Package 'broadSeq' reference manual

Title:	broadSeq : for streamlined exploration of RNA-seq data
Description:	This package helps user to do easily RNA-seq data analysis with multiple methods (usually which needs many different input formats). Here the user will provid the expression data as a SummarizedExperiment object and will get results from different methods. It will help user to quickly evaluate different methods.
Authors:	Rishi Das Roy [aut, cre]
Maintainer:	Rishi Das Roy <[email protected]>
License:	MIT + file LICENSE
Version:	1.1.0
Built:	2024-11-27 06:16:45 UTC
Source:	https://github.com/bioc/broadSeq

broadSeq : for streamlined exploration of RNA-seq data

Description

This package helps user to do easily RNA-seq data analysis with multiple methods (usually which needs many different input formats). Here the user will provid the expression data as a SummarizedExperiment object and will get results from different methods. It will help user to quickly evaluate different methods.

Author(s)

Maintainer: Rishi Das Roy [email protected] (ORCID)

Provides GO gene set enrichment and over-representation analysis

Description

This wrapper function combines clusterProfiler::gseGO and clusterProfiler::enrichGO. The input type of thes two methods are different; order ranked geneList and a vector of entrez gene id. Here combinedEnrichment function internally generates these two data types from user defined DEG_table (differentially expresssed genes).

Usage

combinedEnrichment(
  DEG_table,
  geneCol = "ID",
  logCol = "logFoldChange",
  OrgDB = "org.Hs.eg.db",
  keyType,
  universe,
  ont = "BP",
  logfoldCut = 1,
  pvalueCutoff = 0.05,
  qvalueCutoff = 0.05
)
combinedEnrichment(
  DEG_table,
  geneCol = "ID",
  logCol = "logFoldChange",
  OrgDB = "org.Hs.eg.db",
  keyType,
  universe,
  ont = "BP",
  logfoldCut = 1,
  pvalueCutoff = 0.05,
  qvalueCutoff = 0.05
)

Arguments

`DEG_table`	A data.frame atleast with two columns.
`geneCol`	The column name of DEG_table which provides gene ids and should be compatible with keytype parameter.
`logCol`	The column name of DEG_table which provides logfold(numeric) values to create a order ranked geneList for gseGO funtion.
`OrgDB`	OrgDb; passed to clusterProfiler functions
`keyType`	keytype of input gene(geneCol). One of the keytypes(OrgDB); passed to clusterProfiler functions
`universe`	background genes; passed to clusterProfiler::enrichGO.
`ont`	one of "BP", "MF", and "CC" subontologies, or "ALL" for all three.; passed to clusterProfiler functions
`logfoldCut`	to filter genes based on parameter logCol
`pvalueCutoff`	; passed to clusterProfiler functions
`qvalueCutoff`	; passed to clusterProfiler functions

Value

a named list of three data.frames which are output of gseGO("gseResult") and enrichGO ("oraUP" and "oraDOWN").

Expression of multiple genes/features from a single assay as boxplot (or added dotplot)

Description

Expression of multiple genes/features from a single assay as boxplot (or added dotplot)

Boxplot of a single gene/feature from multiple assays

Usage

genes_plot(se, features, assayName = "counts", facet.by = "feature", x, ...)

assay_plot(se, feature, assayNames = c("counts"), x, ...)
genes_plot(se, features, assayName = "counts", facet.by = "feature", x, ...)

assay_plot(se, feature, assayNames = c("counts"), x, ...)

Arguments

`se`	Object of `SummarizedExperiment` class
`features`	a character vector of rownames or named list of character vectors where name is one of the colnames of rowdata.
`assayName`	One of the values from SummarizedExperiment::assayNames(se); default is "counts" assay
`facet.by`	must be one of the column names of rowData(se). default "feature" which is equivalent to rownames of rowData
`x`	a column name of colData which will be used in x-axis
`...`	other arguments to be passed to ggpubr::`ggboxplot`
`feature`	a character vector of rownames or named list of character vectors where name is one of the column of rowdata.
`assayNames`	names from SummarizedExperiment::assayNames(se); default value is "counts"

Value

ggplot object

return an object of class ggarrange, which is a ggplot or a list of ggplot.

