gess: GESS Methods
In girke-lab/signatureSearch: Environment for Gene Expression Searching Combined with Functional Enrichment Analysis
gess_cmap R Documentation
GESS Methods
Description
The CMAP search method implements the original Gene Expression Signature
Search (GESS) from Lamb et al (2006) known as Connectivity Map (CMap).
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 rank transformed expression profiles
(e.g. ranks of LFC or z-scores).
Correlation-based similarity metrics, such as Spearman or Pearson
coefficients, can be used as Gene Expression Signature Search (GESS) methods.
As non-set-based methods, they require quantitative gene expression values
for both the query and the database entries, such as normalized intensities
or read counts from microarrays or RNA-Seq experiments, respectively.
In its iterative form, Fisher's exact test (Upton, 1992) can be used as Gene
Expression Signature (GES) Search to scan GES databases for entries that
are similar to a query GES.
The gCMAP search method 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.
LINCS search method 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_cmap(
qSig,
chunk_size = 5000,
ref_trts = NULL,
workers = 1,
cmp_annot_tb = NULL,
by = "pert",
cmp_name_col = "pert",
addAnnotations = TRUE
)
gess_cor(
qSig,
method = "spearman",
chunk_size = 5000,
ref_trts = NULL,
workers = 1,
cmp_annot_tb = NULL,
by = "pert",
cmp_name_col = "pert",
addAnnotations = TRUE
)
gess_fisher(
qSig,
higher = NULL,
lower = NULL,
padj = NULL,
chunk_size = 5000,
ref_trts = NULL,
workers = 1,
cmp_annot_tb = NULL,
by = "pert",
cmp_name_col = "pert",
addAnnotations = TRUE
)
gess_gcmap(
qSig,
higher = NULL,
lower = NULL,
padj = NULL,
chunk_size = 5000,
ref_trts = NULL,
workers = 1,
cmp_annot_tb = NULL,
by = "pert",
cmp_name_col = "pert",
addAnnotations = TRUE
)
gess_lincs(
qSig,
tau = FALSE,
sortby = "NCS",
chunk_size = 5000,
ref_trts = NULL,
workers = 1,
cmp_annot_tb = NULL,
by = "pert",
cmp_name_col = "pert",
GeneType = "reference",
addAnnotations = TRUE
)
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.
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.
cmp_annot_tb
data.frame or tibble of compound annotation table.
This table contains annotation information for compounds stored under
pert column of gess_tb. Set to NULL if not available.
This table should not contain columns with names of "t_gn_sym", "MOAss" or
"PCIDss", these three columns will be added internally and thus conserved by
the function. If they are contained in cmp_annot_tb, they will be
overwritten. If users want to maintain these three columns in the provided
annotation table, give them different names.
by
character(1), column name in cmp_annot_tb that can be merged
with pert column in gess_tb. If refdb is set
as 'lincs2', it will be merged with pert_id column in the GESS result
table. If cmp_annot_tb is NULL, by is ignored.
cmp_name_col
character(1), column name in gess_tb or
cmp_annot_tb that store compound names. If there is no compound name
column, set to NULL. If cmp_name_col is available,
three additional columns (t_gn_sym, MOAss, PCIDss) are automatically added
by using get_targets, CLUE touchstone compound MOA annotation,
and 2017 lincs_pert_info annotation table, respectively as
annotation sources. t_gn_sym: target gene symbol, MOAss: MOA annotated from
signatureSearch, PCIDss: PubChem CID annotated from signatureSearch.
addAnnotations
Logical value. If TRUE adds drug annotations to results.
method
One of 'spearman' (default), 'kendall', or 'pearson',
indicating which correlation coefficient to use.
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'.
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'
GeneType
A character value of either "reference" or a combination of
"best inferred", "landmark" or "inferred" indicating which reference gene set
query genes should be filtered against. While "reference" filters query genes
against the reference database, "best inferred", "landmark" or
"inferred" filter genes against LINCS gene spaces.
Details
Lamb et al. (2006) introduced the gene expression-based search method known
as Connectivity Map (CMap) where a GES database is searched with a query GES
for similar entries. Specifically, this GESS 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 rank
transformed expression profiles (e.g.ranks of LFC or z-scores). The actual
GESS algorithm is based on a vectorized rank difference calculation. The
resulting Connectivity Score expresses to what degree the query up/down gene
sets are enriched on the top and bottom of the database entries,
respectively. The search results are a list of perturbagens such as drugs
that induce similar or opposing GESs as the query. Similar GESs suggest
similar physiological effects of the corresponding perturbagens.
