signatureSearchData package provides access to the reference data used by the associated
signatureSearch software package (Duan et al. 2020). The latter allows to search with a query gene expression signature (GES) a database of treatment GESs to identify cellular states sharing similar expression responses (connections). This way one can identify drugs or gene knockouts that induce expression phenotypes similar to a sample of interest. The resulting associations may lead to novel functional insights how perturbagens of interest interact with biological systems.
signatureSearchData includes GES data from the CMap (Connectivity Map) and LINCS (Library of Network-Based Cellular Signatures) projects that are largely based on drug and genetic perturbation experiments performed on variable numbers of human cell lines (Lamb et al. 2006; Subramanian et al. 2017). In
signatureSearchData these data sets have been preprocessed to be compatible with the different gene expression signature search (GESS) algorithms implemented in
signatureSearch. The preprocessed data types include but are not limited to normalized gene expression values (e.g. intensity values), log fold changes (LFC) and Z-scores, p-values or FDRs of differentially expressed genes (DEGs), rankings based on selected preprocessing routines or sets of top up/down-regulated DEGs.
The CMap data were downloaded from the CMap project site (Version build02). The latter is a collection of over 7,000 gene expression profiles (signatures) obtained from perturbation experiments with 1,309 drug-like small molecules on five human cancer cell lines. The Affymetrix Gene Chip technology was used to generate the CMAP2 data set.
In 2017, the LINCS Consortium generated a similar but much larger data set where the total number of gene expression signatures was scaled up to over one million. This was achieved by switching to a much more cost effective gene expression profiling technology called L1000 assay (Peck et al. 2006; Edgar, Domrachev, and Lash 2002). The current set of perturbations covered by the LINCS data set includes 19,811 drug-like small molecules applied at variable concentrations and treatment times to ~70 human non-cancer (normal) and cancer cell lines. Additionally, it includes several thousand genetic perturbagens composed of gene knockdown and over-expression experiments.
The data structures and search algorithms used by
signatureSearchData are designed to work with most genome-wide expression data including hybridization-based methods, such as Affymetrix or L1000, as well as sequencing-based methods, such as RNA-Seq. Currently,
signatureSearchData does not include preconfigured RNA-Seq reference data mainly due to the lack of large-scale perturbation studies (e.g. drug-based) available in the public domain that are based on RNA-Seq. This situation may change in the near future once the technology has become more affordable for this purpose.
signatureSearchData is a R/Bioconductor package and can be installed using
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("signatureSearchData")
After the package is installed, it can be loaded in an R session as follows.
A summary of the data sets provided by the
signatureSearchData package can be obtained with the
query function of the
ExperimentHub package. The information is stored in an object of class
ExperimentHub, here assigned to
library(ExperimentHub) eh <- ExperimentHub() ssd <- query(eh, c("signatureSearchData")) ssd
## ExperimentHub with 12 records ## # snapshotDate(): 2020-10-02 ## # $dataprovider: Broad Institute, GO, DrugBank, Broad Institute, STITCH ## # $species: Homo sapiens ## # $rdataclass: character, data.frame, environment, list ## # additional mcols(): taxonomyid, genome, description, ## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags, ## # rdatapath, sourceurl, sourcetype ## # retrieve records with, e.g., 'object[["EH3223"]]' ## ## title ## EH3223 | cmap ## EH3224 | cmap_expr ## EH3225 | cmap_rank ## EH3226 | lincs ## EH3227 | lincs_expr ## ... ... ## EH3230 | goAnno_drug ## EH3231 | GO_DATA ## EH3232 | GO_DATA_drug ## EH3233 | taurefList ## EH3234 | ES_NULL
The titles of the data sets can be returned with
##  "cmap" "cmap_expr" "cmap_rank" ##  "lincs" "lincs_expr" "dtlink_db_clue_sti" ##  "goAnno" "goAnno_drug" "GO_DATA" ##  "GO_DATA_drug" "taurefList" "ES_NULL"
More detailed information about each data set can be returned as a
list, below subsetted to 10th entry with
## $EH3232 ## ExperimentHub with 1 record ## # snapshotDate(): 2020-10-02 ## # names(): EH3232 ## # package(): signatureSearchData ## # $dataprovider: GO ## # $species: Homo sapiens ## # $rdataclass: environment ## # $rdatadateadded: 2019-10-22 ## # $title: GO_DATA_drug ## # $description: GO annotation environment after drug mappings ## # $taxonomyid: 9606 ## # $genome: GRCh38 ## # $sourcetype: MySQL ## # $sourceurl: https://bioconductor.org/packages/release/data/annotation/html... ## # $sourcesize: NA ## # $tags: c("ExperimentHub", "ExperimentData") ## # retrieve record with 'object[["EH3232"]]'
Details about the usage of
ExperimentHub can be found in its vignettes here.