Examples

se <- readRDS(system.file("extdata", "rat_vole_mouseSE_salmon.rds",
    package = "broadSeq"))
# The normalized values are added with the assay name "logCPM"
se <- broadSeq::normalizeEdgerCPM(se ,method = "none",cpm.log = TRUE )

broadSeq::genes_plot(se,
                     features = list(mouse_gene_id = c("ENSMUSG00000022510" ,
                                                        "ENSMUSG00000027985")),
                     facet.by = "symbol", # column of rowData
                     x = "stage",  fill="stage")

broadSeq::genes_plot(se,
                     features = list(symbol=c("Shh","Edar") ),
                     facet.by = "symbol", # column of rowData
                     x = "stage",  fill="stage")

broadSeq::assay_plot(se, feature = c("Shh"),
                    assays =  c("counts","logCPM"),
                    x = "stage", fill="stage", add="dotplot", palette = "npg")
se <- readRDS(system.file("extdata", "rat_vole_mouseSE_salmon.rds",
    package = "broadSeq"))
# The normalized values are added with the assay name "logCPM"
se <- broadSeq::normalizeEdgerCPM(se ,method = "none",cpm.log = TRUE )

broadSeq::genes_plot(se,
                     features = list(mouse_gene_id = c("ENSMUSG00000022510" ,
                                                        "ENSMUSG00000027985")),
                     facet.by = "symbol", # column of rowData
                     x = "stage",  fill="stage")

broadSeq::genes_plot(se,
                     features = list(symbol=c("Shh","Edar") ),
                     facet.by = "symbol", # column of rowData
                     x = "stage",  fill="stage")

broadSeq::assay_plot(se, feature = c("Shh"),
                    assays =  c("counts","logCPM"),
                    x = "stage", fill="stage", add="dotplot", palette = "npg")

Use of edgeR package to normalize count data

Description

Use of edgeR package to normalize count data

Usage

normalizeEdgerCPM(se, method = "TMM", cpm.log = TRUE, ...)
normalizeEdgerCPM(se, method = "TMM", cpm.log = TRUE, ...)

Arguments

`se`	Object of `SummarizedExperiment` class
`method`	value for edgeR::`normLibSizes` function. default "TMM"
`cpm.log`	value for edgeR::`cpm` function. default TRUE
`...`	passed to normLibSizes function

Value

Object of SummarizedExperiment class where a new assay is added to the input object.

Examples

se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

se <- broadSeq::normalizeEdgerCPM(se , method = "TMM", cpm.log = FALSE )
# The normalized values are added with the assay name "TMM"
SummarizedExperiment::assayNames(se)
se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

se <- broadSeq::normalizeEdgerCPM(se , method = "TMM", cpm.log = FALSE )
# The normalized values are added with the assay name "TMM"
SummarizedExperiment::assayNames(se)

Classical multidimensional scaling

Description

Classical multidimensional scaling is based on measuring the distance between the samples.

Usage

plot_MDS(se, scaledAssay = "vst", ntop = 500L, features = NULL, ...)
plot_MDS(se, scaledAssay = "vst", ntop = 500L, features = NULL, ...)

Arguments

`se`	Object of `SummarizedExperiment` class
`scaledAssay`	an scaled assay name from SummarizedExperiment::assayNames(se)
`ntop`	number of most-variable genes to select. Igored if "features" is specified.
`features`	character vector features/genes to be used to measure distance between the samples
`...`	other arguments like color or shape whose values should be similar to colData columns names passed to ggpubr::`ggscatter`

Value

ggplot object

Examples

se <- readRDS(system.file("extdata","rat_vole_mouseSE_salmon.rds", package = "broadSeq"))

se <- broadSeq::transformDESeq2(se,method = "vst"  )

broadSeq::plot_MDS(se, scaledAssay = "vst", ntop=500,
                    color = "species", shape = "stage",
                    ellipse=TRUE, legend = "bottom")
se <- readRDS(system.file("extdata","rat_vole_mouseSE_salmon.rds", package = "broadSeq"))

se <- broadSeq::transformDESeq2(se,method = "vst"  )

broadSeq::plot_MDS(se, scaledAssay = "vst", ntop=500,
                    color = "species", shape = "stage",
                    ellipse=TRUE, legend = "bottom")

