Package 'singscore'

Permutation test for the derived scores of each sample

Description

This function generates a number of random gene sets that have the same number of genes as the scored gene set. It scores each random gene set and returns a matrix of scores for all samples. The empirical scores are used to calculate the empirical p-values and plot the null distribution. The implementation uses BiocParallel::bplapply() for easy access to parallel backends. Note that one should pass the same values to the upSet, downSet, centerScore and bidirectional arguments as what they provide for the simpleScore() function to generate a proper null distribution.

Usage

generateNull(
  upSet,
  downSet = NULL,
  rankData,
  subSamples = NULL,
  centerScore = TRUE,
  knownDirection = TRUE,
  B = 1000,
  ncores = 1,
  seed = sample.int(1e+06, 1),
  useBPPARAM = NULL
)

## S4 method for signature 'vector,ANY'
generateNull(
  upSet,
  downSet = NULL,
  rankData,
  subSamples = NULL,
  centerScore = TRUE,
  knownDirection = TRUE,
  B = 1000,
  ncores = 1,
  seed = sample.int(1e+06, 1),
  useBPPARAM = NULL
)

## S4 method for signature 'GeneSet,ANY'
generateNull(
  upSet,
  downSet = NULL,
  rankData,
  subSamples = NULL,
  centerScore = TRUE,
  knownDirection = TRUE,
  B = 1000,
  ncores = 1,
  seed = sample.int(1e+06, 1),
  useBPPARAM = NULL
)

## S4 method for signature 'vector,vector'
generateNull(
  upSet,
  downSet = NULL,
  rankData,
  subSamples = NULL,
  centerScore = TRUE,
  knownDirection = TRUE,
  B = 1000,
  ncores = 1,
  seed = sample.int(1e+06, 1),
  useBPPARAM = NULL
)

## S4 method for signature 'GeneSet,GeneSet'
generateNull(
  upSet,
  downSet = NULL,
  rankData,
  subSamples = NULL,
  centerScore = TRUE,
  knownDirection = TRUE,
  B = 1000,
  ncores = 1,
  seed = sample.int(1e+06, 1),
  useBPPARAM = NULL
)

Arguments

upSet	A GeneSet object or character vector of gene IDs of up-regulated gene set or a gene set where the nature of genes is not known
downSet	A GeneSet object or character vector of gene IDs of down-regulated gene set or NULL where only a single gene set is provided
rankData	A matrix object, ranked gene expression matrix data generated using the rankGenes() function (make sure this matrix is not modified, see details)
subSamples	A vector of sample labels/indices that will be used to subset the rankData matrix. All samples will be scored if not provided
centerScore	A Boolean, specifying whether scores should be centered around 0, default as TRUE. Note: scores never centered if knownDirection = FALSE
knownDirection	A boolean, determining whether the gene set should be considered to be directional or not. A gene set is directional if the type of genes in it are known i.e. up- or down-regulated. This should be set to TRUE if the gene set is composed of both up- AND down-regulated genes. Defaults to TRUE. This parameter becomes irrelevant when both upSet(Colc) and downSet(Colc) are provided.
B	integer, the number of permutation repeats or the number of random gene sets to be generated, default as 1000
ncores	integer, the number of CPU cores the function can use
seed	integer, set the seed for randomisation
useBPPARAM	the backend the function uses, if NULL is provided, the function uses the default parallel backend which is the first on the list returned by BiocParallel::registered() i.e BiocParallel::registered()[[1]] for your machine. It can be changed explicitly by passing a BPPARAM

Value

A matrix of empirical scores for all samples

Author(s)

Ruqian Lyu

See Also

Post about BiocParallel browseVignettes("BiocParallel")

Examples

ranked <- rankGenes(toy_expr_se)
scoredf <- simpleScore(ranked, upSet = toy_gs_up, downSet = toy_gs_dn)

# find out what backends can be registered on your machine
BiocParallel::registered()
# the first one is the default backend
# ncores = ncores <- parallel::detectCores() - 2
permuteResult = generateNull(upSet = toy_gs_up, downSet = toy_gs_dn, ranked,
centerScore = TRUE, B =10, seed = 1, ncores = 1 )

---

Estimate the empirical p-values

Description

With null distributions estimated using the generateNull() function, p-values are estimated using a one-tailed test. A minimum p-value of 1/B can be achieved with B permutations.

