gess_gcmap: gCMAP Search Method
In signatureSearch: Environment for Gene Expression Searching Combined with Functional Enrichment Analysis

Description Usage Arguments Details Value Column description References See Also Examples

Adapts the Gene Expression Signature Search (GESS) method from the gCMAP package (Sandmann et al. 2014) to make it compatible with the database containers and methods defined by signatureSearch. The specific GESS method, called gCMAP, uses as query a rank transformed GES and the reference database is composed of the labels of up and down regulated DEG sets.

gess_gcmap(
  qSig,
  higher = NULL,
  lower = NULL,
  padj = NULL,
  chunk_size = 5000,
  ref_trts = NULL,
  workers = 1
)

`qSig`	`qSig` object defining the query signature including the GESS method (should be 'gCMAP') and the path to the reference database. For details see help of `qSig` and `qSig-class`.
`higher`	The 'upper' threshold. If not 'NULL', genes with a score larger than or equal to 'higher' will be included in the gene set with sign +1. At least one of 'lower' and 'higher' must be specified. `higher` argument need to be set as `1` if the `refdb` in `qSig` is path to the HDF5 file that were converted from the gmt file.
`lower`	The lower threshold. If not 'NULL', genes with a score smaller than or equal 'lower' will be included in the gene set with sign -1. At least one of 'lower' and 'higher' must be specified. `lower` argument need to be set as `NULL` if the `refdb` in `qSig` is path to the HDF5 file that were converted from the gmt file.
`padj`	numeric(1), cutoff of adjusted p-value or false discovery rate (FDR) of defining DEGs that is less than or equal to 'padj'. The 'padj' argument is valid only if the reference HDF5 file contains the p-value matrix stored in the dataset named as 'padj'.
`chunk_size`	number of database entries to process per iteration to limit memory usage of search.
`ref_trts`	character vector. If users want to search against a subset of the reference database, they could set ref_trts as a character vector representing column names (treatments) of the subsetted refdb.
`workers`	integer(1) number of workers for searching the reference database parallelly, default is 1.

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 GES and the reference database is composed of the labels of up and down regulated DEG sets. This is the opposite situation of the orignal CMAP method from Lamb et al (2006), where the query is composed of the labels of up and down regulated DEGs and the database contains rank transformed GESs.

gessResult object, the result table contains the search results for each perturbagen in the reference database ranked by their signature similarity to the query.

Descriptions of the columns specific to the gCMAP method are given below. Note, the additional columns, those that are common among the GESS methods, are described in the help file of the gessResult object.

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.

Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Golub, T. R. (2006). The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science, 313 (5795), 1929-1935. URL: https://doi.org/10.1126/science.1132939

Sandmann, T., Kummerfeld, S. K., Gentleman, R., & Bourgon, R. (2014). gCMAP: user-friendly connectivity mapping with R. Bioinformatics , 30 (1), 127-128. URL: https://doi.org/10.1093/bioinformatics/btt592

qSig, gessResult, gess

db_path <- system.file("extdata", "sample_db.h5", 
                       package = "signatureSearch")
# 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 sample databass
# query_mat <- as.matrix(assay(sample_db[,"vorinostat__SKB__trt_cp"]))
# qsig_gcmap <- qSig(query=query_mat, gess_method="gCMAP", refdb=db_path)
# gcmap <- gess_gcmap(qsig_gcmap, higher=1, lower=-1)
# result(gcmap)