Plot clustered heatmaps

Description

Plot clustered heatmaps from SummarizedExperiment with pheatmap and return object as ggplot

Usage

plotHeatmapCluster(
  se,
  scaledAssay = "vst",
  ntop = 500L,
  features = NULL,
  show_geneAs = NULL,
  annotation_col = NA,
  annotation_row = NA,
  ...
)
plotHeatmapCluster(
  se,
  scaledAssay = "vst",
  ntop = 500L,
  features = NULL,
  show_geneAs = NULL,
  annotation_col = NA,
  annotation_row = NA,
  ...
)

Arguments

`se`	Object of `SummarizedExperiment` class
`scaledAssay`	an scaled assay name from SummarizedExperiment::assayNames(se)
`ntop`	number of most-variable genes to select. Igored if "features" is specified.
`features`	character vector features/genes to be used to measure distance between the samples
`show_geneAs`	a character vector of colnames of rowData(se)
`annotation_col`	a character vector of colnames of colData(se)
`annotation_row`	a list of character vector of colnames of rowData(se)
`...`	other arguments like color or shape whose values should be similar to colData columns names passed to `pheatmap`

Value

ggplot object

Examples

se <- readRDS(system.file("extdata","rat_vole_mouseSE_salmon.rds", package = "broadSeq"))

se <- broadSeq::normalizeEdgerCPM(se ,method = "none",cpm.log = TRUE )

broadSeq::plotHeatmapCluster(
    se,
    scaledAssay = "logCPM",
    annotation_col = c("species", "stage"),
    annotation_row = c("Class","gene_biotype"),
    ntop = 30, show_geneAs = "symbol",
    cluster_cols = TRUE, cluster_rows = FALSE,
    show_rownames = TRUE, show_colnames = FALSE,
    main = "Top 30 variable gene vst"
)
se <- readRDS(system.file("extdata","rat_vole_mouseSE_salmon.rds", package = "broadSeq"))

se <- broadSeq::normalizeEdgerCPM(se ,method = "none",cpm.log = TRUE )

broadSeq::plotHeatmapCluster(
    se,
    scaledAssay = "logCPM",
    annotation_col = c("species", "stage"),
    annotation_row = c("Class","gene_biotype"),
    ntop = 30, show_geneAs = "symbol",
    cluster_cols = TRUE, cluster_rows = FALSE,
    show_rownames = TRUE, show_colnames = FALSE,
    main = "Top 30 variable gene vst"
)

Perform Principal Components Analysis

Description

This function returns the results of stats::prcomp in a tidy list format. This is more flexible for further custom PCA , biplot and exploring gene(factor) loading of the PCA.

Usage

prcompTidy(se, scaledAssay = "vst", ntop = 500L, features = NULL, ...)

plotAnyPC(computedPCA, x = 1, y = 2, ...)

biplotAnyPC(computedPCA, x = 1, y = 2, genes = NULL, genesLabel = NULL, ...)

getFeatureLoadRanking(computedPCA, pcs = seq_len(5), topN = 10, keep)
prcompTidy(se, scaledAssay = "vst", ntop = 500L, features = NULL, ...)

plotAnyPC(computedPCA, x = 1, y = 2, ...)

biplotAnyPC(computedPCA, x = 1, y = 2, genes = NULL, genesLabel = NULL, ...)

getFeatureLoadRanking(computedPCA, pcs = seq_len(5), topN = 10, keep)

Arguments

`se`	Object of `SummarizedExperiment` class
`scaledAssay`	an scaled assay name from SummarizedExperiment::assayNames(se)
`ntop`	number of most-variable genes to select. Igored if "features" is specified.
`features`	character vector features/genes to be used for PCA
`...`	other arguments like color or shape whose values should be similar to colData columns names passed to ggpubr::`ggscatter`
`computedPCA`	a list of data.frame returned by `prcompTidy`
`x`	PC number for x-axis default 1
`y`	PC number for y-axis default 2
`genes`	if genes is NULL then top max and min loaded genes of each PCs are plotted
`genesLabel`	one of rowData column names
`pcs`	The numbers of PCs
`topN`	Number of features per PC
`keep`	the column names of rowData to keep the corresponding information

