The 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.
Currently, 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.
In 2020, the LINCS 2017 database was expanded to a new beta release, here refered to as LINCS2. It contains >80k perturbations and >200 cell lines and over 3M gene expression profiles. This represents roughly a 3-fold expansion of the LINCS 2017 database, and several new data sets including CRISPSR knockouts of >5k genes and hematopoietic and non-cancer cell models. The new LINCS2 datasets can be downloaded from the clue.io site.
The data structures and search algorithms used by
signatureSearch and 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 BiocManager::install().
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.
library(signatureSearchData)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 ssd.
library(ExperimentHub)
eh <- ExperimentHub()
ssd <- query(eh, c("signatureSearchData"))
ssd## ExperimentHub with 13 records
## # snapshotDate(): 2024-10-24
## # $dataprovider: Broad Institute, GO, DrugBank, Broad Institute, STITCH
## # $species: Homo sapiens
## # $rdataclass: character, data.frame, environment, list
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH3223"]]'
##
## title
## EH3223 | cmap
## EH3224 | cmap_expr
## EH3225 | cmap_rank
## EH3226 | lincs
## EH3227 | lincs_expr
## ... ...
## EH3231 | GO_DATA
## EH3232 | GO_DATA_drug
## EH3233 | taurefList
## EH3234 | ES_NULL
## EH7297 | lincs2The titles of the data sets can be returned with
ssd$title.
ssd$title## [1] "cmap" "cmap_expr" "cmap_rank"
## [4] "lincs" "lincs_expr" "dtlink_db_clue_sti"
## [7] "goAnno" "goAnno_drug" "GO_DATA"
## [10] "GO_DATA_drug" "taurefList" "ES_NULL"
## [13] "lincs2"More detailed information about each data set can be returned as a
list, below subsetted to 10th entry with
[10].
as.list(ssd)[10]## $EH3232
## ExperimentHub with 1 record
## # snapshotDate(): 2024-10-24
## # 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.
ExperimentHubThe 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 signatureSearch package.
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$pert_idose[7]
## filter rows by 'pert_type' as compound, 10uM concentration, and 24h treatment time
meta42_filter <- sig_filter(meta42, pert_type="trt_cp", dose=dose, time="24 h") # 45956 X 14Next, the large Z-score matrix of expression signatures is imported
step-wise in subsets of manageable sizes and then appended to an HDF5
file (here lincs.h5). In this vignette, the latter is
referred to as the LINCS Z-score database. Since the size of the full
matrix is several GBs in size, it would consume too much memory to be
read into R at once. Reading the matrix in smaller batches and appending
them to an HDF5 file is much more memory efficient. Subsequently, the
HDF5Array function from the HDF5Array package
combined with the SummarizedExperiment function could
import the data from the HDF5 file into a
SummarizedExperiment object, here assigned to
se.
library(signatureSearch)
gctx <- "./data/GSE92742_Broad_LINCS_Level5_COMPZ.MODZ_n473647x12328.gctx"
gctx2h5(gctx, cid=meta42_filter$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 higher and
lower cutoffs in the gess_gcmap and
gess_fisher functions.
# 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
signatureSearch package.
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 13Next, mean expression values are calculated among biological replicates and then appended in batches to the corresponding HDF5 file.
# It takes some time
library(signatureSearch)
meanExpr2h5(gctx="./data/GSE92742_Broad_LINCS_Level3_INF_mlr12k_n1319138x12328.gctx",
inst=inst42_filter, h5file="./data/lincs_expr.h5") # 12328 X 38824The LINCS 2020 beta release data set contains 1.2 million signatures with 720,216 compound treatment GESs of 34,418 compounds, 34,171 gene over-expression on 4,040 genes, 318,208 gene knockdowns on 7,976 genes using shRNAs on 4,917 genes (177263) and CRISPR on 5,158 genes (140945). Out of 720,216 compound treatments, it includes 34,418 drug-like small molecules tested on 230 different cell lines at multiple concentrations and treatment times. To minimize redundancy of perturbagens having many signatures in different cell lines, dosage and treatment times, the ‘exemplar’ signature for each perturbagen in selected cell lines 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 generated LINCS2 dataset 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. It is exactly the same as the reference database used for the Query Tool in CLUE website. Like the LINCS database, users have the option to assemble any custom collection from the original LINCS 2020 beta release dataset.
ExperimentHubThe LINCS2 database 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
lincs2_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", "lincs2"))
lincs2_path <- eh[['EH7297']]
rhdf5::h5ls(lincs2_path) In this case the loaded data instance includes moderated Z-scores
from DE analyses of 12,328 genes for 30,456 compound treatments across a
total of 58 cell lines corresponding to 136,460 expression signatures.
This data set can be used by all set-based and correlation-based GESS
methods provided by the signatureSearch package.
