gess_lincs {signatureSearch} | R Documentation

## LINCS Search Method

### Description

Implements the Gene Expression Signature Search (GESS) from Subramanian et al, 2017, here referred to as LINCS. The method uses as query the two label sets of the most up- and down-regulated genes from a genome-wide expression experiment, while the reference database is composed of differential gene expression values (e.g. LFC or z-scores). Note, the related CMAP method uses here ranks instead.

### Usage

    gess_lincs(
      qSig,
      tau = FALSE,
      sortby = "NCS",
      chunk_size = 5000,
      ref_trts = NULL
    )

### Arguments

`qSig` | `qSig` object defining the query signature including the GESS method (should be 'LINCS') and the path to the reference database. For details see help of `qSig` and `qSig-class`.
`tau` | TRUE or FALSE, whether to compute the tau score. Note, TRUE is only meaningful when the full LINCS database is searched, since accurate Tau score calculation depends on the usage of the exact same database their background values are based on.
`sortby` | sort the GESS result table based on one of the following statistics: 'WTCS', 'NCS', 'Tau', 'NCSct' or 'NA'
`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.

### Details

Subramanian et al. (2017) introduced a more complex GESS algorithm, here referred to as LINCS. While related to CMAP, there are several important differences among the two approaches. First, LINCS weights the query genes based on the corresponding differential expression scores of the GESs in the reference database (e.g. LFC or z-scores). Thus, the reference database used by LINCS needs to store the actual score values rather than their ranks. Another relevant difference is that the LINCS algorithm uses a bi-directional weighted Kolmogorov-Smirnov enrichment statistic (ES) as similarity metric.

### Value

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

### Column description

Descriptions of the columns specific to the LINCS 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.

- WTCS: Weighted Connectivity Score, a bi-directional Enrichment Score for an up/down query set. If the ES values of an up set and a down set are of different signs, then WTCS is (ESup-ESdown)/2, otherwise, it is 0. WTCS values range from -1 to 1. They are positive or negative for signatures that are positively or inversely related, respectively, and close to zero for signatures that are unrelated.

- WTCS_Pval: Nominal p-value of WTCS computed by comparing WTCS against a null distribution of WTCS values obtained from a large number of random queries (e.g. 1000).

- WTCS_FDR: False discovery rate of WTCS_Pval.

- NCS: Normalized Connectivity Score. To make connectivity scores comparable across cell types and perturbation types, the scores are normalized. Given a vector of WTCS values w resulting from a query, the values are normalized within each cell line c and perturbagen type t to obtain NCS by dividing the WTCS value with the signed mean of the WTCS values within the subset of the signatures in the reference database corresponding to c and t.

- Tau: Enrichment score standardized for a given database. The Tau score compares an observed NCS to a large set of NCS values that have been pre-computed for a specific reference database. The query results are scored with Tau as a standardized measure ranging from 100 to -100. A Tau of 90 indicates that only 10 stronger connectivity to the query. This way one can make more meaningful comparisons across query results.

- TauRefSize: Size of reference perturbations for computing Tau.

- NCSct: NCS summarized across cell types. Given a vector of NCS values for perturbagen p, relative to query q, across all cell lines c in which p was profiled, a cell-summarized connectivity score is obtained using a maximum quantile statistic. It compares the 67 and 33 quantiles of NCSp,c and retains whichever is of higher absolute magnitude.

### References

For detailed description of the LINCS method and scores, please refer to: Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Golub, T. R. (2017). A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell, 171 (6), 1437-1452.e17. URL: https://doi.org/10.1016/j.cell.2017.10.049

### See Also

`qSig`, `gessResult`, `gess`

### Examples

    db_path <- system.file("extdata", "sample_db.h5",
                           package = "signatureSearch")
    #qsig_lincs <- qSig(query = list(
    #                   upset=c("230", "5357", "2015", "2542", "1759"),
    #                   downset=c("22864", "9338", "54793", "10384", "27000")),
    #                   gess_method = "LINCS", refdb = db_path)
    #lincs <- gess_lincs(qsig_lincs, sortby="NCS", tau=FALSE)
    #result(lincs)

[Package signatureSearch version 1.0.4 Index]