Details

Reused code

Value

a list with four data.frame objects: pc_scores, eigen_values, loadings (eigen vectors) and the original data.

ggplot object

a data.frame

Examples

se <- readRDS(system.file("extdata","rat_vole_mouseSE_salmon.rds", package = "broadSeq"))

se <- broadSeq::normalizeEdgerCPM(se ,method = "none",cpm.log = TRUE )
computedPCA_logCPM <- broadSeq::prcompTidy(se, scaledAssay = "logCPM", ntop = 500)

plotAnyPC(computedPCA = computedPCA_logCPM, x = 1, y = 2, color = "species",
         shape = "stage",legend = "bottom")
plotAnyPC(computedPCA = computedPCA_logCPM, x = 2, y = 3, color = "species",
         shape = "stage",legend = "bottom")

computedPCA_logCPM$eigen_values %>%
 dplyr::filter(var_exp >= 0.5) %>% # Selecting PC explaining more than 1% variance
    ggbarplot(x="PC",y="var_exp", label = TRUE, label.pos = "out")

se <- readRDS(system.file("extdata","rat_vole_mouseSE_salmon.rds", package = "broadSeq"))

se <- broadSeq::normalizeEdgerCPM(se ,method = "none",cpm.log = TRUE )
computedPCA_logCPM <- broadSeq::prcompTidy(se, scaledAssay = "logCPM", ntop = 500)

plotAnyPC(computedPCA = computedPCA_logCPM, x = 1, y = 2, color = "species",
         shape = "stage",legend = "bottom")
plotAnyPC(computedPCA = computedPCA_logCPM, x = 2, y = 3, color = "species",
         shape = "stage",legend = "bottom")

computedPCA_logCPM$eigen_values %>%
 dplyr::filter(var_exp >= 0.5) %>% # Selecting PC explaining more than 1% variance
    ggbarplot(x="PC",y="var_exp", label = TRUE, label.pos = "out")

Applies round function only on numeric columns of a data.frame.

Description

Applies round function only on numeric columns of a data.frame.

Usage

round_df(df, digits)
round_df(df, digits)

Arguments

`df`	data.frame object
`digits`	passed to `round`

Value

data.frame object

Examples

data("iris")
iris %>% round_df(digits = 0) %>% head()
data("iris")
iris %>% round_df(digits = 0) %>% head()

Useful to visualize distribution of assay values for each sample. Plots 'boxplot' of any assay for each sample. Aesthetic can be added from colData.

Description

Useful to visualize distribution of assay values for each sample. Plots 'boxplot' of any assay for each sample. Aesthetic can be added from colData.

Usage

sampleAssay_plot(se, assayName = "counts", ...)
sampleAssay_plot(se, assayName = "counts", ...)

Arguments

`se`	Object of `SummarizedExperiment` class
`assayName`	One of the values from SummarizedExperiment::assayNames(se)
`...`	other arguments to be passed to ggpubr::`ggboxplot`

Value

ggplot object

Examples

se <- readRDS(system.file("extdata","rat_vole_mouseSE_salmon.rds", package = "broadSeq"))

sampleAssay_plot(se, assayName = "counts",
fill="stage", # stage is a column name of colData(se)
yscale="log2")

se <- broadSeq::normalizeEdgerCPM(se ,method = "none",cpm.log = TRUE )

sampleAssay_plot(se, assayName = "logCPM", fill="stage")
se <- readRDS(system.file("extdata","rat_vole_mouseSE_salmon.rds", package = "broadSeq"))

sampleAssay_plot(se, assayName = "counts",
fill="stage", # stage is a column name of colData(se)
yscale="log2")

se <- broadSeq::normalizeEdgerCPM(se ,method = "none",cpm.log = TRUE )

sampleAssay_plot(se, assayName = "logCPM", fill="stage")

Transform SummarizedExperiment with DESeq2 package

Description

To use SummarizedExperiment with DESeq2, this function makes sure that 'counts' assay should be the first in assays list and the mode is integer.

Usage

transformDESeq2(se, method = "vst", ...)
transformDESeq2(se, method = "vst", ...)