The L1000 assay, used for generating the LINCS data, measures the expression of 978 landmark genes and 80 control genes by loading amplified mRNA populations onto beads and then detecting their abundance with a fluorescent-based method (Peck et al. 2006). The expression of 11,350 additional genes is imputed from the landmark genes by using as training data a large collection of Affymetrix gene chips (Edgar, Domrachev, and Lash 2002).
The LINCS data have been pre-processed by the Broad Institute to 5 different levels and are available for download from GEO. Level 1 data are the raw mean fluorescent intensity values that come directly from the Luminex scanner. Level 2 data are the expression intensities of the 978 landmark genes. They have been normalized and used to impute the expression of the additional 11,350 genes, forming Level 3 data. A robust z-scoring procedure was used to generate differential expression values from the normalized profiles (Level 4). Finally, a moderated z-scoring procedure was applied to the replicated samples of each experiment (mostly 3 replicates) to compute a weighted average signature (Level 5). For a more detailed description of the preprocessing methods used by the LINCS project, readers want to refer to the LINCS user guide.
Disregarding replicates, the LINCS data set contains 473,647 signatures with unique cell type and treatment combinations. This includes 19,811 drug-like small molecules tested on different cell lines at multiple concentrations and treatment times. In addition to compounds, several thousand genetic perturbations (gene knock-downs and over expressions) have been tested. Currently, the data described in this vignette are restricted to signatures of small molecule treatments across different cells lines. However, users have the option to assemble any custom collection of the LINCS data. For consistency, only signatures at one specific concentration (10\(\mu\)M) and one time point (24h) have been selected for each small molecule in the default collection. These choices are similar to the conditions used in primary high-throughput compound screens of cell lines. Since the selected compound concentrations and treatment duration have not been tested by LINCS across all cell types yet, a subset of compounds had to be selected that best met the chosen treatment requirements. This left us with 8,104 compounds that were uniformly tested at the chosen concentration and treatment time, but across variable numbers of cell lines. The total number of expression signatures meeting this requirement is 45,956, while the total number of cell lines included in this data set is 30.
The LINCS sub-dataset, filtered and assembled according to the above criteria, can be downloaded from Bioconductor’s
ExperimentHub as HDF5 file. In the example below, the path to this file is assigned to a character vector called
lincs_path. A summary of the content of the HDF5 file can be returned with the
h5ls function. Note, due to the large size of the LINCS data set, its download takes too much time to evaluate the following code section during the build time of this vignette.
library(ExperimentHub); library(rhdf5) eh <- ExperimentHub() query(eh, c("signatureSearchData", "lincs")) lincs_path <- eh[['EH3226']] rhdf5::h5ls(lincs_path)
In this case the loaded data instance includes moderated Z-scores from DE analyses of 12,328 genes for 8,140 compound treatments across a total of 30 cell lines corresponding to 45,956 expression signatures. This data set can be used by all set-based and correlation-based GESS methods provided by the
The following explains how to generate the above LINCS data object from scratch. This also illustrates how to filter the LINCS level 5 data in other ways.
Download and unzip the following files from GEO entry GSE92742:
The following code examples assume that the downloaded datasets are stored in a sub-directory called
data. All paths in this vignette are given relative to the present working directory of a user’s R session.