The following explains how to generate the above LINCS2 data object from scratch.
Download level 5 data for compounds from CLUE as a gctx file:
In the example below, examplar signatures are identified by
downloading the meta data and selecting records with
is_exemplar_sig equal to one. These records are saved to an
object called exemplar which is used to generate compound
IDs and specify which records in the the level 5 data are imported
stepwise and appended to an HDF5 file (here lincs2.h5
referred to as LINCS2 in this vignette). Nesting the
SummarizedExperiment and HDF5Array functions
can load LINCS2 into a summarized experiment object called
sedb that can be used with the signatureSearch
package.
siginfo_beta <- fread("https://s3.amazonaws.com/macchiato.clue.io/builds/LINCS2020/siginfo_beta.txt")
exemplar <- siginfo_beta %>% filter(pert_type=="trt_cp" & is_exemplar_sig == 1)
new_cid <- paste(exemplar$pert_id, exemplar$cell_iname, rep("trt_cp", length(exemplar$cmap_name)), sep="__")
gctx2h5("level5_beta_trt_cp_n720216x12328.gctx", cid=exemplar$sig_id, new_cid=new_cid,
h5file="lincs2.h5", by_ncol=5000, overwrite=TRUE)
DBpath <- "lincs2.h5"
sedb <- SummarizedExperiment(HDF5Array(DBpath, name="assay"))
rownames(sedb) <- HDF5Array(DBpath, name="rownames")
colnames(sedb) <- HDF5Array(DBpath, name="colnames")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 gess_cmap method.
ExperimentHubThe 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.
The 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")The 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) The sub-setted rankMatrix is written to an HDF5 file,
referred to as cmap_rank database.
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")ExperimentHubTo 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).
The 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 signatureSearch package.
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 data.frame or matrix. The
gene expression data can be most types of the pre-processed gene
expression values described under section 1.4 of the
signatureSearch vignette.
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 results directory.
dir.create("data"); dir.create("data/CEL"); dir.create("results") The getCmapCEL function will download the 7,056 CEL
files from the CMap project
site, and save each of them to a subdirectory named CEL
under 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
ExperimentHub instead.
getCmapCEL(rerun=FALSE) 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
./data/chiptype.rds.
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 mas5df.
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_expr database.
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.
ExperimentHubThe preprocessed 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
signatureSearch package.
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.
The 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")limmaThe analysis of differentially expressed genes (DEGs) is performed
with the limma package.
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 degListThe 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 cmap database.
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 higher,
lower and padj cutoffs in the
gess_gcmap and 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 padj) cutoffs.
# 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)sessionInfo()## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
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## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
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## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ExperimentHub_2.15.0 AnnotationHub_3.15.0
## [3] BiocFileCache_2.15.0 dbplyr_2.5.0
## [5] BiocGenerics_0.53.1 generics_0.1.3
## [7] signatureSearchData_1.21.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.47.0 xfun_0.49 bslib_0.8.0
## [4] Biobase_2.67.0 vctrs_0.6.5 tools_4.5.0
## [7] stats4_4.5.0 curl_5.2.3 tibble_3.2.1
## [10] fansi_1.0.6 AnnotationDbi_1.69.0 RSQLite_2.3.7
## [13] blob_1.2.4 pkgconfig_2.0.3 R.oo_1.27.0
## [16] S4Vectors_0.45.0 GenomeInfoDbData_1.2.13 lifecycle_1.0.4
## [19] compiler_4.5.0 Biostrings_2.75.0 statmod_1.5.0
## [22] GenomeInfoDb_1.43.0 htmltools_0.5.8.1 sass_0.4.9
## [25] yaml_2.3.10 preprocessCore_1.69.0 pillar_1.9.0
## [28] crayon_1.5.3 jquerylib_0.1.4 R.utils_2.12.3
## [31] affy_1.85.0 cachem_1.1.0 limma_3.63.1
## [34] mime_0.12 tidyselect_1.2.1 digest_0.6.37
## [37] purrr_1.0.2 dplyr_1.1.4 BiocVersion_3.21.1
## [40] fastmap_1.2.0 cli_3.6.3 magrittr_2.0.3
## [43] utf8_1.2.4 withr_3.0.2 filelock_1.0.3
## [46] UCSC.utils_1.3.0 rappdirs_0.3.3 bit64_4.5.2
## [49] rmarkdown_2.29 XVector_0.47.0 httr_1.4.7
## [52] affyio_1.77.0 bit_4.5.0 png_0.1-8
## [55] R.methodsS3_1.8.2 memoise_2.0.1 evaluate_1.0.1
## [58] knitr_1.48 IRanges_2.41.0 rlang_1.1.4
## [61] glue_1.8.0 DBI_1.2.3 BiocManager_1.30.25
## [64] jsonlite_1.8.9 R6_2.5.1 zlibbioc_1.53.0This 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.