Usage

getPvals(permuteResult, scoredf, subSamples = NULL)

Arguments

permuteResult	A matrix, null distributions for each sample generated using the generateNull() function
scoredf	A dataframe, the scored results of samples under test generated using the simpleScore() function
subSamples	A vector of sample labels/indices that will be used to subset the score matrix. All samples will be scored if not provided

Value

Estimated p-values for enrichment of the signature in each sample. A p-value of 1/B indicates that the estimated p-value is less than or equal to 1/B.

Examples

ranked <- rankGenes(toy_expr_se)
scoredf <- simpleScore(ranked, upSet = toy_gs_up, downSet = toy_gs_dn)
# find out what backends can be registered on your machine
BiocParallel::registered()
# the first one is the default backend, and it can be changed explicitly.
# See vignette for more details
permuteResult = generateNull(upSet = toy_gs_up, downSet = toy_gs_dn, ranked,
B =10, seed = 1, useBPPARAM = NULL)

# call the permutation function to generate the empirical scores
# for B times.
pvals <- getPvals(permuteResult,scoredf)

---

Get a list of stably expressed genes

Description

Get a list of genes that are stably expressed in cancer and normal solid tissue.

Usage

getStableGenes(
  n_stable,
  type = c("carcinoma", "blood", "protein"),
  id = c("geneid", "ensembl")
)

Arguments

n_stable	numeric, number of stable genes to retrieve
type	character, type of stable genes requested, stable genes in "carcinoma" or stable genes in "blood"
id	character, gene identifier required. This can be either "geneid" for symbols or "ensembl" ensembl id)

Value

a character vector with gene IDs sorted by their expected expression levels in the requested tissue

Examples

getStableGenes(5)
getStableGenes(5, id = 'ensembl')
getStableGenes(5, type = 'blood')

---

single-sample gene-set scoring method for multiple signatures

Description

This function computes 'singscores' using a ranked gene expression matrix obtained from the rankGenes() function and a GeneSetCollection object or a list of GeneSet objects. It returns a list of two matrices containing the scores and dispersions. This function should be used when scoring needs to be performed for multiple signatures. It is faster than applying simpleScore() across the different signatures independently.

Usage

multiScore(
  rankData,
  upSetColc,
  downSetColc,
  subSamples = NULL,
  centerScore = TRUE,
  dispersionFun = mad,
  knownDirection = TRUE
)

## S4 method for signature 'matrix,GeneSetCollection,missing'
multiScore(
  rankData,
  upSetColc,
  downSetColc,
  subSamples = NULL,
  centerScore = TRUE,
  dispersionFun = mad,
  knownDirection = TRUE
)

## S4 method for signature 'matrix,GeneSetCollection,GeneSetCollection'
multiScore(
  rankData,
  upSetColc,
  downSetColc,
  subSamples = NULL,
  centerScore = TRUE,
  dispersionFun = mad,
  knownDirection = TRUE
)

## S4 method for signature 'matrix,list,missing'
multiScore(
  rankData,
  upSetColc,
  downSetColc,
  subSamples = NULL,
  centerScore = TRUE,
  dispersionFun = mad,
  knownDirection = TRUE
)

## S4 method for signature 'matrix,list,list'
multiScore(
  rankData,
  upSetColc,
  downSetColc,
  subSamples = NULL,
  centerScore = TRUE,
  dispersionFun = mad,
  knownDirection = TRUE
)

Arguments

rankData	A matrix object, ranked gene expression matrix data generated using the rankGenes() function (make sure this matrix is not modified, see details)
upSetColc	A GeneSetCollection object, a list of GeneSet objects, or a list of character vectors of up-regulated (or mixed, see simpleScore) gene sets.
downSetColc	A GeneSetCollection object, a list of GeneSet objects, or a list of character vectors of down-regulated gene sets. NULL otherwise. Names of gene sets within this collection/list should be the same as those of the upSetColc
subSamples	A vector of sample labels/indices that will be used to subset the rankData matrix. All samples will be scored if not provided
centerScore	A Boolean, specifying whether scores should be centered around 0, default as TRUE. Note: scores never centered if knownDirection = FALSE
dispersionFun	A function, dispersion function with default being mad
knownDirection	A boolean, determining whether the gene set should be considered to be directional or not. A gene set is directional if the type of genes in it are known i.e. up- or down-regulated. This should be set to TRUE if the gene set is composed of both up- AND down-regulated genes. Defaults to TRUE. This parameter becomes irrelevant when both upSet(Colc) and downSet(Colc) are provided.