Arguments

`se`	Object of `SummarizedExperiment` class
`method`	"vst", "normTransform" or "rlog" to choose either of DESeq2::`varianceStabilizingTransformation` DESeq2::`normTransform` and DESeq2::`rlog` function. default is "vst"
`...`	arguments passed to `varianceStabilizingTransformation` `normTransform` and `rlog`

Value

Object of SummarizedExperiment class where a new assay is added to the input object.

Examples

 se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

se <- broadSeq::transformDESeq2(se,method = "vst"  )
# The transformed values are added with the assay name "vst"
SummarizedExperiment::assayNames(se)
se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

se <- broadSeq::transformDESeq2(se,method = "vst"  )
# The transformed values are added with the assay name "vst"
SummarizedExperiment::assayNames(se)

To use SummarizedExperiment with package DELocal

Description

A wrapper function of DELocal where input is an object of SummarizedExperiment

Usage

use_DELocal(se, colData_id, control, treatment, rank = FALSE, ...)
use_DELocal(se, colData_id, control, treatment, rank = FALSE, ...)

Arguments

`se`	Object of `SummarizedExperiment` class
`colData_id`	One of the columns of colData(se). It should be factors of more than one value.
`control`	Base level and one of the factor values of `colData(se)[[colData_id]]`
`treatment`	one of the factor values of `colData(se)[[colData_id]]`
`rank`	Logical value default FALSE. If true the result will have an additional column named "rank" and the results are ranked on "relative.logFC"
`...`	other arguments to be passed to main function DELocal::`DELocal`.

Value

a data.frame from DELocal

Examples


se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

# To reduce runtime
se <- se[rowData(se)$chromosome_name == 2,colData(se)$species == "Mouse"]

result <-
    use_DELocal(se = se,
           colData_id = "stage", control = "Bud", treatment = "Cap",
           rank = TRUE)
se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

# To reduce runtime
se <- se[rowData(se)$chromosome_name == 2,colData(se)$species == "Mouse"]

result <-
    use_DELocal(se = se,
           colData_id = "stage", control = "Bud", treatment = "Cap",
           rank = TRUE)

To use SummarizedExperiment with package DESeq2

Description

A wrapper function of DESeq2 where input is an object of SummarizedExperiment

Usage

use_deseq2(se, colData_id, control, treatment, rank = FALSE, ...)
use_deseq2(se, colData_id, control, treatment, rank = FALSE, ...)

Arguments

`se`	Object of `SummarizedExperiment` class
`colData_id`	One of the columns of colData(se). It should be factors of more than one value.
`control`	Base level and one of the factor values of `colData(se)[[colData_id]]`
`treatment`	one of the factor values of `colData(se)[[colData_id]]`
`rank`	Logical value default FALSE. If true the result will have an additional column named "rank" and the results are ranked on "padj"
`...`	other arguments to be passed to main function DESeq2::`results`.

Value

a data.frame converted from DESeq2::DESeqResults

Examples


se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

# To reduce runtime
se <- se[rowData(se)$chromosome_name == 2,colData(se)$species == "Mouse"]

result <-
    use_deseq2(se = se,
           colData_id = "stage", control = "Bud", treatment = "Cap",
           rank = TRUE)
se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

# To reduce runtime
se <- se[rowData(se)$chromosome_name == 2,colData(se)$species == "Mouse"]

result <-
    use_deseq2(se = se,
           colData_id = "stage", control = "Bud", treatment = "Cap",
           rank = TRUE)

To use SummarizedExperiment with package EBSeq

Description

A wrapper function of EBSeq where input is an object of SummarizedExperiment

Usage

use_EBSeq(se, colData_id, control, treatment, rank = FALSE, ...)
use_EBSeq(se, colData_id, control, treatment, rank = FALSE, ...)

Arguments

`se`	Object of `SummarizedExperiment` class
`colData_id`	One of the columns of colData(se). It should be factors of more than one value.
`control`	Base level and one of the factor values of `colData(se)[[colData_id]]`
`treatment`	one of the factor values of `colData(se)[[colData_id]]`
`rank`	Logical value default FALSE. If true the result will have an additional column named "rank" and the results are ranked on "PPDE"
`...`	other arguments to be passed to main function EBSeq::`GetDEResults`.

Value

a data.frame object converted from the output of EBSeq::GetDEResults.

