3 Examples
3.1 Loading User Supplied PubChem Bioassay Data
This section demonstrates the process for creating a new bioactivity database from user supplied data. As an example, we will demonstrate the process of downloading an assay from the NCBI PubChem Bioassay bioactivity data repository, and loading this into a new database (Wang, et al., 2012).
First, get two files from Pubchem Bioassay for the assay of interest: an XML file containing details on how the experiment was performed, and a CSV (comma separated value) file which contains the actual activity scores. For the purposes of this example, we will use the data from assay 1000, which is a confirmatory assay (titration assay) of 57 small molecules against a mevalonate kinase protein. More details on this assay were provided in the “Quick Tutorial,” where the same data is used. These files can be downloaded from PubChem Bioassay at http://pubchem.ncbi.nlm.nih.gov/ or loaded from the example data repository included in this package as follows:
library(bioassayR)
extdata_dir <- system.file("extdata", package="bioassayR")
assayDescriptionFile <- file.path(extdata_dir, "exampleAssay.xml")
activityScoresFile <- file.path(extdata_dir, "exampleScores.csv")Next, create a new empty database for loading these data into. This example uses the R tempfile function to create the database in a random location. If you would like to keep your resulting database, replace myDatabaseFilename with your desired path and filename.
myDatabaseFilename <- tempfile()
mydb <- newBioassayDB(myDatabaseFilename, indexed=F)We will also add a data source to this database, specifying that our data here mirrors an assay provided by PubChem Bioassay.
addDataSource(mydb, description="PubChem Bioassay", version="unknown")The XML file provided by PubChem Bioassay contains extensive details on how the assay was performed, molecular targets, and results scoring methods. You can extract these using the parsePubChemBioassay function as follows. The parsePubChemBioassay function also requires a .csv file which contains the activity scores for each assay, in the standard CSV format provided by PubChem Bioassay. For data from sources other than PubChem Bioassay, you may need to write your own code to parse out the assay details- or type them in manually.
myAssay <- parsePubChemBioassay("1000", activityScoresFile, assayDescriptionFile)
myAssay## class: bioassay
## aid: 1000
## source_id: PubChem Bioassay
## assay_type: confirmatory
## organism: NA
## scoring: IC50
## targets: 116516899
## target_types: protein
## total scores: 57Next, load the resulting data parsed from the XML and CSV files into the database. This creates records in the database for both the assay itself, and it’s molecular targets.
loadBioassay(mydb, myAssay)To load additional assays, repeat the above steps. After all data is loaded, you can significantly improve subsequent query performance by adding an index to the database.
addBioassayIndex(mydb)## Creating index: note this may take a long time for a large databaseAfter indexing, perform a test query on your database to confirm that the data loaded correctly.
activeAgainst(mydb,"116516899")## fraction_active total_assays
## 16749973 1 1
## 16749974 1 1
## 16749975 1 1
## 16749976 1 1
## 16749977 1 1
## 16749978 1 1
## 16749979 1 1
## 16749980 1 1
## 16749981 1 1
## 16749982 1 1
## 16749983 1 1
## 16749984 1 1
## 16749985 1 1
## 16749986 1 1
## 16749987 1 1
## 16749988 1 1
## 16749989 1 1
## 16749990 1 1
## 16749991 1 1
## 16749992 1 1
## 16749993 1 1
## 16749994 1 1
## 16749995 1 1
## 16749996 1 1
## 16749997 1 1
## 16749998 1 1
## 16749999 1 1
## 16750000 1 1
## 16750001 1 1
## 16750002 1 1
## 16750003 1 1
## 16750004 1 1
## 16750005 1 1
## 16750006 1 1
## 16750007 1 1
## 16750016 1 1Lastly, disconnect from the database to prevent data corruption.
disconnectBioassayDB(mydb)3.2 Prebuilt Database Example: Investigate Activity of a Known Drug
A pre-built database containing large quantities of public domain bioactivity data sourced from the PubChem Bioassay database, can be downloaded from http://chemmine.ucr.edu/bioassayr. While downloading the full database is recommended, it is possible to run this example using a small subset of the database, included within the bioassayR package for testing purposes. This example demonstrates the utility of bioassayR for identifying the bioactivity patterns of a small drug-like molecule. In this example, we look at the binding activity patterns for the drug acetylsalicylic acid (aka Aspirin) and compare these binding data to annotated targets in the DrugBank drug database (Wishart, et al., 2008).