The following selects LINCS Level 5 signatures of compound treatments at a concentration of 10\(\mu\)M and a treatment time of 24 hours. Note, the import command below may issue a warning message that can be ignored.
meta42 <- readr::read_tsv("./data/GSE92742_Broad_LINCS_sig_info.txt") dose <- meta42$pert_idose ## filter rows by 'pert_type' as compound, 10uM concentration, and 24h treatment time meta42_filter <- sig_filter(meta42, pert_type="trt_cp", dose=dose, time="24 h") # 45956 X 14
Next, the large Z-score matrix of expression signatures is imported step-wise in subsets of manageable sizes and then appended to an HDF5 file (here
lincs.h5). In this vignette, the latter is referred to as the LINCS Z-score database. Since the size of the full matrix is several GBs in size, it would consume too much memory to be read into R at once. Reading the matrix in smaller batches and appending them to an HDF5 file is much more memory efficient. Subsequently, the
HDF5Array function from the
HDF5Array package combined with the
SummarizedExperiment function could import the data from the HDF5 file into a
SummarizedExperiment object, here assigned to
library(signatureSearch) gctx <- "./data/GSE92742_Broad_LINCS_Level5_COMPZ.MODZ_n473647x12328.gctx" gctx2h5(gctx, cid=meta42_filter$sig_id, new_cid=meta42_filter$pert_cell_factor, h5file="./data/lincs.h5", by_ncol=5000, overwrite=TRUE) library(HDF5Array) se <- SummarizedExperiment(HDF5Array("./data/lincs.h5", name="assay")) rownames(se) <- HDF5Array("./data/lincs.h5", name="rownames") colnames(se) <- HDF5Array("./data/lincs.h5", name="colnames")
The DEGs for the LINCS level 5 Z-score database can be defined by users by setting the cutoffs of Z-scores (e.g. +2 and -2) to define up/down regulated DEGs. The cutoff parameters of defining DEGs are available as the argument of the GESS methods when the reference database needs to be DEG sets and the
lincs Z-score data are provided (only for gCMAP and Fisher GESS methods). The query gene sets could also be defined by users by either selecting 150 up and down genes or defining cutoffs of Z-scores. The query gene sets can be used for CMAP, LINCS GESS methods. The following codes show examples of defining DEGs used as query and defining DEG sets used as reference database.
Defining query gene sets
library(signatureSearch) # Get up and down 150 DEGs degs <- getDEGSig(cmp="vorinostat", cell="SKB", refdb="lincs", Nup=150, Ndown=150) # Get DEGs by setting cutoffs degs2 <- getDEGSig(cmp="vorinostat", cell="SKB", refdb="lincs", higher=2, lower=-2)
Defining gene sets reference database. The LINCS Z-score reference database will be internally converted to the gene sets database in forms of the 0, 1, -1 matrix when user defining the
lower cutoffs in the
# gCMAP method gep <- getSig("vorinostat", "SKB", refdb="lincs") qsig_gcmap <- qSig(query = gep, gess_method = "gCMAP", refdb = "lincs") gcmap_res <- gess_gcmap(qsig_gcmap, higher=2, lower=-2) # Fisher method qsig_fisher <- qSig(query = degs, gess_method = "Fisher", refdb = "lincs") fisher_res <- gess_fisher(qSig=qsig_fisher, higher=2, lower=-2)
The LINCS Level 3 data can be downloaded from GEO the same way as described above for the Level 5 data. The Level 3 data contain normalized gene expression values across all treatments and cell lines used by LINCS. The Level 3 signatures were filtered using the same dosage and duration criteria as the Level 5 data. The biological replicate information included in the Level 3 data were collapsed to mean values. Subsequently, the resulting matrix of mean expression values was written to an HDF5 file. The latter is referred to as
lincs_expr database containing 38,824 signatures for a total of 5,925 small molecule treatments and 30 cell lines. Although the LINCS Level 3 and 5 data are filtered here the same way, the number of small molecules represented in the Level 3 data (5,925) is smaller than in the Level 5 data (8,140). The reason for this inconsistency is most likely that the Level 3 dataset, downloadable from GEO, is incomplete.
