Package 'DgeaHeatmap'

Title: Implementation of Functions to Analyze Nanostring and Other Data for DGE and to Generate Heatmaps
Description: Package for data extraction from Nanostring GeoMx DSP data, also works for other data. Simple functions for Differential Expression Analysis. User-friendly and highly customizable functions for heatmap generation.
Authors: Leonie Johanna Lancelle [aut, cre] (ORCID: <https://orcid.org/0009-0004-3138-0752>)
Maintainer: Leonie Johanna Lancelle <[email protected]>
License: GPL-3 + file LICENSE
Version: 0.99.0
Built: 2026-07-21 14:46:38 UTC
Source: https://github.com/BiocStaging/DgeaHeatmap

Help Index

Build a matrix from an input csv file, with one column as rownames

Description

Build a matrix from an input csv file, with one column as rownames

Usage

add_demoElem(demoData)

Arguments

demoData

A NanostringGeoMxSet object.

Value

A NanostringGeoMxSet object.

Examples

demoData <- readRDS(
  system.file(
    "extdata",
    "miniGeoMx.rds",
    package = "DgeaHeatmap"
  )
)

demoData <- add_demoElem(demoData)

Creating a color scheme based on the available color palettes of RColorBrewer for the heatmap.

Description

Creating a color scheme based on the available color palettes of RColorBrewer for the heatmap.

Usage

adv_Heatmap(
  ncounts_matrix,
  column_name = "Heatmap",
  colorPalette = NULL,
  cluster_method = "hierarchical",
  distance_method = "euclidean",
  cluster_rows = TRUE,
  cluster_columns = FALSE,
  k_row = NULL,
  k_col = NULL,
  sample_metadata = NULL,
  annotation_colors = NULL,
  annotation_name_side = "right",
  show_row_names = FALSE,
  show_column_names = TRUE,
  row_annotation = FALSE,
  row_annotation_method = "auto",
  row_anno_names = NULL,
  row_anno_number = 5,
  fontsize_title = 15,
  fontsize_rowAnnotation = 10,
  fontsize_columnNames = 6,
  fontsize_rowNames = 4,
  fontsize_cluster_labels = 8,
  fontsize_group_annotation = 8,
  fontsize_group_annotation_legend = 10,
  fontsize_group_annotation_labels = 8,
  fontsize_heatmap_legend = 10,
  fontsize_heatmap_legend_labels = 8,
  title_heatmapLegend = "Expression",
  WidthNum = 4.5,
  HeightNum = 3,
  UnitSize = "cm"
)

Arguments

ncounts_matrix

An input matrix to create the heatmap.
column_name

A string to set the title of the heatmap, default <- "Heatmap".
colorPalette

Name of the colorPalette used for the heatmap, default <- NULL.
cluster_method

A string setting the cluster method for the heatmap, default <- "hierarchical".
distance_method

A string setting the distance method for clustering, default <- "euclidean".
cluster_rows

A Boolean switching the optional clustering of the rows on and off, default <- TRUE.
cluster_columns

A Boolean switching the optional clustering of the columns on and off, default <- FALSE.
k_row

An integer used to set number of clusters for row clustering in the heatmap, default <- NULL.
k_col

An integer used to set number of clusters for column clustering in the heatmap, default <- NULL.
sample_metadata

A dataframe containing the metadata information for the grouping of the columns, default <- NULL.
annotation_colors

A list assigning choosen colors to the corresponding groups, default <- NULL.
annotation_name_side

The side of the column annotation description, default <- "right".
show_row_names

A Boolean switching rownames on the heatmap on and off, default <- FALSE.
show_column_names

A Boolean switching colum names on the heatmap on and off, default <- TRUE.
row_annotation

A Boolean switching row annotation on the heatmap on and off, default <- FALSE.
row_annotation_method

A string setting the annotation method of the heatmap, default <- "auto".
row_anno_names

A list containing choosen rownames to use for the row annotation, default <- NULL.
row_anno_number

An integer setting the number of automatic annotations assigned per cluster, default <- 5.
fontsize_title

An integer setting the font size of the heatmap title.
fontsize_rowAnnotation

An integer setting the font size of the optional row annotation, default <- 10.
fontsize_columnNames

An integer setting the font size of the column names, default <- 6.
fontsize_rowNames

An integer setting the font size of the row names, default <- 4.
fontsize_cluster_labels

