirs_0.3.3 tools_4.4.2
## [52] ape_5.8 R.oo_1.27.0 glue_1.8.0
## [55] nlme_3.1-166 GOSemSim_2.33.0 rhdf5filters_1.19.0
## [58] grid_4.4.2 reshape2_1.4.4 fgsea_1.33.0
## [61] gtable_0.3.6 tzdb_0.4.0 preprocessCore_1.69.0
## [64] R.methodsS3_1.8.2 tidyr_1.3.1 hms_1.1.3
## [67] data.table_1.16.2 utf8_1.2.4 XVector_0.47.0
## [70] ggrepel_0.9.6 BiocVersion_3.21.1 pillar_1.9.0
## [73] stringr_1.5.1 limma_3.63.2 yulab.utils_0.1.8
## [76] splines_4.4.2 dplyr_1.1.4 treeio_1.31.0
## [79] BiocFileCache_2.15.0 lattice_0.22-6 bit_4.5.0
## [82] annotate_1.85.0 tidyselect_1.2.1 GO.db_3.20.0
## [85] maketools_1.3.1 Biostrings_2.75.1 reactome.db_1.89.0
## [88] knitr_1.49 xfun_0.49 statmod_1.5.0
## [91] visNetwork_2.1.2 stringi_1.8.4 UCSC.utils_1.3.0
## [94] lazyeval_0.2.2 ggfun_0.1.7 yaml_2.3.10
## [97] evaluate_1.0.1 codetools_0.2-20 tibble_3.2.1
## [100] qvalue_2.39.0 BiocManager_1.30.25 graph_1.85.0
## [103] affyio_1.77.0 ggplotify_0.1.2 cli_3.6.3
## [106] xtable_1.8-4 munsell_0.5.1 jquerylib_0.1.4
## [109] dbplyr_2.5.0 png_0.1-8 XML_3.99-0.17
## [112] parallel_4.4.2 readr_2.1.5 blob_1.2.4
## [115] clusterProfiler_4.15.1 DOSE_4.1.0 tidytree_0.4.6
## [118] GSEABase_1.69.0 affy_1.85.0 scales_1.3.0
## [121] purrr_1.0.2 crayon_1.5.3 rlang_1.1.4
## [124] cowplot_1.1.3 fastmatch_1.1-4 KEGGREST_1.47.0Funding
This project is funded by NIH grants U19AG02312 and U24AG051129 awarded by the National Institute on Aging (NIA). Subcomponents of the environment are based on methods developed by projects funded by NSF awards ABI-1661152 and PGRP-1810468. The High-Performance Computing (HPC) resources used for testing and optimizing the code of this project were funded by NIH and NSF grants 1S10OD016290-01A1 and MRI-1429826, respectively.