Package 'hammers'

Add a categorical column to a data frame based on another column

Description

This function adds a categorical column to a data frame based on another column.

Usage

addCategory(df, col, newCol, keys, values)

Arguments

df	A data frame.
col	Column whose values will be used for creating the new column.
newCol	Column to be added.
keys	A list of keys. If vectors are part of the keys, each of their elements will be assigned the corresponding value.
values	A vector of values. Must have the same length as keys.

Value

A data frame with a new categorical column.

Examples

df <- data.frame(fruit = c('apple', 'banana', 'cherry', 'grape'))
df <- addCategory(df,
                'fruit',
                'color',
                list(c('apple', 'cherry'),
                'banana',
                'grape'),
                c('red', 'yellow', 'purple'))

---

Add a categorical column to a Seurat metadata or SingleCellExperiment coldata

Description

Add a categorical column to a Seurat metadata or SingleCellExperiment coldata

Usage

addMetadataCategory(
  scObj,
  col,
  newCol,
  keys,
  values,
  newCol2 = NULL,
  values2 = NULL
)

Arguments

scObj	A Seurat, SingleCellExperiment, dgCMatrix or matrix object.
col	Column whose values will be used for creating the new column.
newCol	Column to be added.
keys	A list of keys. If vectors are part of the keys, each of their elements will be assigned the corresponding value.
values	A vector of values. Must have the same length as keys.
newCol2	A second column to be added based on the same keys. Default is NULL (no second column will be added).
values2	A vector of values corresponding to the second column. Default is NULL (no second column will be added).

Value

A Seurat or SingleCellExpression object with one or two new categorical column(s) in the metadata/coldata.

Examples

scePath <- system.file('extdata', 'sceObj.qs2', package='hammers')
sceObj <- qs2::qs_read(scePath)
sceObj <- addMetadataCategory(sceObj, 'Cell_Cycle', 'Type',
list(c('G0', 'G1'), 'G2M', 'S'), c(2, 3, 1))

---

Adds normalized silhouette column to a single-cell expression object

Description

This function adds a normalized silhouette column to a single-cell expression object.

Usage

addNormSilhouette(scObj, normSilDF, normSilCol = "normSilhouette")

Arguments

scObj	A Seurat, SingleCellExperiment, dgCMatrix or matrix object.
normSilDF	Normalized silhouette data frame.
normSilCol	The name of the normalized silhouette column to be added.

Value

The input object (Seurat or SingleCellExperiment) with an added metadata normalized silhouette column.

Examples

scePath <- system.file('extdata', 'sceObj.qs2', package='hammers')
sceObj <- qs2::qs_read(scePath)
df <- normalizeSilhouette(sceObj, 'Cluster')
sceObj <- addNormSilhouette(sceObj, df)

---

Calculate the center of mass of columns

Description

This function calculates the center of mass based on the columns of a data frame or matrix and a vector of weights.

Usage

centerOfMass(obj, weights)

Arguments

obj	A data frame or matrix.
weights	A vector of weights.

Value

A vector containing the center of mass.

Examples

obj <- matrix(data=c(2, 3, 1, 3, 6, 8), nrow=3, ncol=2)
weights <- c(0.8, 6, 16)
centerOfMass(obj, weights)

---

Check if all genes exist in the single-cell expression object

Description

This function checks if all genes exist in the single-cell expression object.

Usage

checkGenes(scObj, genes)

Arguments

scObj	A Seurat, SingleCellExperiment, dgCMatrix or matrix object.
genes	A character vector of genes.

Value

None. This function is called for its side effect.

Examples

scePath <- system.file('extdata', 'sceObj.qs2', package='hammers')
sceObj <- qs2::qs_read(scePath)
checkGenes(sceObj, c('Gene_0480', 'Gene_0481', 'Gene_0482'))

---

Calculate the centers of mass of metadata/coldata columns

Description

This function calculates the centers of mass of selected metadata/coldata columns from a Seurat or SingleCellExpression object.