Although several variants of the CMAP algorithm are available in other
software packages including Bioconductor, the implementation provided by
signatureSearch follows the original description of the authors as
closely as possible.
For correlation searches to work, it is important that both the query and
reference database contain the same type of gene identifiers. The expected
data structure of the query is a matrix with a single numeric column and the
gene labels (e.g. Entrez Gene IDs) in the row name slot. For convenience, the
correlation-based searches can either be performed with the full set of genes
represented in the database or a subset of them. The latter can be useful to
focus the computation for the correlation values on certain genes of interest
such as a DEG set or the genes in a pathway of interest. For comparing the
performance of different GESS methods, it can also be advantageous to subset
the genes used for a correlation-based search to same set used in a set-based
search, such as the up/down DEGs used in a LINCS GESS. This way the search
results of correlation- and set-based methods can be more comparable because
both are provided with equivalent information content.
When using the Fisher's exact test (Upton, 1992) as GES Search (GESS) method,
both the query and the database are composed of gene label sets, such as
DEG sets.
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 original 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.
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 in GESS result tables.
pert: character, perturbagen (e.g. drugs) in the reference
database. The treatment/column names of the reference database are
organized as pert__cell__trt_cp format. The pert column in
GESS result table contains what stored under the pert slot of the
column names.
cell: character, acronym of cell type
type: character, perturbation type. In the CMAP/LINCS
databases provided by signatureSearchData, the perturbation types
are currently treatments with drug-like compounds (trt_cp). If required,
users can build custom signature database with other types of
perturbagens (e.g., gene knockdown or over-expression events) with the
provided build_custom_db function.
trend: character, up or down when the reference signature is
positively or negatively connected with the query signature,
respectively.
N_upset: integer, number of genes in the query up set
N_downset: integer, number of genes in the query down set
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.
Note, there are NAs in the Tau score column, the reason is that the number
of signatures in Qref that match the cell line of signature r
(the TauRefSize column in the GESS result) is less than 500,
Tau will be set as NA since it is redeemed as there are not large enough
samples for computing meaningful Tau scores.
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.
cor_score: Correlation coefficient based on the method defined in
the gess_cor function.
raw_score: bi-directional enrichment score (Kolmogorov-Smirnov
statistic) of up and down set in the query signature
scaled_score: raw_score scaled to values from 1 to -1 by
dividing the positive and negative scores with the maximum positive score
and the absolute value of the minimum negative score, respectively.
effect: Scaled bi-directional enrichment score corresponding to
the scaled_score under the CMAP result.
nSet: number of genes in the GES in the reference
database (gene sets) after setting the higher and lower cutoff.
nFound: number of genes in the GESs of the reference
database (gene sets) that are also present in the query GES.
signed: whether gene sets in the reference database have signs,
representing up and down regulated genes when computing scores.
pval: p-value of the Fisher's exact test.
padj: p-value adjusted for multiple hypothesis testing using
R's p.adjust function with the Benjamini & Hochberg (BH) method.
effect: z-score based on the standard normal distribution.
LOR: Log Odds Ratio.
t_gn_sym: character, symbol of the gene encoding the
corresponding drug target protein
MOAss: character, compound MOA annotation from signatureSearch
package
PCIDss: character, compound PubChem CID annotation from
signatureSearch package
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
For detailed description of the CMap method, please refer to:
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
Graham J. G. Upton. 1992. Fisher's Exact Test. J. R. Stat. Soc. Ser. A
Stat. Soc. 155 (3). [Wiley, Royal Statistical Society]: 395-402.