Value

A list of two matrices containing the scores and dispersions

See Also

rank "GeneSet"

Examples

ranked <- rankGenes(toy_expr_se)
GSEABase::setName(toy_gs_up)  = "toy_gs_up"
GSEABase::setName(toy_gs_dn)  = "toy_gs_dn"
gslist <- list(toy_gs_up, toy_gs_dn)

gscolc <- GSEABase::GeneSetCollection(gslist)
scoredf <- multiScore(ranked, upSetColc = gscolc)

---

Plot the score v.s. despersion for all samples

Description

This function takes the output from the simpleScore() function and generates scatter plots of score vs. dispersion for the total score, the up score and the down score of samples. If you wish to use the plotting function but with some customized inputs (instead of outputs from simpleScore function), you need to make sure the formats are the same. To be specific, you need to have columns names "TotalScore" "TotalDispersion" "UpScore" "UpDispersion" "DownScore" "DownDispersion" and rows names as samples.

Usage

plotDispersion(
  scoredf,
  annot = NULL,
  annot_name = "",
  sampleLabels = NULL,
  alpha = 1,
  size = 1,
  textSize = 1.2,
  isInteractive = FALSE
)

Arguments

scoredf	data.frame, generated using the simpleScore() function
annot	any numeric, character or factor annotation provided by the user that needs to be plot. Alternatively, this can be a character specifying the column of scoredf holding the annotation. Annotations must be ordered in the same way as the scores
annot_name	character, legend title for the annotation
sampleLabels	vector of character, sample names to display, ordered in the same way as samples are ordered in the 'scoredf' data.frame and with labels for all samples. Samples whose labels should not be displayed should be left as empty strings or NAs. Default as NULL which means the projected points are not labelled.
alpha	numeric, set the transparency of points
size	numeric, set the size of each point
textSize	numeric, relative text sizes for title, labels, and axis values
isInteractive	Boolean, determine whether the plot is interactive

Value

A ggplot object

Examples

ranked <- rankGenes(toy_expr_se)
scoredf <- simpleScore(ranked, upSet = toy_gs_up, downSet = toy_gs_dn)
plotDispersion(scoredf)
plotDispersion(scoredf, isInteractive = TRUE)

---

Plot the empirically estimated null distribution and associated p-values

Description

This function takes the results from function generateNull() and plots the density curves of permuted scores for the provided samples via sampleNames parameter. It can plot null distribution(s) for a single sample or multiple samples.

Usage

plotNull(
  permuteResult,
  scoredf,
  pvals,
  sampleNames = NULL,
  cutoff = 0.01,
  textSize = 2,
  labelSize = 5
)

Arguments

permuteResult	A matrix, null distributions for each sample generated using the generateNull() function
scoredf	A dataframe, singscores generated using the simpleScore() function
pvals	A vector, estimated p-values using the getPvals() function permuteResult,scoredf and pvals are the results for the same samples.
sampleNames	A character vector, sample IDs for which null distributions will be plotted
cutoff	numeric, the cutoff value for determining significance
textSize	numeric, size of axes labels, axes values and title
labelSize	numeric, size of label texts

Value

a ggplot object

Author(s)

Ruqian Lyu

Examples

ranked <- rankGenes(toy_expr_se)
scoredf <- simpleScore(ranked, upSet = toy_gs_up, downSet = toy_gs_dn)
# find out what backends can be registered on your machine
BiocParallel::registered()
# the first one is the default backend, and it can be changed explicitly.
permuteResult = generateNull(upSet = toy_gs_up, downSet = toy_gs_dn, ranked,
B =10, seed = 1,useBPPARAM = NULL)
# call the permutation function to generate the empirical scores
#for B times.
pvals <- getPvals(permuteResult,scoredf)
# plot for all samples
plotNull(permuteResult,scoredf,pvals,sampleNames = names(pvals))
#plot for the first sample
plotNull(permuteResult,scoredf,pvals,sampleNames = names(pvals)[1])

---

Plot the densities of ranks for one sample

Description

This function takes a single-column data frame, which is a single-column subset of the ranked matrix data generated using rankGenes() function, and the gene sets of interest as inputs. It plots the density of ranks for genes in the gene set and overlays a barcode plot of these ranks. Ranks are normalized by dividing them by the maximum rank. Densities are estimated using KDE.