Examples


se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

# To reduce runtime
se <- se[rowData(se)$chromosome_name == 2,colData(se)$species == "Mouse"]

result <-
    use_EBSeq(se = se,
           colData_id = "stage", control = "Bud", treatment = "Cap",
           rank = TRUE)
se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

# To reduce runtime
se <- se[rowData(se)$chromosome_name == 2,colData(se)$species == "Mouse"]

result <-
    use_EBSeq(se = se,
           colData_id = "stage", control = "Bud", treatment = "Cap",
           rank = TRUE)

To use SummarizedExperiment with package edgeR

Description

A wrapper function of DESeq2 where input is an object of SummarizedExperiment

Usage

use_edgeR_GLM(se, colData_id, control, treatment, rank = FALSE, ...)

use_edgeR_exact(se, colData_id, control, treatment, rank = FALSE, ...)

use_edgeR(
  se,
  colData_id,
  control,
  treatment,
  rank = FALSE,
  edgeR.n = Inf,
  edgeR.adjust.method = "BH",
  edgeR.sort.by = "PValue",
  option = "GLM",
  ...
)
use_edgeR_GLM(se, colData_id, control, treatment, rank = FALSE, ...)

use_edgeR_exact(se, colData_id, control, treatment, rank = FALSE, ...)

use_edgeR(
  se,
  colData_id,
  control,
  treatment,
  rank = FALSE,
  edgeR.n = Inf,
  edgeR.adjust.method = "BH",
  edgeR.sort.by = "PValue",
  option = "GLM",
  ...
)

Arguments

`se`	Object of `SummarizedExperiment` class
`colData_id`	One of the columns of colData(se). It should be factors of more than one value.
`control`	Base level and one of the factor values of `colData(se)[[colData_id]]`
`treatment`	one of the factor values of `colData(se)[[colData_id]]`
`rank`	Logical value default FALSE. If true the result will have an additional column named "rank"
`...`	other arguments to be passed to edgeR::`glmLRT` or edgeR::`exactTest`
`edgeR.n`	argument for edgeR::`topTags`
`edgeR.adjust.method`	argument for edgeR::`topTags`
`edgeR.sort.by`	argument for edgeR::`topTags`
`option`	"GLM" or "exact" to indicate to use either edgeR::`glmLRT` or edgeR::`exactTest`

Value

a data.frame of output from edgeR::topTags

Examples


se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

# To reduce runtime
se <- se[rowData(se)$chromosome_name == 2,colData(se)$species == "Mouse"]

result <-
    use_edgeR(se = se,
           colData_id = "stage", control = "Bud", treatment = "Cap",
           rank = TRUE)
se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

# To reduce runtime
se <- se[rowData(se)$chromosome_name == 2,colData(se)$species == "Mouse"]

result <-
    use_edgeR(se = se,
           colData_id = "stage", control = "Bud", treatment = "Cap",
           rank = TRUE)

To use SummarizedExperiment with package limma

Description

A wrapper function of limma where input is an object of SummarizedExperiment

Usage

use_limma_trend(se, colData_id, control, treatment, rank = FALSE, ...)

use_limma_voom(se, colData_id, control, treatment, rank = FALSE, ...)

use_limma(
  se,
  colData_id,
  control,
  treatment,
  rank = FALSE,
  useVoom = TRUE,
  showPlot = FALSE,
  limma.adjust = "BH",
  limma.sort.by = "p",
  limma.number = Inf,
  ...
)
use_limma_trend(se, colData_id, control, treatment, rank = FALSE, ...)

use_limma_voom(se, colData_id, control, treatment, rank = FALSE, ...)

use_limma(
  se,
  colData_id,
  control,
  treatment,
  rank = FALSE,
  useVoom = TRUE,
  showPlot = FALSE,
  limma.adjust = "BH",
  limma.sort.by = "p",
  limma.number = Inf,
  ...
)

Arguments

`se`	Object of `SummarizedExperiment` class
`colData_id`	One of the columns of colData(se). It should be factors of more than one value.
`control`	Base level and one of the factor values of `colData(se)[[colData_id]]`
`treatment`	one of the factor values of `colData(se)[[colData_id]]`
`rank`	Logical value default FALSE. If true the result will have an additional column named "rank"
`...`	other arguments to be passed to main function edgeR::calcNormFactors .
`useVoom`	whether to use limma::voom or edgeR::cpm
`showPlot`	whether to use limma::plotSA ; default FALSE
`limma.adjust`	argument for limma::topTable
`limma.sort.by`	argument for limma::topTable
`limma.number`	argument for limma::topTable