An integer setting the font size of the cluster labels, default <- 8.
fontsize_group_annotation

An integer setting the font size of the group annotation title, default <- 8.
fontsize_group_annotation_legend

An integer setting the font size of the group annotation legend name, default <- 10.
fontsize_group_annotation_labels

An integer setting the font size of the group annotation labels in the legend, default <- 8.
fontsize_heatmap_legend

An integer setting the font size of the heatmap legend title, default <- 10.
fontsize_heatmap_legend_labels

An integer setting the font size of the heatmap legend labels, default <- 8.
title_heatmapLegend

A string setting the changeable title of the legend, default "Expression".
WidthNum

A float setting the width of the heatmap, default <- 4.5.
HeightNum

A float setting the height of the heatmap, default <- 3.
UnitSize

A string such as "cm" or "inch" to set the unit of HeightNum and WidthNum, default <- "cm".

Value

An advanced and customizable heatmap.

Examples

x <- 1
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
matrixCounts <- build_matrix(input_data, x)
ncounts_matrix <- scale_counts(matrixCounts)
set.seed(1)
adv_Heatmap(ncounts_matrix)

Function for automatized quality control.

Description

Function for automatized quality control.

Usage

aExprsDataQC(demoData, vFlags)

Arguments

demoData

A NanostringGeoMxSet object, storing all expression, annotation, and probe information linked together.
vFlags

Character string to set the flags for quality control.

Value

A matrix.

Examples

demoData <- file
vFlags <- "QCFlags"

Builds a matrix from an input csv file, with one column as rownames

Description

Builds a matrix from an input csv file, with one column as rownames

Usage

build_matrix(counts_data, x)

Arguments

counts_data

A data frame with floats or integers that has strings in one column.
x

Number of column which contains the rownames description.

Value

A matrix.

Examples

x <- 1
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
matrixCounts <- build_matrix(input_data, x)

Function to set color scheme for a heatmap.

Description

Function to set color scheme for a heatmap.

Usage

color_setting(colorPalette)

Arguments

colorPalette

Chosen color palette.

Value

No return.

Examples

colorPalette <- "RdBu"
color_setting(colorPalette)

Setting the column clustering

Description

Setting the column clustering

Usage

column_clustering(
  ncounts_matrix,
  cluster_columns = TRUE,
  cluster_method = "hierarchical",
  distance_method = "euclidean",
  k_col = NULL,
  col_split = NULL,
  col_dend = TRUE
)

Arguments

ncounts_matrix

An input matrix to create the clustering.
cluster_columns

A Boolean switching the optional clustering of the rows on and off, default <- TRUE.
cluster_method

A string setting the cluster method for the heatmap.
distance_method

A string setting the distance method for clustering.
k_col

An integer used to set number of clusters for row clustering.
col_split

An integer seeting the column clustering by kmeans.
col_dend

A Boolean switching column clustering by hierarchical clustering on (default).

Value

col_split where the rows are split into clusters

Examples

x <- 1
seed <- 1
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
matrixCounts <- build_matrix(input_data, x)
ncounts_matrix <- scale_counts(matrixCounts)
set.seed
col_split <- NULL
col_dend <- TRUE
columns_clustered <- column_clustering(ncounts_matrix,
  cluster_columns = TRUE, cluster_method = "hierarchical",
  distance_method = "euclidean", k_col = NULL, col_split = col_split,
  col_dend = col_dend
)

create_contrast_matrix_edgeR

Description

Creates a contrast matrix for edgeR

Usage

create_contrast_matrix_edgeR(metadata, comparisons = NULL, prefix = "DEA")

Arguments

metadata

A dataframe containing the meta data information of the raw counts matrix.
comparisons

An element of the class list, that contains optional information on specific comparisons that are to be conducted (simplifies the results).
prefix

A string used as a prefix for the contrast_names that are generated.

Value

A matrix containing the contrasts for DEA with edgeR.