Usage

plotRankDensity(
  rankData,
  upSet,
  downSet = NULL,
  isInteractive = FALSE,
  textSize = 1.5
)

## S4 method for signature 'ANY,vector,missing'
plotRankDensity(
  rankData,
  upSet,
  downSet = NULL,
  isInteractive = FALSE,
  textSize = 1.5
)

## S4 method for signature 'ANY,GeneSet,missing'
plotRankDensity(
  rankData,
  upSet,
  downSet = NULL,
  isInteractive = FALSE,
  textSize = 1.5
)

## S4 method for signature 'ANY,vector,vector'
plotRankDensity(
  rankData,
  upSet,
  downSet = NULL,
  isInteractive = FALSE,
  textSize = 1.5
)

## S4 method for signature 'ANY,GeneSet,GeneSet'
plotRankDensity(
  rankData,
  upSet,
  downSet = NULL,
  isInteractive = FALSE,
  textSize = 1.5
)

Arguments

rankData	one column of the ranked gene expression matrix obtained from the rankGenes() function, use drop = FALSE when subsetting the ranked gene expression matrix, see examples.
upSet	GeneSet object or a vector of gene Ids, up-regulated gene set
downSet	GeneSet object or a vector of gene Ids, down-regulated gene set
isInteractive	Boolean, determine whether the returned plot is interactive
textSize	numberic, set the size of text on the plot

Value

A ggplot object (or a plotly object) with a rank density plot overlayed with a barcode plot

Examples

ranked <- rankGenes(toy_expr_se)
plotRankDensity(ranked[,2,drop = FALSE], upSet = toy_gs_up)

---

Plot landscape of two gene signatures scores

Description

This function takes two data frames which are outputs from the simpleScore() function and plots the relationship between the two gene set scores for samples in the gene expression matrix.Scoredf1 and Scoredf2 are two scoring results of the same set of samples against two different gene signatures. If you wish to use the plotting function but with some customized inputs (instead of outputs from the simpleScore function), you need to make sure the formats are the same To be specific, you need to have column names "TotalScore" "TotalDispersion" "UpScore" "UpDispersion" "DownScore" "DownDispersion" and rows names as samples.

Usage

plotScoreLandscape(
  scoredf1,
  scoredf2,
  scorenames = c(),
  textSize = 1.2,
  isInteractive = FALSE,
  hexMin = 100
)

Arguments

scoredf1	data.frame, result of the simpleScore() function which scores the gene expression matrix against a gene set of interest
scoredf2	data.frame, result of the simpleScore() function which scores the gene expression matrix against another gene set of interest
scorenames	character vector of length 2, names for the two scored gene set/signatures stored in scoredf1 and scoredf2
textSize	numeric, set the text size for the plot, default as 1.5
isInteractive	boolean, whether the plot is interactive default as FALSE
hexMin	integer, the threshold which decides whether hex bin plot or scatter plot is displayed, default as 100

Value

A ggplot object, a scatter plot, demonstrating the relationship between scores from two signatures on the same set of samples.

Examples

ranked <- rankGenes(toy_expr_se)
scoredf <- simpleScore(ranked, upSet = toy_gs_up, downSet = toy_gs_dn)
scoredf2 <- simpleScore(ranked, upSet = toy_gs_up)
plotScoreLandscape(scoredf, scoredf2)

---

Project data on the landscape plot obtained from plotScoreLandscape()

Description

This function takes the output (ggplot object) of the function plotScoreLandscape() and a new dataset. It projects the new data points onto the landscape plot and returns a new ggplot object with projected data points.

Usage

projectScoreLandscape(
  plotObj = NULL,
  scoredf1,
  scoredf2,
  annot = NULL,
  annot_name = NULL,
  subSamples = NULL,
  sampleLabels = NULL,
  isInteractive = FALSE
)