The filtered and processed LINCS Level3 data (
lincs_expr) can be loaded from Bioconductor’s
ExperimentHub interface as follows.
library(ExperimentHub) eh <- ExperimentHub() query(eh, c("signatureSearchData", "lincs_expr")) lincs_expr_path <- eh[['EH3227']]
In this case the loaded
lincs_expr instance includes mean expression values of 12,328 genes for 5,925 compound treatments across a total of 30 cell lines. This data set can be used by all correlation-based GESS methods provided by the
The following steps explain how to generate the above data set from scratch. This also illustrates how to filter the LINCS Level 3 data in other ways.
Download and unzip the following files from GEO entry GSE92742:
As above, the following code examples assume that the downloaded datasets are stored in a sub-directory called
data. All paths in this vignette are given relative to the present working directory of a user’s R session.
The following selects LINCS Level 3 signatures of compound treatments at a concentration of 10\(\mu\)M and a treatment time of 24 hours.
inst42 <- readr::read_tsv("./data/GSE92742_Broad_LINCS_inst_info.txt") inst42_filter <- inst_filter(inst42, pert_type="trt_cp", dose=10, dose_unit="um", time=24, time_unit="h") # 169,795 X 13
Next, mean expression values are calculated among biological replicates and then appended in batches to the corresponding HDF5 file.
# It takes some time meanExpr2h5(gctx="./data/GSE92742_Broad_LINCS_Level3_INF_mlr12k_n1319138x12328.gctx", inst=inst42_filter, h5file="./data/lincs_expr.h5") # 12328 X 38824
CMap2 (Version build02) contains GESs for 1,309 drugs and eight cell lines that were generated with Affymetrix Gene Chips as expression platform. In some cases this includes drug treatments at different concentrations and time points. For consistency, the CMap2 data was reduced to drug treatments with concentrations and time points that are comparable to those used for the above LINCS data. CMap2 data can be downloaded from GEO or its project site either in raw format or as rank transformed matrix. The ranks are based on DEG analyses of drug treatments (drug vs. no-drug) where the resulting Z-scores were used to generate the rank matrix. The latter was used here and is referred to as
rankMatrix. The Affymetrix probe set identifiers stored in the row name slot of this matrix were translated into gene identifies. To obtain a matrix with unique gene identifiers, the ranks for genes represented by more than one probe set were averaged and then re-ranked accordingly. This final gene level rank matrix, referred to as
cmap_rank, contains rank profiles for 12,403 genes from 1,309 compound treatments in up to 5 cells corresponding to a total of 3,587 treatment signatures. This matrix can be used for all GESS methods in the
signatureSearch package that are compatible with rank data, such as the
cmap_rank data can be downloaded from Bioconductor’s
ExperimentHub as HDF5 file. Since CMap2 is much smaller than LINCS, it can be imported in its entirety into a
SummarizedExperiment object (here assigned to
se) without excessive memory requirements.
library(ExperimentHub) eh <- ExperimentHub() query(eh, c("signatureSearchData", "cmap_rank")) cmap_rank_path <- eh[["EH3225"]] se <- SummarizedExperiment(HDF5Array(cmap_rank_path, name="assay")) rownames(se) <- HDF5Array(cmap_rank_path, name="rownames") colnames(se) <- HDF5Array(cmap_rank_path, name="colnames")
The following steps explain how to generate the above CMap2 rank data set from scratch.
rankMatrix can be downloaded from the CMap project site here. The specific file to download from this site is rankMatrix.txt.zip. As before, it should be saved and unzipped in the
data directory of a user’s R session.