Examples

rawCounts <- read.csv(system.file(
    "extdata/RawDataExamplePackageNanostring.csv",
    package = "DgeaHeatmap"
))
rawCounts <- build_matrix(rawCounts, 1)
metadata <- readRDS(system.file(
    "extdata/MetaDataPackageNanostring.rds",
    package = "DgeaHeatmap"
))
grouping_columns <- c("segment", "region", "class", "slide_name")
comparisons <- list(
    Comp1 = c(
        "Geometric_Segment_glomerulus_DKD_disease3",
        "PanCK_tubule_DKD_disease4"
    ),
    Comp2 = c("neg_tubule_DKD_disease4", "PanCK_tubule_DKD_disease4")
)
prefix <- "DEA"
results_edgeR <- DGEAedgeR(
    rawCounts, metadata, grouping_columns, comparisons,
    prefix = "DEA"
)
contrast_matrix <- create_contrast_matrix_edgeR(
    metadata = metadata, comparisons = comparisons, prefix = prefix
)

DGEADESeq2

Description

Performs a differential expression analysis based on DESeq2

Usage

DGEADESeq2(rawCounts, metadata, grouping_columns, comparisons)

Arguments

rawCounts

A matrix containing raw counts (integers).
metadata

A dataframe containing the meta data information of the raw counts matrix.
grouping_columns

A list of the columns names in the metadata file that are used to generate the groups for the DEA that are compared with each other.
comparisons

An element of the class list, that contains optional information on specific comparisons that are to be conducted (simplifies the results).

Value

A list containing the voom data, fitting of the linear model, results of the contrasts, and the generated design.

Examples

set.seed(1)

counts <- matrix(rnbinom(80, mu = 20, size = 1), ncol = 4)
colnames(counts) <- paste0("Sample", 1:4)
rownames(counts) <- paste0("Gene", 1:20)
storage.mode(counts) <- "integer"

metadata <- data.frame(
    segment = factor(rep(c("A", "B"), each = 2)),
    row.names = colnames(counts)
)

comparisons <- list(
    Comp1 = c("A", "B")
)

results_DESeq2 <- DGEADESeq2(
    rawCounts = counts,
    metadata = metadata,
    grouping_columns = "segment",
    comparisons = comparisons
)

DGEAedgeR

Description

Performs a differential expression analysis based on edgeR

Usage

DGEAedgeR(
  rawCounts,
  metadata,
  grouping_columns,
  comparisons = NULL,
  prefix = "DEA"
)

Arguments

rawCounts

A matrix containing raw counts (integers).
metadata

A dataframe containing the meta data information of the raw counts matrix.
grouping_columns

A list of the columns names in the metadata file that are used to generate groups for the DEA that are compared with each other.
comparisons

An element of the class list, that contains optional information on specific comparisons that are to be conducted (simplifies the results).
prefix

A string used as a prefix for the contrast_names that are generated.

Value

A list containing the voom data, fitting of the linear model, results of the contrasts, and the generated design.

Examples

set.seed(1)
counts <- matrix(rnbinom(600, mu = 10, size = 1), ncol = 6)
colnames(counts) <- paste0("Sample", 1:6)
rownames(counts) <- paste0("Gene", 1:100)

# Create simple metadata
metadata <- data.frame(
    segment = factor(rep(c("A", "B"), each = 3)),
    row.names = colnames(counts)
)

comparisons <- list(
    Comp1 = c("A", "B")
)

results_edgeR <- DGEAedgeR(
    rawCounts = counts,
    metadata = metadata,
    grouping_columns = "segment",
    comparisons = comparisons,
    prefix = "DEA"
)

names(results_edgeR)

DGEALimma

Description

Performs a differential expression analysis based on limma voom

Usage

DGEALimma(
  rawCounts,
  metadata,
  grouping_columns,
  comparisons = NULL,
  prefix = "DEA"
)

Arguments

rawCounts

A matrix containing raw counts (integers).
metadata

A dataframe containing the meta data information of the raw counts matrix.
grouping_columns

A list of the columns names in the metadata file that are used to generate the groups for the DEA that are compared with each other.
comparisons

An element of the class list, that contains optional information on specific comparisons that are to be conducted (simplifies the results).
prefix

A string used as a prefix for the contrast_names that are generated.

Value

A list containing the voom data, fitting of the linear model, results of the contrasts, and the generated design.