Value

a data.frame of output from limma::topTable

Examples


se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

# To reduce runtime
se <- se[rowData(se)$chromosome_name == 2,colData(se)$species == "Mouse"]
result <-
    use_limma(se = se,
           colData_id = "stage", control = "Bud", treatment = "Cap",
           rank = TRUE)
se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

# To reduce runtime
se <- se[rowData(se)$chromosome_name == 2,colData(se)$species == "Mouse"]
result <-
    use_limma(se = se,
           colData_id = "stage", control = "Bud", treatment = "Cap",
           rank = TRUE)

To identify differentially expressed genes by multiple methods

Description

To identify differentially expressed genes by multiple methods

Usage

use_multDE(
  deFun_list,
  return.df = FALSE,
  se,
  colData_id,
  control,
  treatment,
  ...
)
use_multDE(
  deFun_list,
  return.df = FALSE,
  se,
  colData_id,
  control,
  treatment,
  ...
)

Arguments

`deFun_list`	a list of function which can perform differential expression analysis
`return.df`	whether to return all results aggregated form of data.frame or a list of results. Default is FALSE
`se`	Object of `SummarizedExperiment` class
`colData_id`	One of the columns of colData(se). It should be factors of more than one value.
`control`	Base level and one of the factor values of `colData(se)[[colData_id]]`
`treatment`	one of the factor values of `colData(se)[[colData_id]]`
`...`	other arguments to be passed to functions listed in deFun_list

Value

a list or data.frame

Examples


se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

# To reduce runtime
se <- se[rowData(se)$chromosome_name == 2,colData(se)$species == "Mouse"]

# First define a named list of functions
funs <- list(limma_trend = use_limma_trend, limma_voom = use_limma_voom,
             edgeR_exact = use_edgeR_exact, edgeR_glm = use_edgeR_GLM,
             deseq2 = use_deseq2,
             DELocal = use_DELocal, noiseq = use_NOIseq,
             EBSeq = use_EBSeq)


multi_result <- broadSeq::use_multDE(
    se <- se[rowData(se)$chromosome_name == 2,colData(se)$species == "Mouse"],
    deFun_list = funs, return.df = TRUE,
    colData_id = "stage", control = "Bud", treatment = "Cap",
    rank = TRUE)
se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

# To reduce runtime
se <- se[rowData(se)$chromosome_name == 2,colData(se)$species == "Mouse"]

# First define a named list of functions
funs <- list(limma_trend = use_limma_trend, limma_voom = use_limma_voom,
             edgeR_exact = use_edgeR_exact, edgeR_glm = use_edgeR_GLM,
             deseq2 = use_deseq2,
             DELocal = use_DELocal, noiseq = use_NOIseq,
             EBSeq = use_EBSeq)


multi_result <- broadSeq::use_multDE(
    se <- se[rowData(se)$chromosome_name == 2,colData(se)$species == "Mouse"],
    deFun_list = funs, return.df = TRUE,
    colData_id = "stage", control = "Bud", treatment = "Cap",
    rank = TRUE)

Differential expression method for NOISeq

Description

This is a wrapper function of NOISeq::noiseqbio whose input class is ´eSet´ and output class is Output which are not widely used. We can use as(se, "ExpressionSet") to get an eSet easily but then it will be hard to refer the treatment and control. The order of factors influence the log fold change sign. To keep it comparable to other methods the NOISeq::readData() is used internally.

Usage

use_NOIseq(se, colData_id, control, treatment, rank = FALSE, ...)
use_NOIseq(se, colData_id, control, treatment, rank = FALSE, ...)

Arguments

`se`	Object of `SummarizedExperiment` class
`colData_id`	One of the columns of colData(se). It should be factors of more than one value.
`control`	Base level and one of the factor values of `colData(se)[[colData_id]]`
`treatment`	one of the factor values of `colData(se)[[colData_id]]`
`rank`	Logical value default FALSE. If true the result will have an additional column named "rank" which is ordered by ´prob´ values returned by function NOISeq::`noiseqbio`.
`...`	other arguments to be passed to main function NOISeq::`noiseqbio`. The 'input' and 'factor' argument should not be used.