URL: http://www.jstor.org/stable/2982890
See Also
qSig, gessResult,
addGESSannot
Examples
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 database
# query_mat <- as.matrix(assay(sample_db[,"vorinostat__SKB__trt_cp"]))
############## CMAP method ##############
# qsig_cmap <- qSig(query=list(
# upset=c("230", "5357", "2015", "2542", "1759"),
# downset=c("22864", "9338", "54793", "10384", "27000")),
# gess_method="CMAP", refdb=db_path)
# cmap <- gess_cmap(qSig=qsig_cmap, chunk_size=5000)
# result(cmap)
######## Correlation-based GESS method #########
# qsig_sp <- qSig(query=query_mat, gess_method="Cor", refdb=db_path)
# sp <- gess_cor(qSig=qsig_sp, method="spearman")
# result(sp)
############## Fisher's Exact Test ##########
# qsig_fisher <- qSig(query=query_mat, gess_method="Fisher", refdb=db_path)
# fisher <- gess_fisher(qSig=qsig_fisher, higher=1, lower=-1)
# result(fisher)
############## gCMAP method ##############
# qsig_gcmap <- qSig(query=query_mat, gess_method="gCMAP", refdb=db_path)
# gcmap <- gess_gcmap(qsig_gcmap, higher=1, lower=-1)
# result(gcmap)
############### LINCS method #############
# 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)
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| gess_cmap | R Documentation |
GESS Methods
Description
The CMAP search method implements the original Gene Expression Signature Search (GESS) from Lamb et al (2006) known as Connectivity Map (CMap). 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 rank transformed expression profiles (e.g. ranks of LFC or z-scores).
Correlation-based similarity metrics, such as Spearman or Pearson coefficients, can be used as Gene Expression Signature Search (GESS) methods. As non-set-based methods, they require quantitative gene expression values for both the query and the database entries, such as normalized intensities or read counts from microarrays or RNA-Seq experiments, respectively.
In its iterative form, Fisher's exact test (Upton, 1992) can be used as Gene Expression Signature (GES) Search to scan GES databases for entries that are similar to a query GES.
The gCMAP search method 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.
LINCS search method 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_cmap(
qSig,
chunk_size = 5000,
ref_trts = NULL,
workers = 1,
cmp_annot_tb = NULL,
by = "pert",
cmp_name_col = "pert",
addAnnotations = TRUE
)
gess_cor(
qSig,
method = "spearman",
chunk_size = 5000,
ref_trts = NULL,
workers = 1,
cmp_annot_tb = NULL,
by = "pert",
cmp_name_col = "pert",
addAnnotations = TRUE
)
gess_fisher(
qSig,
higher = NULL,
lower = NULL,
padj = NULL,
chunk_size = 5000,
ref_trts = NULL,
workers = 1,
cmp_annot_tb = NULL,
by = "pert",
cmp_name_col = "pert",
addAnnotations = TRUE
)
gess_gcmap(
qSig,
higher = NULL,
lower = NULL,
padj = NULL,
chunk_size = 5000,
ref_trts = NULL,
workers = 1,
cmp_annot_tb = NULL,
by = "pert",
cmp_name_col = "pert",
addAnnotations = TRUE
)
gess_lincs(
qSig,
tau = FALSE,
sortby = "NCS",
chunk_size = 5000,
ref_trts = NULL,
workers = 1,
cmp_annot_tb = NULL,
by = "pert",
cmp_name_col = "pert",
GeneType = "reference",
addAnnotations = TRUE
)
Arguments
qSig |
|
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. |
cmp_annot_tb |
data.frame or tibble of compound annotation table.
This table contains annotation information for compounds stored under
|
by |
character(1), column name in |
cmp_name_col |
character(1), column name in |
addAnnotations |
Logical value. If |
method |
One of 'spearman' (default), 'kendall', or 'pearson', indicating which correlation coefficient to use. |
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.
|
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.
|
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'. |
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' |
GeneType |
A character value of either "reference" or a combination of "best inferred", "landmark" or "inferred" indicating which reference gene set query genes should be filtered against. While "reference" filters query genes against the reference database, "best inferred", "landmark" or "inferred" filter genes against LINCS gene spaces. |
Details
Lamb et al. (2006) introduced the gene expression-based search method known
as Connectivity Map (CMap) where a GES database is searched with a query GES
for similar entries. Specifically, this GESS 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 rank
transformed expression profiles (e.g.ranks of LFC or z-scores). The actual
GESS algorithm is based on a vectorized rank difference calculation. The
resulting Connectivity Score expresses to what degree the query up/down gene
sets are enriched on the top and bottom of the database entries,
respectively. The search results are a list of perturbagens such as drugs
that induce similar or opposing GESs as the query. Similar GESs suggest
similar physiological effects of the corresponding perturbagens.
Although several variants of the CMAP algorithm are available in other
software packages including Bioconductor, the implementation provided by
signatureSearch follows the original description of the authors as
closely as possible.