Examples

rawCounts <- read.csv(
    system.file("extdata/RawDataExamplePackageNanostring.csv",
        package = "DgeaHeatmap"
    )
)
rawCounts <- build_matrix(rawCounts, 1)
metadata <- readRDS(system.file("extdata/MetaDataPackageNanostring.rds",
    package = "DgeaHeatmap"
))
grouping_columns <- c("segment", "region", "class", "slide_name")
comparisons <- list(Comp1 = c(
    "Geometric_Segment_glomerulus_DKD_disease3",
    "PanCK_tubule_DKD_disease4"
), Comp2 = c(
    "neg_tubule_DKD_disease4",
    "PanCK_tubule_DKD_disease4"
))
prefix <- "DEA"
DGEAListResults <- DGEALimma(
    rawCounts, metadata, grouping_columns,
    comparisons, prefix
)

Draw the advanced heatmap

Description

Draw the advanced heatmap

Usage

draw_adv_heatmap(
  ncounts_matrix,
  column_name = "Heatmap",
  colorPalette = NULL,
  col_ha = NULL,
  show_row_names = FALSE,
  show_column_names = TRUE,
  fontsize_title = 15,
  fontsize_columnNames = 6,
  fontsize_rowNames = 4,
  fontsize_cluster_labels = 8,
  fontsize_heatmap_legend = 10,
  fontsize_heatmap_legend_labels = 8,
  title_heatmapLegend = "Expression",
  WidthNum = 4.5,
  HeightNum = 3,
  UnitSize = "cm",
  row_annotation = FALSE,
  annotation_for_rows = NULL,
  row_split = NULL,
  col_split = NULL,
  row_dend = TRUE,
  col_dend = TRUE
)

Arguments

ncounts_matrix

An input matrix to create the heatmap.
column_name

A string to set the title of the heatmap.
colorPalette

Name of the colorPalette used for the heatmap, default <- NULL.
col_ha

A "HeamapAnnotation" object if "sample_metadata" is provided, otherwise NULL.
show_row_names

A Boolean switching rownames on the heatmap on and off, default <- FALSE.
show_column_names

A Boolean switching colum names on the heatmap on and off, default <- TRUE.
fontsize_title

An integer setting the font size of the heatmap title, default <- 15.
fontsize_columnNames

An integer setting the font size of the column names, default <- 6.
fontsize_rowNames

An integer setting the font size of the row names, default <- 4.
fontsize_cluster_labels

An integer setting the font size of the cluster labels, default <- 8.
fontsize_heatmap_legend

An integer setting the font size of the heatmap legend title, default <- 10.
fontsize_heatmap_legend_labels

An integer setting the font size of the heatmap legend labels, default <- 8.
title_heatmapLegend

A string setting the changeable title of the legend, default "Expression".
WidthNum

A float setting the width of the heatmap, default <- 4.5.
HeightNum

A float setting the height of the heatmap, default <- 3.
UnitSize

A string such as "cm" or "inch" to set the unit of HeightNum and WidthNum, default <- "cm".
row_annotation

A Boolean switching row annotation on the heatmap on and off, default <- FALSE.
annotation_for_rows

Numeric index from matrix for the row annotation
row_split

An integer seeting the row clustering by kmeans.
col_split

An integer seeting the column clustering by kmeans.
row_dend

A Boolean switching row clustering by hierarchical clustering on (default).
col_dend

A Boolean switching column clustering by hierarchical clustering on (default).

Value

Row annotation

Examples

set.seed(1)
mat <- matrix(
  rnorm(25),
  nrow = 5,
  dimnames = list(
    paste0("Gene", 1:5),
    paste0("Sample", 1:5)
  )
)

sample_metadata <- data.frame(
  Group = rep(c("A", "B"), length.out = 5),
  row.names = colnames(mat)
)

group_colors <- list(
  Group = c(A = "#1b9e77", B = "#7570b3")
)

col_ha <- set_sample_annotation(
  sample_metadata = sample_metadata,
  annotation_colors = group_colors
)

ht <- draw_adv_heatmap(mat)

Function to create an elbow plot to choose k for clustering by k-Means.

Description

Function to create an elbow plot to choose k for clustering by k-Means.

Usage

elbow_plot(top_genes_matrix, maxK = 15)

Arguments

top_genes_matrix

A matrix for which the best number of clusters in k-Means Clustering is supposed to be calculated.
maxK

An integer determining the maximum number of possible clusters.

Value

An elbow plot to choose k.