Usage

colCenters(scObj, columns)

Arguments

scObj	A Seurat, SingleCellExperiment, dgCMatrix or matrix object.
columns	Numeric columns.

Value

A data frame containing the coordinates of centers of mass.

Examples

scePath <- system.file('extdata', 'sceObj.qs2', package='hammers')
sceObj <- qs2::qs_read(scePath)
colCenters(sceObj, c('sizeFactor'))

---

Create a single-cell dimensionality reduction plot with added labeled points for numeric columns

Description

This function creates a single-cell dimensionality reduction plot with added labeled points for metadata numeric columns.

Usage

colsDimPlot(scObj, cols, ...)

Arguments

scObj	A Seurat, SingleCellExperiment, dgCMatrix or matrix object.
cols	Columns whose centers of mass will be plotted.
...	Additional parameters passed to pointsDimPlot.

Value

A ggplot object.

Examples

scePath <- system.file('extdata', 'sceObj.qs2', package='hammers')
sceObj <- qs2::qs_read(scePath)
colsDimPlot(sceObj, c('sizeFactor', 'silhouette'))

---

Compute cluster silhouette for single-cell expression object

Description

This function computes the silhouette for each cell in the Seurat or SingleCellExperiment object.

Usage

computeSilhouette(scObj, idClass, silCol = "silhouette")

Arguments

scObj	A Seurat, SingleCellExperiment, dgCMatrix or matrix object.
idClass	Identity class. Must be present among the metadata columns of the single-cell expression object.
silCol	The name of the silhouette column to be added.

Value

The input object (Seurat or SingleCellExperiment) with an added metadata silhouette column.

Examples

scePath <- system.file('extdata', 'sceObj.qs2', package='hammers')
sceObj <- qs2::qs_read(scePath)
sceObj <- computeSilhouette(sceObj, 'Cluster')

---

Saves plot or list of plots

Description

This function saves a plot or list of plots as a pdf. Can also take as input a function that returns a ggplot object together with its arguments.

Usage

## Default S3 method:
devPlot(plotObject, ...)

## S3 method for class ''function''
devPlot(plotObject, ...)

## S3 method for class 'ggplot'
devPlot(plotObject, ...)

## S3 method for class 'list'
devPlot(plotObject, ...)

devPlot(plotObject, ...)

Arguments

plotObject	A function, ggplot object, or list of ggplot objects.
...	Additional arguments.

Value

No value. This function is called for its side effect.

Examples

library(ggplot2)
df <- data.frame(x = c(1, 2), y = c(3, 5))
p <- ggplot(df) + geom_point(aes(x, y))
devPlot(p)

simplePlot <- function(df, title)
    return(ggplot(df) + geom_point(aes(x, y)) + ggtitle(title))

devPlot(simplePlot, df, 'Plot title')

if (file.exists('Rplots.pdf'))
    file.remove('Rplots.pdf')
if (file.exists('Rplots1.pdf'))
    file.remove('Rplots1.pdf')

---

Plot the distribution of cells across two columns

Description

This function plots the distribution of cells across two columns.

Usage

distributionPlot(
  scObj,
  title = NULL,
  col1 = "seurat_clusters",
  col2 = "orig.ident",
  type = c("counts", "percs"),
  xLab = col1,
  yLab = if (type == "counts") "Count" else "Percentage",
  legendLab = col2,
  palette = "Spectral",
  legendPos = "right",
  legendTextSize = 10,
  legendTitleSize = 10,
  axisTextSize = 12,
  axisTitleSize = 12,
  sigDigits = 2
)

Arguments

scObj	A Seurat, SingleCellExperiment, dgCMatrix or matrix object.
title	Plot title.
col1	Column as string.
col2	Column as string.
type	Whether the plot should display counts ('counts', default) or percentages ('percs').
xLab	x axis label.
yLab	y axis label.
legendLab	Legend label.
palette	Color palette.
legendPos	Legend position.
legendTextSize	Legend text size.
legendTitleSize	Legend title size.
axisTextSize	Axis text size.
axisTitleSize	Axis title size.
sigDigits	Number of significant digits used by percentages displayed on the plot. Ignored if type is 'counts'.