For correlation searches to work, it is important that both the query and reference database contain the same type of gene identifiers. The expected data structure of the query is a matrix with a single numeric column and the gene labels (e.g. Entrez Gene IDs) in the row name slot. For convenience, the correlation-based searches can either be performed with the full set of genes represented in the database or a subset of them. The latter can be useful to focus the computation for the correlation values on certain genes of interest such as a DEG set or the genes in a pathway of interest. For comparing the performance of different GESS methods, it can also be advantageous to subset the genes used for a correlation-based search to same set used in a set-based search, such as the up/down DEGs used in a LINCS GESS. This way the search results of correlation- and set-based methods can be more comparable because both are provided with equivalent information content.
When using the Fisher's exact test (Upton, 1992) as GES Search (GESS) method, both the query and the database are composed of gene label sets, such as DEG sets.
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 original 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.
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 in GESS result tables.
pert: character, perturbagen (e.g. drugs) in the reference database. The treatment/column names of the reference database are organized as
pert__cell__trt_cpformat. Thepertcolumn in GESS result table contains what stored under thepertslot of the column names.cell: character, acronym of cell type
type: character, perturbation type. In the CMAP/LINCS databases provided by
signatureSearchData, the perturbation types are currently treatments with drug-like compounds (trt_cp). If required, users can build custom signature database with other types of perturbagens (e.g., gene knockdown or over-expression events) with the providedbuild_custom_dbfunction.trend: character, up or down when the reference signature is positively or negatively connected with the query signature, respectively.
N_upset: integer, number of genes in the query up set
N_downset: integer, number of genes in the query down set
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.
Note, there are NAs in the Tau score column, the reason is that the number of signatures in Qref that match the cell line of signature r (the
TauRefSizecolumn in the GESS result) is less than 500, Tau will be set as NA since it is redeemed as there are not large enough samples for computing meaningful Tau scores.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.
cor_score: Correlation coefficient based on the method defined in the
gess_corfunction.raw_score: bi-directional enrichment score (Kolmogorov-Smirnov statistic) of up and down set in the query signature
scaled_score: raw_score scaled to values from 1 to -1 by dividing the positive and negative scores with the maximum positive score and the absolute value of the minimum negative score, respectively.
effect: Scaled bi-directional enrichment score corresponding to the scaled_score under the CMAP result.
nSet: number of genes in the GES in the reference database (gene sets) after setting the higher and lower cutoff.
nFound: number of genes in the GESs of the reference database (gene sets) that are also present in the query GES.
signed: whether gene sets in the reference database have signs, representing up and down regulated genes when computing scores.
pval: p-value of the Fisher's exact test.
padj: p-value adjusted for multiple hypothesis testing using R's p.adjust function with the Benjamini & Hochberg (BH) method.
effect: z-score based on the standard normal distribution.
LOR: Log Odds Ratio.
t_gn_sym: character, symbol of the gene encoding the corresponding drug target protein
MOAss: character, compound MOA annotation from
signatureSearchpackagePCIDss: character, compound PubChem CID annotation from
signatureSearchpackage
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
For detailed description of the CMap method, please refer to: 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
Graham J. G. Upton. 1992. Fisher's Exact Test. J. R. Stat. Soc. Ser. A Stat. Soc. 155 (3). [Wiley, Royal Statistical Society]: 395-402. URL: http://www.jstor.org/stable/2982890
See Also
qSig, gessResult,
addGESSannot
Examples
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 database
# query_mat <- as.matrix(assay(sample_db[,"vorinostat__SKB__trt_cp"]))
############## CMAP method ##############
# qsig_cmap <- qSig(query=list(
# upset=c("230", "5357", "2015", "2542", "1759"),
# downset=c("22864", "9338", "54793", "10384", "27000")),
# gess_method="CMAP", refdb=db_path)
# cmap <- gess_cmap(qSig=qsig_cmap, chunk_size=5000)
# result(cmap)
######## Correlation-based GESS method #########
# qsig_sp <- qSig(query=query_mat, gess_method="Cor", refdb=db_path)
# sp <- gess_cor(qSig=qsig_sp, method="spearman")
# result(sp)
############## Fisher's Exact Test ##########
# qsig_fisher <- qSig(query=query_mat, gess_method="Fisher", refdb=db_path)
# fisher <- gess_fisher(qSig=qsig_fisher, higher=1, lower=-1)
# result(fisher)
############## gCMAP method ##############
# qsig_gcmap <- qSig(query=query_mat, gess_method="gCMAP", refdb=db_path)
# gcmap <- gess_gcmap(qsig_gcmap, higher=1, lower=-1)
# result(gcmap)
############### LINCS method #############
# 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)
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