The following selects from
rankMatrix for each compound the chosen treatment concentration and time point. This is achieved with help of the experiment annotation file
cmap_instances_02.txt, also available from the CMap project site. Since this file is relatively small it has been included in the
signatureSearchData package from where it can be loaded into R as shown below.
path <- system.file("extdata", "cmap_instances_02.txt", package="signatureSearchData") cmap_inst <- read.delim(path, check.names=FALSE) inst_id <- cmap_inst$instance_id[!duplicated(paste(cmap_inst$cmap_name, cmap_inst$cell2, sep="_"))] rankM <- read.delim("./data/rankMatrix.txt", check.names=FALSE, row.names=1) # 22283 X 6100 rankM_sub <- rankM[, as.character(inst_id)] colnames(rankM_sub) <- unique(paste(cmap_inst$cmap_name, cmap_inst$cell2, "trt_cp", sep="__"))
The following generates annotation information for Affymetirx probe set identifiers. Note, the three different Affymetrix chip types (HG-U133A, HT_HG-U133A, U133AAofAv2) used by CMap2 share nearly all probe set identifiers, meaning it is possible to use the same annotation package (here
hgu133a.db) for all three.
library(hgu133a.db) myAnnot <- data.frame(ACCNUM=sapply(contents(hgu133aACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(hgu133aSYMBOL), paste, collapse=", "), UNIGENE=sapply(contents(hgu133aUNIGENE), paste, collapse=", "), ENTREZID=sapply(contents(hgu133aENTREZID), paste, collapse=", "), ENSEMBL=sapply(contents(hgu133aENSEMBL), paste, collapse=", "), DESC=sapply(contents(hgu133aGENENAME), paste, collapse=", ")) saveRDS(myAnnot, "./data/myAnnot.rds")
probe2gene function transforms probe set to gene level data. If genes are represented by several probe sets then their mean intensities are used.
rankM_sub_gene <- probe2gene(rankM_sub, myAnnot)
rankMatrix is written to an HDF5 file, referred to as
matrix2h5(rankM_sub_gene, "./data/cmap_rank.h5", overwrite=TRUE) # 12403 X 3587 rhdf5::h5ls("./data/cmap_rank.h5") ## Read in cmap_rank.h5 as SummarizedExperiment object se <- SummarizedExperiment(HDF5Array("./data/cmap_rank.h5", name="assay")) rownames(se) <- HDF5Array("./data/cmap_rank.h5", name="rownames") colnames(se) <- HDF5Array("./data/cmap_rank.h5", name="colnames")
To search CMap2 with
signatureSearch's correlation based GESS methods (
gess_cor), normalized gene expression values (here intensities) are required where the biological replicate information has been collapsed to mean values. For this, the
cmap_expr database has been created from CEL files, which are the raw data of the Affymetrix technology. To obtain normalized expression data, the CEL files were downloaded from the CMap project site, and then processed with the MAS5 algorithm. Gene level expression data was generated the same way as described above. Next, the gene expression values for different concentrations and treatment times of each compound and cell were averaged. Subsequently, the expression matrix was saved to an HDF5 file, referred to as the
cmap_expr database. It represents mean expression values of 12,403 genes for 1,309 compound treatments in up to 5 cells (3,587 signatures in total).
cmap_expr database can be downloaded as HDF5 file from Bioconductor’s
ExperimentHub as follows.
library(ExperimentHub) eh <- ExperimentHub() query(eh, c("signatureSearchData", "cmap_expr")) cmap_expr_path <- eh[["EH3224"]]
This data set can be used by all correlation-based GESS methods provided by the
How to generate the above
cmap_expr database from scratch is explained in the Supplementary Material section of this vignette (see Section 8).
Custom databases of GESs can be built with the
build_custom_db function provided by the
signatureSearch package. For this the user provides custom genome-wide gene expression data (e.g. for drug, disease or genetic perturbations) in a
matrix. The gene expression data can be most types of the pre-processed gene expression values described under section 1.4 of the
signatureSearchData package also contains several annotation datasets, such as drug-target information of small molecules. They are required for
signatureSearch's functional enrichment analysis (FEA) routines. Currently, most of these annotation data were downloaded from the following databases:
The following steps explain how to generate the
cmap_expr database in subsection 5.3 from scratch. They are intended for expert users and have been included here for reproduciblity reasons.