Examples

x <- 1
seed <- 1
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
matrixCounts <- build_matrix(input_data, x)
scaled_counts <- scale_counts(matrixCounts)
top_genes_matrix <- scaled_counts
set.seed(seed)
elbow_plot(top_genes_matrix)

Get list of genes and their direction of regulation

Description

Get list of genes and their direction of regulation

Usage

extract_genes_direction(
  results_list,
  only_up = FALSE,
  only_down = FALSE,
  only_sig = FALSE,
  padj = NULL,
  padj_cutoff = 0.05,
  lfc_cutoff = 0
)

Arguments

results_list

A list containing the results of the DEA.
only_up

A Boolean opting for only up regulated genes.
only_down

A Boolean opting for only down regulated genes.
only_sig

A Boolean opting for only significantly regulated genes.
padj

A Float, defining the adjusted p-value.
padj_cutoff

A Float, setting the adjusted p-values cutoff.
lfc_cutoff

A Float, setting the fold change cutoff.

Value

A character vector of contrast names.

Examples

rawCounts <- read.csv(
    system.file("extdata/RawDataExamplePackageNanostring.csv",
        package = "DgeaHeatmap"
    )
)
rawCounts <- build_matrix(rawCounts, 1)
metadata <- readRDS(system.file("extdata/MetaDataPackageNanostring.rds",
    package = "DgeaHeatmap"
))
grouping_columns <- c("segment", "region", "class", "slide_name")
comparisons <- list(
    Comp1 = c(
        "Geometric_Segment_glomerulus_DKD_disease3",
        "PanCK_tubule_DKD_disease4"
    ),
    Comp2 = c("neg_tubule_DKD_disease4", "PanCK_tubule_DKD_disease4")
)
storage.mode(rawCounts) <- "integer"
sum(!is.finite(as.matrix(rawCounts)))
results_list_DESeq2 <- DGEADESeq2(
    rawCounts, metadata, grouping_columns, comparisons
)
results_list <- results_list_DESeq2$results
gene_list <- extract_genes_direction(
    results_list,
    only_up = TRUE, only_down = FALSE, only_sig = FALSE,
    padj = NULL, padj_cutoff = 0.05, lfc_cutoff = 0
)

extractDEGenes

Description

Extracts the results of the DEA with DESeq2

Usage

extractDEGenes(
  results_list,
  comparisons,
  only_up = FALSE,
  only_down = FALSE,
  up_down = FALSE,
  only_sig = FALSE,
  padj_cutoff = 0.05,
  lfc_cutoff = 0
)

Arguments

results_list

A list containing the results of the DEA.
comparisons

An element of the class list, that contains optional information on specific comparisons that are to be conducted (simplifies the results).
only_up

A Boolean opting for only up regulated genes.
only_down

A Boolean opting for only down regulated genes.
up_down

A Boolean opting for only up and down regulated genes.
only_sig

A Boolean opting for only significantly regulated genes.
padj_cutoff

A Float, setting the adjusted p-values cutoff.
lfc_cutoff

A Float, setting the fold change cutoff.

Value

A list containing the voom data, fitting of the linear model, results of the contrasts, and the generated design.

Examples

rawCounts <- read.csv(system.file(
    "extdata/RawDataExamplePackageNanostring.csv",
    package = "DgeaHeatmap"
))
rawCounts <- build_matrix(rawCounts, 1)
metadata <- readRDS(system.file(
    "extdata/MetaDataPackageNanostring.rds",
    package = "DgeaHeatmap"
))
grouping_columns <- c("segment", "region", "class", "slide_name")
comparisons <- list(
    Comp1 = c(
        "Geometric_Segment_glomerulus_DKD_disease3",
        "PanCK_tubule_DKD_disease4"
    ),
    Comp2 = c("neg_tubule_DKD_disease4", "PanCK_tubule_DKD_disease4")
)
storage.mode(rawCounts) <- "integer"
sum(!is.finite(as.matrix(rawCounts)))
results_list_DESeq2 <- DGEADESeq2(
    rawCounts, metadata, grouping_columns,
    comparisons
)
results_list <- results_list_DESeq2$results
down_genes <- extractDEGenes(results_list, comparisons, only_down = TRUE)

Function to filter a matrix to extract a chosen number of most variable rows through calculation of the variance.

Description

Function to filter a matrix to extract a chosen number of most variable rows through calculation of the variance.

Usage

filtering_for_top_exprGenes(counts_data, top_number_of_genes)

Arguments

counts_data

An input matrix from which the most variable rows are to be extracted.
top_number_of_genes

An integer to set the number of how many rows should be extracted from the original matrix by variance.

Value

A new matrix containing only the top_number_of_genes count of rows with the highest variance from input matrix.