The DrugBank database is a valuable resource containing numerous data on drug activity in humans, including known molecular targets. In this exercise, first take a look at the annotated molecular targets for acetylsalicylic acid by searching this name at http://drugbank.ca. This will provide a point of reference for comparing to the bioactivity data we find in the prebuild PubChem Bioassay database. Note that DrugBank also contains the PubChem CID of this compound, which you can use to query the bioassayR PubChem Bioassay database.
To get started first connect to the database. The variable sampleDatabasePath can be replaced with the filename of the full test database you downloaded, if you would like to use that instead of the small example version included with this software package.
library(bioassayR)
extdata_dir <- system.file("extdata", package="bioassayR")
sampleDatabasePath <- file.path(extdata_dir, "sampleDatabase.sqlite")
pubChemDatabase <- connectBioassayDB(sampleDatabasePath) Next, use the activeTargets function to find all protein targets which acetylsalicylic acid shows activity against in the loaded database. These target IDs are NCBI Protein identifiers as provided by PubChem Bioassay (Tatusova, et al., 2014). In which cases do these results agree with or disagree with the annotated targets from DrugBank?
drugTargets <- activeTargets(pubChemDatabase, "2244")
drugTargets## fraction_active total_screens
## 116241312 1.00 2
## 117144 1.00 1
## 166897622 1.00 1
## 317373262 1.00 1
## 3914304 1.00 14
## 3915797 0.75 8
## 548481 1.00 21
## 6686268 1.00 1
## 84028191 1.00 1Now we would like to connect to the UniProt (Universal Protein Resource) database to obtain annotation details on these targets
(Bateman, et al., 2015). The biomaRt Bioconductor package is an excellent tool for this purpose, but works best with UniProt identifers, instead of the NCBI Protein identifiers we currently have, so we must translate the identifiers first (Durinck, et al., 2009; Durinck, et al., 2005).
We will use the translateTargetId function in bioassayR to obtain a corresponding UniProt identifier for each NCBI Protein identifier (GI). These identifier translations were obtained from UniProt and come pre-loaded into the database. The translateTargetId takes only a single query and returns one or more UniProt identifiers. Here we call it with sapply which automates calling the function multiple times, one for each protein stored in drugTargets. In many instances, a single query GI will translate into multiple UniProt identifiers. In this case, we keep only the first one as the annotation details we are looking for here will likely be the same for all of them.
# run translateTargetId on each target identifier
uniProtIds <- lapply(row.names(drugTargets), translateTargetId, database=pubChemDatabase, category="UniProt")
# if any targets had more than one UniProt ID, keep only the first one
uniProtIds <- sapply(uniProtIds, function(x) x[1])Next, we connect to UniProt via biomaRt to obtain the gene name, organism name, and protein name for each of these targets. For more information, consult the biomaRt documentation. After retrieving these data, we call the match function to ensure they are in the same order as the query data.
library(biomaRt)
unimart <- useMart("unimart")
uniprot <- useDataset("uniprot", mart=unimart)
proteinDetails <- getBM(attributes=c("accession", "gene_name", "organism", "protein_name"), filters=c("accession"), mart=uniprot, values=uniProtIds)
proteinDetails <- proteinDetails[match(uniProtIds, proteinDetails$accession),]Now we can view this annotation data.
proteinDetails## accession gene_name organism protein_name
## 4 P08684 CYP3A4 Homo sapiens Cytochrome P450 3A4
## 2 P05177 CYP1A2 Homo sapiens Cytochrome P450 1A2
## 1 O62664 PTGS1 Bos taurus Prostaglandin G/H synthase 1
## 7 P23219 PTGS1 Homo sapiens Prostaglandin G/H synthase 1
## 9 P79208 PTGS2 Ovis aries Prostaglandin G/H synthase 2
## 8 P35354 PTGS2 Homo sapiens Prostaglandin G/H synthase 2
## 3 P05979 PTGS1 Ovis aries Prostaglandin G/H synthase 1
## 6 P11712 CYP2C9 Homo sapiens Cytochrome P450 2C9
## 5 P10635 CYP2D6 Homo sapiens Cytochrome P450 2D6Last, let’s again look at our active target list, with the annotation alongside. Note, these only match up in length and order because in the above code we removed all but one UniProt ID for each target protein, and then reordered the biomaRt results with the match function to get them in the correct order.