The large number of files processed in the next steps are organized in two sub-directories of a user’s R session. Input files will be stored in a
data directory, while all output files will be written to a
dir.create("data"); dir.create("data/CEL"); dir.create("results")
getCmapCEL function will download the 7,056 CEL files from the CMap project site, and save each of them to a subdirectory named
data. Since this download step will take some time, the argument
rerun has been assigned
FALSE in the below function call to avoid running it accidentally. To execute the download, the argument
rerun needs to be assigned
TRUE. If the raw data are not needed, users can skip this time consuming step and work with the preprocessed
cmap_expr database downloaded from the
The CMAP data set is based on three different Affymetrix chip types (HG-U133A, HT_HG-U133A and U133AAofAv2). The following extracts the chip type information from the downloaded CEL files and stores the information in an
rds file with the path
library(affxparser) celfiles <- list.files("./data/CEL", pattern=".CEL$") chiptype <- sapply(celfiles, function(x) affxparser::readCelHeader(paste0("data/CEL/", x))$chiptype) saveRDS(chiptype, "./data/chiptype.rds")
The following processes the CEL files from each chip type separately using the MAS5 normalization algorithm. The results will be written to 3 subdirectores under
data that are named after the chip type names. To reduce the memory consumption of this step, the CEL files are normalized in batches of 200. The normalization takes about 10 hours without parallelization. To save time, this process can be easily accelerated on a computer cluster.
chiptype <- readRDS("./data/chiptype.rds") chiptype_list <- split(names(chiptype), as.character(chiptype)) normalizeCel(chiptype_list, batchsize=200, rerun=FALSE)
Next the results from each chip type are assembled in a data frame. After this all three of these data frames are combined to a single one, here named
chiptype_dir <- unique(chiptype) combineResults(chiptype_dir, rerun=FALSE) mas5df <- combineNormRes(chiptype_dir, norm_method="MAS5")
After moving the
myAnnot.rds file from above into the
data directory, the
probe2gene function is used to transforms probe set to gene level data. If genes are represented by several probe sets then their mean intensities are used.
myAnnot <- readRDS("./data/myAnnot.rds") mas5df <- probe2gene(mas5df, myAnnot) saveRDS(mas5df,"./data/mas5df.rds")
The following averages the normalized gene expression values for different concentrations, treatment times and replicates of compounds and cell types.
mas5df <- readRDS("./data/mas5df.rds") # dim: 12403 x 7056 path <- system.file("extdata", "cmap_instances_02.txt", package="signatureSearchData") cmap_inst <- read.delim(path, check.names=FALSE) cmap_drug_cell_expr <- meanExpr(mas5df, cmap_inst) # dim: 12403 X 3587 saveRDS(cmap_drug_cell_expr, "./data/cmap_drug_cell_expr.rds")
The normalized and averaged expression values are saved to an HDF5 file, referred to as
cmap_drug_cell_expr <- readRDS("./data/cmap_drug_cell_expr.rds") ## match colnames to '(drug)__(cell)__(factor)' format colnames(cmap_drug_cell_expr) <- gsub("(^.*)_(.*$)", "\\1__\\2__trt_cp", colnames(cmap_drug_cell_expr)) matrix2h5(cmap_drug_cell_expr, "./data/cmap_expr.h5", overwrite=TRUE) h5ls("./data/cmap_expr.h5")
The MAS5 normalized CEL files from the
CMap2 Intensities from Sources section can be used for DE analysis with
limma package to get the
logMA matrix containing the LFC scores. The treatment v.s. control instances were defined in the
cmap_instances_02.txt. The same as the
cmap_expr database, only one treatment condition is selected for a compound in a cell. So, the resulting
logMA matrix has LFC scores of 1,281 compound treatments in 5 cells (3,478 signatures in total). The latter was stored in an HDF5 file, which is referred to as the
cmap database. Note, The number of compound treatments in
cmap database is slightly different from that of the
cmap_expr database. The reason is that some of the compound treatment is discarded if the number of control and treatment samples are less than 3 during the DE analysis.
cmap database can be loaded through the
ExperimentHub interface as follows.