Value

A ggplot object.

Examples

scePath <- system.file('extdata', 'sceObj.qs2', package='hammers')
sceObj <- qs2::qs_read(scePath)
p <- distributionPlot(sceObj, col1='Cluster', col2='Donor')

---

Find rare genes in a Seurat or SingleCellExpression object

Description

This function finds genes expressed in a low number of cells in a Seurat or SingleCellExpression object.

Usage

findRareGenes(scObj, nCells = 10)

Arguments

scObj	A Seurat, SingleCellExperiment, dgCMatrix or matrix object.
nCells	Minimum number of cells in which a gene must be expressed to be regarded as non-rare.

Value

A data frame with the rare genes as rownames and a single column representing their frequencies.

Examples

scePath <- system.file('extdata', 'sceObj.qs2', package='hammers')
sceObj <- qs2::qs_read(scePath)
df <- findRareGenes(sceObj)

---

Generates cell sets for each input gene

Description

This function constructs, for each input gene, sets of cells expressing the gene

Usage

geneCellSets(scObj, genes = NULL)

Arguments

scObj	A Seurat, SingleCellExperiment, dgCMatrix or matrix object.
genes	A character vector of genes.

Value

A named list of character vectors of cell names.

Examples

mat <- matrix(0, 1000, 500)
rownames(mat) <- paste0('G', seq(1000))
colnames(mat) <- paste0('C', seq(500))
mat[sample(length(mat), 70000)] <- sample(50, 70000, TRUE)
mat <- mat[paste0('G', sample(1000, 3)), ]
geneCellSets(mat)

---

Calculate the centers of mass of the expression of input genes

Description

This function calculates the centers of mass of the expression of input genes.

Usage

geneCenters(scObj, genes)

Arguments

scObj	A Seurat, SingleCellExperiment, dgCMatrix or matrix object.
genes	A character vector of genes.

Value

A data frame containing the centers of mass.

Examples

scePath <- system.file('extdata', 'sceObj.qs2', package='hammers')
sceObj <- qs2::qs_read(scePath)
geneCenters(sceObj, c('Gene_0480', 'Gene_0481', 'Gene_0482'))

---

Extract gene presence from a Seurat or SingleCellExperiment object

Description

This function extracts the number of cells in which a gene from a Seurat or SingleCellExperiment is expressed.

Usage

genePresence(scObj, genes = NULL, minCutoff = NULL, maxCutoff = NULL)

Arguments

scObj	A Seurat, SingleCellExperiment, dgCMatrix or matrix object.
genes	Genes for which the number of cells in which the gene is expressed will be computed. If NULL (as default), all genes will be included in the assessment.
minCutoff	Minimum cutoff for gene counts. Genes with counts below this value will be omitted.
maxCutoff	Maximum cutoff for gene counts. Genes with counts above this value will be omitted.

Value

A data frame with two columns. The first column lists the genes ordered decreasingly by the number of cells in which they appear, the second lists the corresponding numbers of cells.

Examples

scePath <- system.file('extdata', 'sceObj.qs2', package='hammers')
sceObj <- qs2::qs_read(scePath)
df <- genePresence(sceObj)

---

Create a single-cell dimensionality reduction plot with added labeled points for genes

Description

This function creates a single-cell dimensionality reduction plot with added labeled points for genes.

Usage

genesDimPlot(scObj, genes, ...)

Arguments

scObj	A Seurat, SingleCellExperiment, dgCMatrix or matrix object.
genes	Genes whose centers of mass will be plotted.
...	Additional parameters passed to pointsDimPlot.