cbind(proteinDetails[,3:4], drugTargets)## organism protein_name fraction_active total_screens
## 4 Homo sapiens Cytochrome P450 3A4 1.00 2
## 2 Homo sapiens Cytochrome P450 1A2 1.00 1
## 1 Bos taurus Prostaglandin G/H synthase 1 1.00 1
## 7 Homo sapiens Prostaglandin G/H synthase 1 1.00 1
## 9 Ovis aries Prostaglandin G/H synthase 2 1.00 14
## 8 Homo sapiens Prostaglandin G/H synthase 2 0.75 8
## 3 Ovis aries Prostaglandin G/H synthase 1 1.00 21
## 6 Homo sapiens Cytochrome P450 2C9 1.00 1
## 5 Homo sapiens Cytochrome P450 2D6 1.00 13.3 Identify Target Selective Compounds
In the previous example, acetylsalicylic acid was found to show binding activity against numerous proteins, including the COX-1 cyclooxygenase enzyme (NCBI Protein ID 166897622). COX-1 activity is theorized to be the key mechanism in this molecules function as a nonsteroidal anti-inflammatory drug (NSAID). In this example, we will look for other small molecules which selectively bind to COX-1, under the assumption that these may be worth further investigation as potential nonsteroidal anti-inflammatory drugs. This example shows how bioassayR can be used identify small molecules which selectively bind to a target of interest, and assist in the discovery of small molecule drugs and molecular probes.
First, we will start by connecting to a database. Once again, the variable sampleDatabasePath can be replaced with the filename of the full PubChem Bioassay database (downloadable from http://chemmine.ucr.edu/bioassayr), if you would like to use that instead of the small example version included with this software package.
library(bioassayR)
extdata_dir <- system.file("extdata", package="bioassayR")
sampleDatabasePath <- file.path(extdata_dir, "sampleDatabase.sqlite")
pubChemDatabase <- connectBioassayDB(sampleDatabasePath) The activeAgainst function can be used to show all small molecules in the database which demonstrate activity against COX-1 as follows. Each row name represents a small molecule cid. The column labeled “total assays” shows the total number of times each small molecule has been screened against the target of interest. The column labeled “fraction active” shows the portion of these which were annotated as active as a number between 0 and 1. This function allows users to consider different binding assays from distinct sources as replicates, to assist in distinguishing potentially spurious binding results from those with demonstrated reproducibility.
activeCompounds <- activeAgainst(pubChemDatabase, "166897622")
activeCompounds[1:10,] # look at the first 10 compounds## fraction_active total_assays
## 2244 1 1
## 2662 1 3
## 3033 1 1
## 3194 1 1
## 3672 1 1
## 3715 1 2
## 133021 1 2
## 247704 1 1
## 444899 1 1
## 445580 1 1Looking only at compounds which show binding to the target of interest is not sufficient for identifying drug candidates, as a portion of these compounds may be target unselective compounds (TUCs) which bind indiscriminately to a large number of distinct protein targets. The R function selectiveAgainst provides the user with a list of compounds that show activity against a target of interest (in at least one assay), while also showing limited activity against other targets.
The maxCompounds option limits the maximum number of results returned, and the minimumTargets option limits returned compounds to those screened against a specified minimum of distinct targets. Results are formatted as a data.frame whereby each row name represents a distinct compound. The first column shows the number of distinct targets this compound shows activity against, and the second shows the total number of targets it was screened against.
selectiveCompounds <- selectiveAgainst(pubChemDatabase, "166897622",
maxCompounds = 10, minimumTargets = 1)
selectiveCompounds## active_targets tested_targets
## 2662 1 1
## 3033 1 1
## 133021 1 1
## 11314954 1 1
## 13015959 1 1
## 44563999 1 1
## 44564000 1 1
## 44564001 1 1
## 44564002 1 1
## 44564003 1 1In the example database these compounds are only showing one tested target because very few assays are loaded. Users are encouraged to try this example for themselves with the full PubChem Bioassay database downloadable from http://chemmine.ucr.edu/bioassayr for a more interesting and informative result.