Examples

x <- 1
top_number_of_genes <- 20
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
matrixCounts <- build_matrix(input_data, x)
counts_data <- matrixCounts
varGenesMatrix <- filtering_for_top_exprGenes(
  counts_data,
  top_number_of_genes
)

Creating a heatmap with annotation of x most variable rows(genes).

Description

Creating a heatmap with annotation of x most variable rows(genes).

Usage

function_complexHeatmap_var(
  topGenes_matrix,
  probes,
  number_of_annotations_per_cluster,
  k,
  Title,
  fontsize_rowAnnotation,
  fontsize_columnNames,
  fontsize_rowNames,
  title_heatmapLegend,
  WidthNum,
  HeightNum,
  UnitSize,
  color_Palette
)

Arguments

topGenes_matrix

An input matrix to create the heatmap.
probes

A list of strings to summarize biological replicated in the columns.
number_of_annotations_per_cluster

An integer used to set the number of annotations per cluster in the heatmap.
k

An integer used to set number of cluster in the heatmap.
Title

A string to set the title of the heatmap.
fontsize_rowAnnotation

An integer used to set the font size of the row annotation in the heatmap
fontsize_columnNames

An integer used to set the font size of the columns in the heatmap.
fontsize_rowNames

An integer used to set the font size of the rownames in the heatmap.
title_heatmapLegend

A string setting the title of the legend of the heatmap.
WidthNum

A float setting the width of the heatmap.
HeightNum

A float setting the height of the heatmap.
UnitSize

A string such as "cm" or "inch" to set the unit of HeightNum and WidthNum.
color_Palette

Name of the colorPalette used for the heatmap.

Value

A plotted heatmap of the input data.

Examples

x <- 1
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
matrixCounts <- build_matrix(input_data, x)
topGenes_matrix <- scale_counts(matrixCounts)
probes <- list("disease3", "disease4", "disease1B")
k <- 1
seed <- 1
fontsize_rowAnnotation <- 8
number_of_annotations_per_cluster <- 5
Title <- "Heatmap of Data"
fontsize_columnNames <- 6
fontsize_rowNames <- 4
title_heatmapLegend <- "Expression"
WidthNum <- 4.5
HeightNum <- 3
UnitSize <- "cm"
color_Palette <- "RdBu"
set.seed(seed)
function_complexHeatmap_var(
  topGenes_matrix, probes,
  number_of_annotations_per_cluster, k, Title, fontsize_rowAnnotation,
  fontsize_columnNames, fontsize_rowNames, title_heatmapLegend, WidthNum,
  HeightNum, UnitSize, color_Palette
)

Function to generating a raw read count table.

Description

Function to generating a raw read count table.

Usage

genRawReadCountTable(demoData)

Arguments

demoData

A NanostringGeoMxSet object, storing all expression, annotation, and probe information linked together.

Value

A dataframe containing only the raw read counts that have passed quality control.

Examples

demoData <- readRDS(
  system.file(
    "extdata",
    "miniGeoMx.rds",
    package = "DgeaHeatmap"
  )
)

demoData <- add_demoElem(demoData)

NanoStringNCTools::assayDataApply(
  demoData,
  MARGIN = 1,
  FUN = mean,
  elt = "demoElem"
)[seq_len(5)]

demoData <- split_data_by_column(
  demoData,
  vGroup = "aoi",
  vElt = "demoElem"
)
df_Exp <- genRawReadCountTable(demoData)

Calculates the distance matrix

Description

Calculates the distance matrix

Usage

get_dist(x, method)

Arguments

x

An input matrix to create the distance matrix.
method

Astring setting the distance method for clustering.

Value

A distance matrix.

Examples

input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
x <- 1
matrixCounts <- build_matrix(input_data, x)
matrix <- scale_counts(matrixCounts)
method <- "euclidean"
distance_matrix <- get_dist(matrix, method)

Creating a color scheme based on the available color palettes of RColorBrewer for the heatmap.

Description

Creating a color scheme based on the available color palettes of RColorBrewer for the heatmap.

Usage

get_heatmap_colors(colorPalette)

Arguments

colorPalette

An input string defining the color palette (available from RColorBrewer).

Value

The colors used in heatmap based on either ComplexHeatmap default or RColorBrewer.

Examples

colorPalette <- "RdBu"
get_heatmap_colors(colorPalette)

Creates a matrix only containing chosen columns of an original matrix with more data.