Arguments

plotObj	a ggplot object, resulted from plotScoreLandscape()
scoredf1	data.frame, result of the simpleScore() function which scores the gene expression matrix against a gene set of interest
scoredf2	data.frame, result of the simpleScore() function which scores the gene expression matrix against another gene set of interest. Scores in scoredf1 and scoredf2 consist of the new data points that will be projected on the plotObj landscape plot.
annot	any numeric, character or factor annotation provided by the user that needs to be plot. Alternatively, this can be a character specifying the column of scoredf1 holding the annotation. Annotations must be ordered in the same way as the scores
annot_name	character, legend title for the annotation
subSamples	vector of character or indices for subsetting the scoredfs, default as NULL and all samples in scoredfs will be plotted. The subsetted samples are projected onto the landscape plot of plotObj.
sampleLabels	vector of character, sample names to display, ordered in the same way as samples are ordered in the 'scoredfs' data.frames and with labels for all samples. Samples whose labels should not be displayed should be left as empty strings or NAs. Default as NULL which means the projected points are not labelled.
isInteractive	boolean, whether the plot is interactive default as FALSE

Value

New data points on the already plotted ggplot object from plotScoreLanscape()

See Also

plotScoreLandscape() @examples ranked <- rankGenes(toy_expr_se) scoredf1 <- simpleScore(ranked, upSet = toy_gs_up, downSet = toy_gs_dn) scoredf2 <- simpleScore(ranked, upSet = toy_gs_up) psl <- plotScoreLandscape(scoredf1, scoredf2) projectScoreLandscape(psl,scoredf1, scoredf2)

---

Rank genes by the gene expression intensities

Description

The rankGenes function is a generic function that can deal with mutilple types of inputs. Given a matrix of gene expression that has samples in columns, genes in rows, and values being gene expression intensity,rankGenes ranks gene expression intensities in each sample.

It can also work with S4 objects that have gene expression matrix as a component (i.e ExpressionSet, DGEList,SummarizedExperiment). It calls the rank function in the base package which ranks the gene expression matrix by its absolute expression level. If the input is S4 object of DGEList, ExpressionSet, or SummarizedExperiment, it will extract the gene expression matrix from the object and rank the genes. The default 'tiesMethod' is set to 'min'.

Usage

rankGenes(expreMatrix, tiesMethod = "min", stableGenes = NULL)

## S4 method for signature 'matrix'
rankGenes(expreMatrix, tiesMethod = "min", stableGenes = NULL)

## S4 method for signature 'data.frame'
rankGenes(expreMatrix, tiesMethod = "min", stableGenes = NULL)

## S4 method for signature 'DGEList'
rankGenes(expreMatrix, tiesMethod = "min", stableGenes = NULL)

## S4 method for signature 'ExpressionSet'
rankGenes(expreMatrix, tiesMethod = "min", stableGenes = NULL)

## S4 method for signature 'SummarizedExperiment'
rankGenes(expreMatrix, tiesMethod = "min", stableGenes = NULL)

Arguments

expreMatrix	matrix, data.frame, ExpressionSet, DGEList or SummarizedExperiment storing gene expression measurements
tiesMethod	character, indicating what method to use when dealing with ties
stableGenes	character, containing a list of stable genes to be used to rank genes using expression of stable genes. This is required when using the stable genes dependent version of singscore (see details in simpleScore). Stable genes for solid cancers (carcinomas) and blood transcriptomes can be obtained using the getStableGenes function

Value

The ranked gene expression matrix that has samples in columns and genes in rows. Unit normalised ranks are returned if data is ranked using stable genes

See Also

getStableGenes, simpleScore, rank, "ExpressionSet", "SummarizedExperiment", "DGEList"

Examples

rankGenes(toy_expr_se) # toy_expr_se is a gene expression dataset

# ExpressionSet object
emat <- SummarizedExperiment::assay(toy_expr_se)
e <- Biobase::ExpressionSet(assayData = as.matrix(emat))
rankGenes(e)

#scoring using the stable version of singscore
rankGenes(e, stableGenes = c('2', '20', '25'))

## Not run: 
#for real cancer or blood datasets, use getStableGenes()
rankGenes(cancer_expr, stableGenes = getStableGenes(5))
rankGenes(blood_expr, stableGenes = getStableGenes(5, type = 'blood'))

## End(Not run)

---

Pre-computed scores of the CCLE dataset against an epithelial gene signature

Description

This data.frame stores pre-computed scores of the CCLE dataset Barretina et al calculated using the simpleScore() function against the epithelial gene signature from Tan, Tuan Zea et al. The data.frame has scores for 55 samples. Please refer to the vignettes for instructions on how to obtain the full datasets.

Usage

scoredf_ccle_epi

Format

An object of class data.frame with 55 rows and 2 columns.