Value

A ggplot object.

Examples

scePath <- system.file('extdata', 'sceObj.qs2', package='hammers')
sceObj <- qs2::qs_read(scePath)
genesDimPlot(sceObj, c('Gene_0364', 'Gene_0388', 'Gene_0477'))

---

Join all combinations of elements from character vectors

Description

This function joins all combinations of elements from character vectors with a separating character.

Usage

joinCharCombs(..., joinChar = "_")

Arguments

...	Vectors passed to expandGrid.
joinChar	Character used to join combinations.

Value

A character vector.

Examples

joinCharCombs(c('a', 'b', 'c', 'd'), c('eee', 'ff'), c(1, 2, 3))

---

Create a map from keys to values

Description

This function creates a map from keys to values.

Usage

keyvalMap(keys, values)

Arguments

keys	A list of keys. If vectors are part of the keys, each of their elements will be assigned the corresponding value.
values	A vector of values. Must have the same length as keys.

Value

A named vector.

Examples

keyvalMap(list(2, c(3, 4, 5), 6, 8), c('a', 'b', 'c', 'd'))

---

Normalize silhouette by identity class for single-cell expression object

Description

This function normalizes the already computed silhouette for each identity class in the single-cell expression object.

Usage

normalizeSilhouette(scObj, idClass, silCol = "silhouette")

Arguments

scObj	A Seurat, SingleCellExperiment, dgCMatrix or matrix object.
idClass	Identity class. Must be present among the metadata columns of the single-cell expression object.
silCol	The name of the silhouette column.

Value

A data frame with normalized silhouettes for each unique element in the identity class.

Examples

scePath <- system.file('extdata', 'sceObj.qs2', package='hammers')
sceObj <- qs2::qs_read(scePath)
df <- normalizeSilhouette(sceObj, 'Cluster')

---

Create a single-cell dimensionality reduction plot with added labeled points

Description

This function creates a single-cell dimensionality reduction plot with added labeled points.

Usage

pointsDimPlot(
  scObj,
  title = NULL,
  pointsObj = NULL,
  alpha = 1,
  pointShape = 4,
  pointSize = 2,
  pointColor = "black",
  labelSize = 2.5,
  maxOverlaps = 30,
  ...
)

Arguments

scObj	A Seurat, SingleCellExperiment, dgCMatrix or matrix object.
title	Plot title.
pointsObj	A data frame or matrix of points with two columns representing x and y coordinates.
alpha	Opaqueness level.
pointShape	Point shape.
pointSize	Point size.
pointColor	Point color.
labelSize	Label size. If NULL, the points will not receive labels.
maxOverlaps	Maximum overlaps.
...	Additional parameters passed to Seurat::DimPlot.

Details

A wrapper around scLang::dimPlot.

Value

A ggplot object.

Examples

scePath <- system.file('extdata', 'sceObj.qs2', package='hammers')
sceObj <- qs2::qs_read(scePath)
pointsObj <- data.frame(x = c(2, 3),
y = c(1, 0),
row.names = c('P1', 'P2'))
pointsDimPlot(sceObj, pointsObj=pointsObj)

---

Prepare dataframe for alluvial plot

Description

This function extracts the relevant information from a data frame and adjusts p-values to be used as weights for the alluvia.

Usage

prepAlluvial(
  df,
  pvalCol = "pvalAdj",
  colIndices = c(1, 2),
  weightExp = 1/2,
  pvalOffset = 1.00000023069254e-317
)

Arguments

df	A data frame.
pvalCol	Name of p-value column to be used by the alluvial plot.
colIndices	A vector respresenting the indices of the two categorical columns from the data frame that will be used.
weightExp	Exponent used in constructing weight from p-values.
pvalOffset	Offset used to avoid zeros inside the logarithm function.

Value

A data frame with weight scores in lieu of p-values.