Value

A data.frame object from the results of NOISeq::noiseqbio(). For details check the documentation of ´NOISeq´

Examples


se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

# To reduce runtime
se <- se[rowData(se)$chromosome_name == 2,colData(se)$species == "Mouse"]

result_Noiseq <-
    use_NOIseq(se = se,
           colData_id = "stage", control = "Bud", treatment = "Cap",
           rank = TRUE,
           r = 10) # r is an argument of NOISeq::noiseqbio
se <- readRDS(system.file("extdata",
        "rat_vole_mouseSE_salmon.rds",
        package = "broadSeq"))

# To reduce runtime
se <- se[rowData(se)$chromosome_name == 2,colData(se)$species == "Mouse"]

result_Noiseq <-
    use_NOIseq(se = se,
           colData_id = "stage", control = "Bud", treatment = "Cap",
           rank = TRUE,
           r = 10) # r is an argument of NOISeq::noiseqbio

To use SummarizedExperiment with package samr

Description

To use SummarizedExperiment with package samr

Usage

use_SAMseq(se, colData_id, control, treatment, rank = FALSE, ...)
use_SAMseq(se, colData_id, control, treatment, rank = FALSE, ...)

Arguments

`se`	Object of `SummarizedExperiment` class
`colData_id`	One of the columns of colData(se). It should be factors of more than one value.
`control`	Base level and one of the factor values of `colData(se)[[colData_id]]`
`treatment`	one of the factor values of `colData(se)[[colData_id]]`
`rank`	Logical value default FALSE. If true the result will have an
`...`	other arguments to be passed to samr::SAMseq

Value

a data.frame object as a result

Volcano plot with formatted x and y axis label.

Description

Volcano plot with formatted x and y axis label.

Usage

volcanoPlot(
  df,
  pValName,
  lFCName,
  sigThreshold = 0.05,
  logFCThreshold = 1,
  labelName = NULL,
  selectedLabel = NULL,
  palette = "nejm"
)
volcanoPlot(
  df,
  pValName,
  lFCName,
  sigThreshold = 0.05,
  logFCThreshold = 1,
  labelName = NULL,
  selectedLabel = NULL,
  palette = "nejm"
)

Arguments

`df`	a data.frame object
`pValName`	column name of df which provides p-values
`lFCName`	column name of df which provides log fold change values
`sigThreshold`	Threshold for p-values
`logFCThreshold`	Threshold for log fold change values
`labelName`	column name of df to label the dots
`selectedLabel`	which dots to highlight
`palette`	one of "npg" ,"aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "nejm" or similar to viridis::cividis(3)

Value

ggplot object

Package 'broadSeq'

Help Index

broadSeq : for streamlined exploration of RNA-seq data

Description

Author(s)

See Also

Provides GO gene set enrichment and over-representation analysis

Description

Usage

Arguments

Value

Expression of multiple genes/features from a single assay as boxplot (or added dotplot)

Description

Usage

Arguments

Value

Examples

Use of edgeR package to normalize count data

Description

Usage

Arguments

Value

Examples

Classical multidimensional scaling

Description

Usage

Arguments

Value

Examples

Plot clustered heatmaps

Description

Usage

Arguments

Value

Examples

Perform Principal Components Analysis

Description

Usage

Arguments

Details

Value

Examples

Applies round function only on numeric columns of a data.frame.

Description

Usage

Arguments

Value

Examples

Useful to visualize distribution of assay values for each sample. Plots 'boxplot' of any assay for each sample. Aesthetic can be added from colData.

Description

Usage

Arguments

Value

Examples

Transform SummarizedExperiment with DESeq2 package

Description

Usage

Arguments

Value

Examples

To use SummarizedExperiment with package DELocal

Description

Usage

Arguments

Value

Examples

To use SummarizedExperiment with package DESeq2

Description

Usage

Arguments

Value

Examples

To use SummarizedExperiment with package EBSeq

Description

Usage

Arguments

Value

Examples

To use SummarizedExperiment with package edgeR

Description