References

Barretina, Jordi, Giordano Caponigro, Nicolas Stransky, Kavitha Venkatesan, Adam A Margolin, Sungjoon Kim, Christopher J Wilson, et al. 2012. “The Cancer Cell Line Encyclopedia Enables Predictive Modelling of Anticancer Drug Sensitivity.” Nature 483 (7391): 603–7.

Tan, Tuan Zea, Qing Hao Miow, Yoshio Miki, Tetsuo Noda, Seiichi Mori, Ruby Yun-Ju Huang, and Jean Paul Thiery. 2014–10AD. “Epithelial-Mesenchymal Transition Spectrum Quantification and Its Efficacy in Deciphering Survival and Drug Responses of Cancer Patients.” EMBO Molecular Medicine 6 (10). Oxford, UK: BlackWell Publishing Ltd: 1279–93. doi:10.15252/emmm.201404208.

See Also

scoredf_ccle_mes

---

Pre-computed scores of the CCLE dataset against a mesenchymal gene signature

Description

This data.frame stores pre-computed scores of the CCLE dataset Barretina et al calculated using the simpleScore() function against the mesenchymal gene signature from Tan, Tuan Zea et al. The data.frame has scores for 55 samples. Please refer to the vignettes for instructions on how to obtain the full datasets.

Usage

scoredf_ccle_mes

Format

An object of class data.frame with 55 rows and 2 columns.

References

Barretina, Jordi, Giordano Caponigro, Nicolas Stransky, Kavitha Venkatesan, Adam A Margolin, Sungjoon Kim, Christopher J Wilson, et al. 2012. “The Cancer Cell Line Encyclopedia Enables Predictive Modelling of Anticancer Drug Sensitivity.” Nature 483 (7391): 603–7.

Tan, Tuan Zea, Qing Hao Miow, Yoshio Miki, Tetsuo Noda, Seiichi Mori, Ruby Yun-Ju Huang, and Jean Paul Thiery. 2014–10AD. “Epithelial-Mesenchymal Transition Spectrum Quantification and Its Efficacy in Deciphering Survival and Drug Responses of Cancer Patients.” EMBO Molecular Medicine 6 (10). Oxford, UK: BlackWell Publishing Ltd: 1279–93 doi:10.15252/emmm.201404208.

See Also

scoredf_ccle_epi

---

Pre-computed scores of the TCGA breast cancer gene expression matrix against an epithelial signature

Description

This data.frame stores pre-computed scores of the TCGA dataset calculated using the simpleScore() function against the epithelial gene signature from Tan, Tuan Zea et al. Please refer to the vignettes for instructions on how to obtain the full datasets.

Usage

scoredf_tcga_epi

Format

An object of class data.frame with 1119 rows and 2 columns.

References

Tan, Tuan Zea, Qing Hao Miow, Yoshio Miki, Tetsuo Noda, Seiichi Mori, Ruby Yun-Ju Huang, and Jean Paul Thiery. 2014–10AD. “Epithelial-Mesenchymal Transition Spectrum Quantification and Its Efficacy in Deciphering Survival and Drug Responses of Cancer Patients.” EMBO Molecular Medicine 6 (10). Oxford, UK: BlackWell Publishing Ltd: 1279–93 doi:10.15252/emmm.201404208.

See Also

scoredf_tcga_mes

---

Pre-computed scores of the TCGA breast cancer gene expression matrix against a mesenchymal signature

Description

This data.frame stores pre-computed scores of the TCGA dataset calculated using the simpleScore() function against the mesenchymal gene signature from Tan, Tuan Zea et al. Please refer to the vignettes for instructions on how to obtain the full datasets.

Usage

scoredf_tcga_mes

Format

An object of class data.frame with 1119 rows and 2 columns.

References

Tan, Tuan Zea, Qing Hao Miow, Yoshio Miki, Tetsuo Noda, Seiichi Mori, Ruby Yun-Ju Huang, and Jean Paul Thiery. 2014–10AD. “Epithelial-Mesenchymal Transition Spectrum Quantification and Its Efficacy in Deciphering Survival and Drug Responses of Cancer Patients.” EMBO Molecular Medicine 6 (10). Oxford, UK: BlackWell Publishing Ltd: 1279–93 doi:10.15252/emmm.201404208.