Description

Creates a matrix only containing chosen columns of an original matrix with more data.

Usage

individual_matrix(factors_for_matrix_devision, mmatrix)

Arguments

factors_for_matrix_devision

A list containing variables used to extract specific columns from the original matrix.
mmatrix

A matrix used as input, from which specifically chosen columns are extracted.

Value

A new matrix containing fewer columns than before, that have previously been specified.

Examples

factors_for_individual_matrix <- list("DKD", "glomerulus")
x <- 1
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
matrixCounts <- build_matrix(input_data, x)
indiMatrix <- individual_matrix(factors_for_individual_matrix, matrixCounts)

Generates K-means for the columns and rows of a matrix.

Description

Generates K-means for the columns and rows of a matrix.

Usage

Kmean_generation(m_top_genes_matrix, k)

Arguments

m_top_genes_matrix

An input matrix for which they K-Means are calculated next.
k

Number k of how many clusters are created.

Value

All calculated K-Means of the matrix.

Examples

x <- 1
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
matrixCounts <- build_matrix(input_data, x)
m_top_genes_matrix <- scale_counts(matrixCounts)
seed <- 1
k <- 1
set.seed(seed)
k_means <- Kmean_generation(m_top_genes_matrix, k)

Function to determine the most variable genes of each cluster to enable annotation..

Description

Function to determine the most variable genes of each cluster to enable annotation..

Usage

most_variable_genes(m_kmeans, number_of_annotations_per_cluster, k)

Arguments

m_kmeans

Matrix of the k-Means for each row in a gene set.
number_of_annotations_per_cluster

Number of wanted annotations per cluster.
k

Number of clusters.

Value

A list of the most variable rows (genes) of each cluster.

Examples

x <- 1
number_of_annotations_per_cluster <- 5
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
matrixCounts <- build_matrix(input_data, x)
m_top_genes_matrix <- scale_counts(matrixCounts)
seed <- 1
k <- 1
set.seed(seed)
m_kmeans <- Kmean_generation(m_top_genes_matrix, k)
top_x_variable_genes <- most_variable_genes(
  m_kmeans,
  number_of_annotations_per_cluster, k
)

Create pairwise contrasts

Description

Create pairwise contrasts

Usage

pairwise_contrasts(comparisons, comp_factor, prefix = "Contrast")

Arguments

comparisons

An element of the class list, that contains optional information on specific comparisons that are to be conducted (simplifies the results).
comp_factor

A factor, for fallback all pairwise comparison.
prefix

A string used as a prefix for the contrast_names that are generated.

Value

A character vector of contrast names.

Examples

rawCounts <- read.csv(system.file(
    "extdata/RawDataExamplePackageNanostring.csv",
    package = "DgeaHeatmap"
))
rawCounts <- build_matrix(rawCounts, 1)
metadata <- readRDS(system.file(
    "extdata/MetaDataPackageNanostring.rds",
    package = "DgeaHeatmap"
))
grouping_columns <- c("segment", "region", "class", "slide_name")
comparisons <- list(
    Comp1 = c(
        "Geometric_Segment_glomerulus_DKD_disease3",
        "PanCK_tubule_DKD_disease4"
    ),
    Comp2 = c("neg_tubule_DKD_disease4", "PanCK_tubule_DKD_disease4")
)
comp <- apply(
    metadata[, grouping_columns, drop = FALSE], 1, paste,
    collapse = "_"
)
comp <- gsub(" ", "_", comp) # Replace spaces with underscores
metadata$comp <- make.names(comp) # Clean names for use in model.matrix
comp_factor <- factor(metadata$comp)
paired_contrasts <- pairwise_contrasts(comparisons, comp_factor)

Function to perform k-Means clustering for a matrix and setting split to split a heatmap into clusters.

Description

Function to perform k-Means clustering for a matrix and setting split to split a heatmap into clusters.

Usage

performing_kMeans(m_top_genes_matrix, k)

Arguments

m_top_genes_matrix

A matrix for which K-Means are calculated.
k

Number of clusters for k-Means clustering (chosen by elbow-plot).

Value

A new dataframe containing information assigning each rows of the input matrix to a cluster.