Users can combine bioassayR with the ChemmineR library to obtain structural information on these target selective compounds, and then perform further analysis- such as structural clustering, visualization, and computing physicochemical properties.
The ChemmineR software library can be used to download structural data for any of these compounds, and to visualize these structures as follows (Cao, et al., 2008). This example requires an active internet connection, as the compound structures are obtained from a remote server.
library(ChemmineR)
structures <- getIds(as.numeric(row.names(selectiveCompounds)))Here we visualize just the first four compounds found with selectiveAgainst. Consult the vignette supplied with ChemmineR for numerous examples of visualizing and analyzing these structures further.
plot(structures[1:4], print=FALSE) # Plots structures to R graphics device
3.4 Cluster Compounds by Activity Profile
This example demonstrates an example of clustering small molecules by similar bioactivity profiles across several distinct bioassay experiments. In many cases it is too cpu and memory intensive to cluster all compounds in the database, so we first pull just a subset of these data from the database into an bioassaySet object, and then convert that into a compounds vs targets activity matrix for subsequent clustering according to similarities in activity profile. The function getBioassaySetByCids extracts the activity data for a given list of compounds. Alternatively, the entire data for a given list of assay ids can be extracted with the function getAssays.
First, connect to the included sample database:
library(bioassayR)
extdata_dir <- system.file("extdata", package="bioassayR")
sampleDatabasePath <- file.path(extdata_dir, "sampleDatabase.sqlite")
sampleDB <- connectBioassayDB(sampleDatabasePath) Next, select data from just 3 compounds to extract into a bioassaySet object for subsequent analysis.
compoundsOfInterest <- c("2244", "2662", "3715")
selectedAssayData <- getBioassaySetByCids(sampleDB, compoundsOfInterest)
selectedAssayData## class: bioassaySet
## assays: 1176
## compounds: 3
## targets: 453
## sources: bioassayR_testdataThe function perTargetMatrix converts the activity data extracted earlier into a matrix of targets (rows) vs compounds (columns). Data from multiple assays hitting the same target is summarized by giving a “2” (active) if any of these data are active. Inactive combinations can optionally be included as “1” values by passing the option inactives = T. Untested combinations are assigned zeros to create a “complete matrix.” Here a sparse matrix (which omits actually storing 0 values) is used to save memory. In a sparse matrix a period “.” entry is equal to a zero “0” entry, but is implied without taking extra space in memory.
myActivityMatrix <- perTargetMatrix(selectedAssayData, inactives=T)## Note: in this version active scores now use a 2 instead of a 1myActivityMatrix[1:15,] # print the first 15 rows## 15 x 3 sparse Matrix of class "dgCMatrix"
## 2244 2662 3715
## 548481 2 . .
## 3914304 2 . .
## 3915797 2 . .
## 166897622 2 2 2
## 317373262 2 . .
## 117144 2 . .
## 6686268 2 . .
## 84028191 2 . .
## 116241312 2 . .
## 73915100 1 . .
## 160794 1 . .
## 21464101 1 . .
## 68565074 1 . .
## 4325211 1 . .
## 66528677 1 . .Note that the function perTargetMatrix also contains an additional option assayTargets which let’s you specify the targets for each assay instead of taking them from the bioassaySet object. A useful trick here is to merge assays with similar targets (such as orthologue proteins) based on target clustering results, by giving these targets the same identifier with this option.