See Also

scoredf_tcga_epi

---

single-sample gene-set scoring method

Description

This function computes 'singscores' using an unmodified ranked gene expression matrix obtained from the rankGenes() function and a gene set or a pair of up-regulated and down-regulated gene sets. It returns a data.frame of scores and dispersions for each sample. The gene sets can be in vector format or as GeneSet objects (from GSEABase packages). If samples need to be scored against a single gene set, the upSet argument should be used to pass the gene set while the downSet argument is set to NULL. This setting is ideal for gene sets representing gene ontologies where the nature of the genes is unknown (up- or down-regulated).

Usage

simpleScore(
  rankData,
  upSet,
  downSet = NULL,
  subSamples = NULL,
  centerScore = TRUE,
  dispersionFun = mad,
  knownDirection = TRUE
)

## S4 method for signature 'matrix,vector,missing'
simpleScore(
  rankData,
  upSet,
  downSet = NULL,
  subSamples = NULL,
  centerScore = TRUE,
  dispersionFun = mad,
  knownDirection = TRUE
)

## S4 method for signature 'matrix,GeneSet,missing'
simpleScore(
  rankData,
  upSet,
  downSet = NULL,
  subSamples = NULL,
  centerScore = TRUE,
  dispersionFun = mad,
  knownDirection = TRUE
)

## S4 method for signature 'matrix,vector,vector'
simpleScore(
  rankData,
  upSet,
  downSet = NULL,
  subSamples = NULL,
  centerScore = TRUE,
  dispersionFun = mad,
  knownDirection = TRUE
)

## S4 method for signature 'matrix,GeneSet,GeneSet'
simpleScore(
  rankData,
  upSet,
  downSet = NULL,
  subSamples = NULL,
  centerScore = TRUE,
  dispersionFun = mad,
  knownDirection = TRUE
)

Arguments

rankData	A matrix object, ranked gene expression matrix data generated using the rankGenes() function (make sure this matrix is not modified, see details)
upSet	A GeneSet object or character vector of gene IDs of up-regulated gene set or a gene set where the nature of genes is not known
downSet	A GeneSet object or character vector of gene IDs of down-regulated gene set or NULL where only a single gene set is provided
subSamples	A vector of sample labels/indices that will be used to subset the rankData matrix. All samples will be scored if not provided
centerScore	A Boolean, specifying whether scores should be centered around 0, default as TRUE. Note: scores never centered if knownDirection = FALSE
dispersionFun	A function, dispersion function with default being mad
knownDirection	A boolean, determining whether the gene set should be considered to be directional or not. A gene set is directional if the type of genes in it are known i.e. up- or down-regulated. This should be set to TRUE if the gene set is composed of both up- AND down-regulated genes. Defaults to TRUE. This parameter becomes irrelevant when both upSet(Colc) and downSet(Colc) are provided.

Details

Signature scores can be computed using transcriptome-wide measurements or using a smaller set of measuremnts. If ranks are computed using the default invocation of rankgenes, the former method is applied where the rank of each gene in the signature is computed relative to all other genes in the dataset. Accuracy of this approximation of the relative expression of a gene will be improved if all or most transctripts are measured in the experiment. This was the approach proposed in the original manucript of singscore (Foroutan M, Bhuva DD, et al 2018).

If instead a selected panel of genes is measured (such as from nanostring or RT-qPCR), a different rank approximation methods using a small set of stable genes can be used. This approach only requires measurements of genes in the signature and a small set of stable genes. This approach of scoring can be invoked by producing a rank matrix by passing in the stableGenes argument of rankGenes. Stable genes in solid cancers and in blood can be retrieved using getStableGenes. Upon providing a set of stable genes, rankGenes automatically ranks all genes relative to these stable genes. When simpleScore is provided with a rank matrix constructed using stable genes, it automatically computes scores using a new approach. Details of the set of stable genes, the new rank estimation approach and the new scoring approach will soon be published (manuscript in preparation).

Value

A data.frame consists of singscores and dispersions for all samples

References

Foroutan, M., Bhuva, D. D., Lyu, R., Horan, K., Cursons, J., & Davis, M. J. (2018). Single sample scoring of molecular phenotypes. BMC bioinformatics, 19(1), 1-10.

See Also

rankGenes, getStableGenes, rank, "GeneSet"

Examples

ranked <- rankGenes(toy_expr_se)
scoredf <- simpleScore(ranked, upSet = toy_gs_up, downSet = toy_gs_dn)
# toy_gs_up is a GeneSet object, alternatively a vector of gene ids may also
# be supplied.