library(ExperimentHub) eh <- ExperimentHub() query(eh, c("signatureSearchData", "cmap")) cmap_path <- eh[["EH3223"]]
In summary, the loaded data instance includes LFC scores of 12,403 genes of 1,281 compound treatments across a total of 5 cell lines. This data set can be used by all GESS methods provided by the
The following steps explain how to generate the above data set from the MAS5 normalized expression matrix of CEL files (
mas5df) generated at the
CMap2 Intensities from Sources section. Use the same working directory as
cmap_expr signature database.
sampleList function extracts the sample comparisons (contrasts) from the CMAP annotation table and stores them as a list.
path <- system.file("extdata", "cmap_instances_02.txt", package="signatureSearchData") cmap_inst <- read.delim(path, check.names=FALSE) comp_list <- sampleList(cmap_inst, myby="CMP_CELL")
The analysis of differentially expressed genes (DEGs) is performed with the
mas5df <- readRDS("./data/mas5df.rds") degList <- runLimma(df=log2(mas5df), comp_list, fdr=0.10, foldchange=1, verbose=TRUE) saveRDS(degList, "./results/degList.rds") # saves entire degList
logMA contains the LFC scores of compound treatments in cells. The LFC as well as the FDR matrix are saved to an HDF5 file, which is the
degList <- readRDS("./results/degList.rds") logMA <- degList$logFC ## match colnames of logMA to '(drug)__(cell)__(factor)' format colnames(logMA) <- gsub("(^.*)_(.*$)", "\\1__\\2__trt_cp", colnames(logMA)) fdr <- degList$FDR colnames(fdr) <- gsub("(^.*)_(.*$)", "\\1__\\2__trt_cp", colnames(fdr)) matrix2h5(logMA, "./data/cmap.h5", name="assay", overwrite=TRUE) # 12403 X 3478 matrix2h5(fdr, "./data/cmap.h5", name="padj", overwrite=FALSE) rhdf5::h5ls("./data/cmap.h5")
The DEGs for the CMAP2 database can be defined by users by setting the cutoffs of LFC as well as the adjusted p-value or FDR to define up/down regulated DEGs if the p-value matrix is available in the CMAP HDF5 file. The cutoff parameters of defining DEGs are available as the argument of the GESS methods when the reference database needs to be DEG sets (only for gCMAP and Fisher GESS methods).
The query gene sets could also be defined by users by either selecting 150 up and down genes or defining cutoffs of LFC and FDRs. The query gene sets can be used for CMAP, LINCS GESS methods. The following codes show examples of defining DEGs used as query and defining DEG sets used as reference database.
Defining query gene sets
library(signatureSearch) # Get up and down 150 DEGs degs <- getDEGSig(cmp="vorinostat", cell="PC3", refdb="cmap", Nup=150, Ndown=150) # Get DEGs by setting cutoffs degs2 <- getDEGSig(cmp="vorinostat", cell="PC3", refdb="cmap", higher=1, lower=-1, padj=0.05)
Defining gene sets reference database. The CMAP2 reference database will be internally converted to the gene sets database in forms of the 0, 1, -1 matrix when user defining the
padj cutoffs in the
gess_fisher functions. The
padj argument is supported when the reference database contains both the LFC score and p-value matrix, so it is possible to define DEGs combined from the LFC and p-value (could be either p-value, adjusted p-value or FDR depending on the type of p-value stored in dataset named as
# gCMAP method gep <- getSig("vorinostat", "PC3", refdb="cmap") qsig_gcmap <- qSig(query = gep, gess_method = "gCMAP", refdb = "cmap") gcmap_res <- gess_gcmap(qsig_gcmap, higher=1, lower=-1, padj=0.05) # Fisher method qsig_fisher <- qSig(query = degs, gess_method = "Fisher", refdb = "cmap") fisher_res <- gess_fisher(qSig=qsig_fisher, higher=1, lower=-1, padj=0.05)
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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 optimizing and applying the code of this project were funded by NIH and NSF grants 1S10OD016290-01A1 and MRI-1429826, respectively.
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