Examples

df <- data.frame(A = c('a1', 'a2', 'a3', 'a4'),
B = c('b1', 'b2', 'b3', 'b4'),
pvalAdj = c(0.81, 1e-6, 1e-3, 0.022))
prepAlluvial(df)

---

Plot representation data frame

Description

This function plots representation data frame as an alluvial plot.

Usage

pvalRiverPlot(
  df,
  pvalCol = "pvalAdj",
  colIndices = c(1, 2),
  weightExp = 1/2,
  pvalOffset = 1.00000023069254e-317,
  ...
)

Arguments

df	A data frame.
pvalCol	Name of p-value column to be used by the alluvial plot.
colIndices	A vector respresenting the indices of the two categorical columns from the data frame that will be used.
weightExp	Exponent used in constructing weight from p-values.
pvalOffset	Offset used to avoid zeros inside the logarithm function.
...	Additional parameters passed to henna::riverPlot

Value

A ggplot object

Examples

scePath <- system.file('extdata', 'sceObj.qs2', package='hammers')
sceObj <- qs2::qs_read(scePath)
df <- repAnalysis(sceObj, 'Cluster', 'Donor')
pvalRiverPlot(df)

---

Remove rare genes from a Seurat or SingleCellExpression object

Description

This function removes genes expressed in a low number of cells in a Seurat or SingleCellExpression object.

Usage

removeRareGenes(scObj, nCells = 10, verbose = TRUE)

Arguments

scObj	A Seurat, SingleCellExperiment, dgCMatrix or matrix object.
nCells	Minimum number of cells in which a gene must be expressed to be retained.
verbose	Logical; whether the output should be verbose.

Value

A Seurat or SingleCellExpression object with rare genes removed.

Examples

scePath <- system.file('extdata', 'sceObj.qs2', package='hammers')
sceObj <- qs2::qs_read(scePath)
sceObj <- removeRareGenes(sceObj, 30)

---

Find the differential representation of two Seurat or SingleCellExperiment columns

Description

This function finds the differential representation of two Seurat or SingleCellExperiment columns.

Usage

repAnalysis(
  scObj,
  col1 = "seurat_clusters",
  col2 = "orig.ident",
  doOverrep = TRUE,
  mtMethod = c("BY", "holm", "hochberg", "hommel", "bonferroni", "BH", "fdr", "none"),
  ...
)

Arguments

scObj	A Seurat, SingleCellExperiment, dgCMatrix or matrix object.
col1	Column as string.
col2	Column as string.
doOverrep	Whether to perform overrepresentation analysis (TRUE) or underrepresentation analysis (FALSE).
mtMethod	Multiple testing correction method. Choices are 'BY' (default), 'holm', hochberg', hommel', 'bonferroni', 'BH', 'fdr' and 'none'.
...	Additional parameters passed to LISTO::mtCorrectDF.

Value

An overrepresentation or underrepresentation data frame.

Examples

scePath <- system.file('extdata', 'sceObj.qs2', package='hammers')
sceObj <- qs2::qs_read(scePath)
repAnalysis(sceObj, 'Cluster', 'Donor')

---

Message an input if verbose is set to TRUE

Description

This function messages an input if verbose is set to TRUE.

Usage

safeMessage(msg, verbose = TRUE)

Arguments

msg	Message
verbose	Whether the message should be displayed.

Value

No return value. This function is called for its side effect (messaging the input if verbose is set to TRUE).

Examples

safeMessage('message')

---

Perform min-max normalization when possible; otherwise return a single-value vector.

Description

This function min-max-normalizes a vector when possible, and otherwise returns a single-value vector.

Usage

safeMinmax(scores, safeVal = 0)

Arguments

scores	Numeric vector.
safeVal	Value to replace all values with when all values in the vector are the same.

Value

Min-max-normalized scores or a single-value vector.

Examples

safeMinmax(c(0, 3, 2, 1, 4, 5.5, 6.32, 8, 1.1))

---