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singscore: A package for deriving gene-set scores at a single sample level

Description

The package provides functions for calculating gene-set enrichment scores at a single-sample level using gene expression data. It includes functions to perform hypothesis testing and provides visualisations to enable diagnosis of scores and gene sets along with visualisations to enable exploration of results.

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An example gene expression dataset

Description

A microarray gene expression dataset that was originally obtained from the integrated TGFb-EMT data published by (Foroutan et al, 2017). (ComBat corrected values). tgfb_expr_10 is a subset of the integrated TGFb-EMT data consisting of 10 samples (4 TGFb treated and 6 controls) each with expression values for 11900 genes.

Usage

tgfb_expr_10_se

Format

A SummarizedExperiment object

Source

Foroutan et al,2017

References

Foroutan, Momeneh, Joseph Cursons, Soroor Hediyeh-Zadeh, Erik W Thompson, and Melissa J Davis. 2017. “A Transcriptional Program for Detecting Tgfbeta-Induced Emt in Cancer.” Molecular Cancer Research. American Association for Cancer Research. doi:10.1158/1541-7786.MCR-16-0313.

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Gene set of down-regulated genes for the TGFb-induced EMT gene signature

Description

A GeneSet object that contains the down-regulated genes of the TGFb-induced EMT gene signature that was derived by (Foroutan et al,2017), using two meta-analysis techniques. The gene signature contains an up-regulated gene set (up-set) and a down-regulated gene set (down-set). Please refer to the vignettes for the steps to acquire the exact data object.

Usage

tgfb_gs_dn

Format

A GeneSet object

Source

Foroutan et al,2017

References

Foroutan, Momeneh, Joseph Cursons, Soroor Hediyeh-Zadeh, Erik W Thompson, and Melissa J Davis. 2017. “A Transcriptional Program for Detecting Tgfbeta-Induced Emt in Cancer.” Molecular Cancer Research. American Association for Cancer Research. doi:10.1158/1541-7786.MCR-16-0313.

See Also

"GeneSet",tgfb_gs_up

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Gene set of up-regulated genes for the TGFb-induced EMT gene signature

Description

A GeneSet object that contains the up-regulated genes of the TGFb-induced EMT gene signature that was derived by (Foroutan et al.,2017), using two meta-analysis techniques. The gene signature contains an up-regulated gene set (up-set) and a down-regulated gene set (down-set). Please refer to the vignettes for the steps to acquire the exact data object.

Usage

tgfb_gs_up

Format

A GeneSet object

Source

Foroutan et al,2017

References

Foroutan, Momeneh, Joseph Cursons, Soroor Hediyeh-Zadeh, Erik W Thompson, and Melissa J Davis. 2017. “A Transcriptional Program for Detecting Tgfbeta-Induced Emt in Cancer.” Molecular Cancer Research. American Association for Cancer Research. doi:10.1158/1541-7786.MCR-16-0313.

See Also

"GeneSet",tgfb_gs_dn

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A toy gene expression dataset of two samples

Description

A toy dataset consisting of 2 samples with the expression values of 20 genes. The data was created by sampling 2 samples and 20 genes from the dataset by Foroutan et al, 2017.

Usage

toy_expr_se

Format

A SummarizedExperiment of 2 samples each with 20 genes

D_Ctrl_R1

a control sample

D_TGFb_R1

a TGFb-treated sample

Source

Foroutan et al.,2017

References

Foroutan, Momeneh, Joseph Cursons, Soroor Hediyeh-Zadeh, Erik W Thompson, and Melissa J Davis. 2017. “A Transcriptional Program for Detecting Tgfbeta-Induced Emt in Cancer.” Molecular Cancer Research. American Association for Cancer Research. doi:10.1158/1541-7786.MCR-16-0313.

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A gene set object of down-regulated genes for the toy dataset

Description

A GeneSet object with 5 genes randomly selected from the toy dataset. These genes are independent of those in toy_gs_up

Usage

toy_gs_dn

Format

A GSEABase::GeneSet object with 5 genes

See Also

"GeneSet",toy_expr_se,toy_gs_up

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A gene set object of up-regulated genes for the toy dataset

Description

A GeneSet object with 5 genes randomly selected from the toy dataset. These genes are independent of those in toy_gs_dn

Usage

toy_gs_up

Format

A GeneSet object with 5 genes

See Also

"GeneSet",toy_expr_se,toy_gs_dn

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