Now, to make it possible to use binary clustering methods, we simplify the matrix into a binary matrix where “1” represents active, and “0” represents either inactive or untested combinations. We caution the user to carefully consider if this makes sense within the context of the specific experiments being analyzed.
binaryMatrix <- 1*(myActivityMatrix > 1)
binaryMatrix[1:15,] # print the first 15 rows## 15 x 3 sparse Matrix of class "dgCMatrix"
## 2244 2662 3715
## 548481 1 . .
## 3914304 1 . .
## 3915797 1 . .
## 166897622 1 1 1
## 317373262 1 . .
## 117144 1 . .
## 6686268 1 . .
## 84028191 1 . .
## 116241312 1 . .
## 73915100 0 . .
## 160794 0 . .
## 21464101 0 . .
## 68565074 0 . .
## 4325211 0 . .
## 66528677 0 . .As mentioned earlier, “0” and “.” entries in a sparse matrix are numerically equivalent.
Cluster using the built in euclidean clustering functions within R to cluster. This provides a dendrogram which indicates the similarity amongst compounds according to their activity profiles.
transposedMatrix <- t(binaryMatrix)
distanceMatrix <- dist(transposedMatrix)
clusterResults <- hclust(distanceMatrix, method="average")
plot(clusterResults)
Finally, disconnect from the database.
disconnectBioassayDB(sampleDB)3.5 Analyze and Load Raw Screening Data
This example demonstrates the basics of analyzing and loading data from a high throughput screening experiment with scores for 21,888 distinct compounds.
This example is based on the cellHTS2 library (Boutros, et al., 2006). Example data is used which is included with cellHTS2. This is actually data from screening dsRNA against live cells, however we will treat it as small molecule binding data against a protein target as the data format is the same.
First read in the screening data provided with cellHTS2.
library(cellHTS2)
library(bioassayR)
dataPath <- system.file("KcViab", package="cellHTS2")
x <- readPlateList("Platelist.txt",
name="KcViab",
path=dataPath)
x <- configure(x,
descripFile="Description.txt",
confFile="Plateconf.txt",
logFile="Screenlog.txt",
path=dataPath)
xn <- normalizePlates(x,
scale="multiplicative",
log=FALSE,
method="median",
varianceAdjust="none")Next, score and summarize the replicates.
xsc <- scoreReplicates(xn, sign="-", method="zscore")
xsc <- summarizeReplicates(xsc, summary="mean")Parse the annotation data.
xsc <- annotate(xsc, geneIDFile="GeneIDs_Dm_HFA_1.1.txt", path=dataPath)Apply a sigmoidal transformation to generate binary calls.
y <- scores2calls(xsc, z0=1.5, lambda=2)
binaryCalls <- round(Data(y))Convert the binary calls into an activity table that bioassayR can parse.
scoreDataFrame <- cbind(geneAnno(y), binaryCalls)
rawScores <- as.vector(Data(xsc))
rawScores <- rawScores[wellAnno(y) == "sample"]
scoreDataFrame <- scoreDataFrame[wellAnno(y) == "sample",]
activityTable <- cbind(cid=scoreDataFrame[,1], sid=scoreDataFrame[,1],
activity=scoreDataFrame[,2], score=rawScores)
activityTable <- as.data.frame(activityTable)
activityTable[1:10,]## cid sid activity score
## 1 CG11371 CG11371 1 2.147814893189
## 2 CG31671 CG31671 1 4.00105151825918
## 3 CG11376 CG11376 0 0.955550327380792
## 4 CG11723 CG11723 0 0.768879954745058
## 5 CG12178 CG12178 0 1.12105534792054
## 6 CG7261 CG7261 0 -0.126481089856157
## 7 CG2674 CG2674 0 0.706942495432899
## 8 CG7263 CG7263 0 0.920455054477106
## 9 CG4822 CG4822 0 0.251440513724104
## 10 CG4265 CG4265 0 0.414137389305878Create a new (temporary in this case) bioassayR database to load these data into.
myDatabaseFilename <- tempfile()
mydb <- newBioassayDB(myDatabaseFilename, indexed=F)
addDataSource(mydb, description="other", version="unknown")Create an assay object for the new assay.
myAssay <- new("bioassay",aid="1", source_id="other",
assay_type="confirmatory", organism="unknown", scoring="activity rank",
targets="2224444", target_types="protein", scores=activityTable)Load this assay object into the bioassayR database.
loadBioassay(mydb, myAssay)
mydb## class: BioassayDB
## assays: 1
## sources: other
## writeable: yesNow that these data are loaded, you can use them to perform any of the other analysis examples in this document.
Lastly, for the purposes of this example, disconnect from the example database.
disconnectBioassayDB(mydb)