Examples

x <- 1
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
matrixCounts <- build_matrix(input_data, x)
m_top_genes_matrix <- scale_counts(matrixCounts)
k <- 1
split <- performing_kMeans(m_top_genes_matrix, k)

DGEAedgeR

Description

Creates a contrast matrix for edgeR

Usage

prepare_dge_list(rawCounts, metadata)

Arguments

rawCounts

A matrix containing raw counts (integers).
metadata

A dataframe containing the meta data information of the raw counts matrix, with a column containing the comparison factors.

Value

A DGEList object.

Examples

rawCounts <- read.csv(system.file(
    "extdata/RawDataExamplePackageNanostring.csv",
    package = "DgeaHeatmap"
))
rawCounts <- build_matrix(rawCounts, 1)
metadata <- readRDS(system.file(
    "extdata/MetaDataPackageNanostring.rds",
    package = "DgeaHeatmap"
))
y <- prepare_dge_list(rawCounts, metadata)

Function to build a heatmap using other functions.

Description

Function to build a heatmap using other functions.

Usage

print_heatmap(
  m_top_genes_matrix,
  title,
  split,
  anno,
  fontsize_columnNames,
  fontsize_rowNames,
  title_heatmapLegend,
  WidthNum,
  HeightNum,
  UnitSize,
  color_Palette
)

Arguments

m_top_genes_matrix

An input matrix for which the heatmap is created.
title

A string used to set the title of the heatmap.
split

A dataframe containing information about the clustering of the rows of the input matrix.
anno

A numeric index containing row informations for annotation.
fontsize_columnNames

An integer used to set the font size of the columns in the heatmap.
fontsize_rowNames

An integer used to set the font size of the rownames in the heatmap.
title_heatmapLegend

A string setting the title of the legend of the heatmap.
WidthNum

A float setting the width of the heatmap.
HeightNum

A float setting the height of the heatmap.
UnitSize

A string such as "cm" or "inch" to set the unit of HeightNum and WidthNum.
color_Palette

Name of the colorPalette used for the heatmap.

Value

A plotted heatmap.

Examples

x <- 1
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
matrixCounts <- build_matrix(input_data, x)
m_top_genes_matrix <- scale_counts(matrixCounts)
k <- 1
split <- performing_kMeans(m_top_genes_matrix, k)
seed <- 1
fontsize_rowAnnotation <- 8
set.seed(seed)
m_kmeans <- Kmean_generation(m_top_genes_matrix, k)
number_of_annotations_per_cluster <- 5
top_x_genes_cluster <- most_variable_genes(
  m_kmeans,
  number_of_annotations_per_cluster, k
)
anno <- set_annotation(
  m_top_genes_matrix, top_x_genes_cluster,
  fontsize_rowAnnotation
)
title <- "Heatmap of Data"
fontsize_columnNames <- 6
fontsize_rowNames <- 4
title_heatmapLegend <- "Expression"
WidthNum <- 4.5
HeightNum <- 3
UnitSize <- "cm"
color_Palette <- "RdBu"
set.seed(seed)
print_heatmap(
  m_top_genes_matrix, title, split, anno, fontsize_columnNames,
  fontsize_rowNames, title_heatmapLegend, WidthNum, HeightNum,
  UnitSize, color_Palette
)

Setting the row clustering

Description

Setting the row clustering

Usage

row_clustering(
  ncounts_matrix,
  cluster_rows = TRUE,
  cluster_method = "hierarchical",
  distance_method = "euclidean",
  k_row = NULL,
  row_split = NULL,
  row_dend = TRUE
)

Arguments

ncounts_matrix

An input matrix to create the clustering.
cluster_rows

A Boolean switching the optional clustering of the rows on and off, default <- TRUE.
cluster_method

A string setting the cluster method for the heatmap.
distance_method

A string setting the distance method for clustering.
k_row

An integer used to set number of clusters for row clustering.
row_split

An integer seeting the column clustering by kmeans.
row_dend

A Boolean switching column clustering by hierarchical clustering on (default).

Value

row_split where the rows are split into clusters

Examples

x <- 1
seed <- 1
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
matrixCounts <- build_matrix(input_data, x)
ncounts_matrix <- scale_counts(matrixCounts)
set.seed
row_split <- NULL
row_dend <- TRUE
rows_clustered <- row_clustering(ncounts_matrix,
  cluster_rows = TRUE,
  cluster_method = "hierarchical", distance_method = "euclidean",
  k_row = NULL, row_split = row_split, row_dend = row_dend
)

Function to Z-count scale the values of a matrix.

Description

Function to Z-count scale the values of a matrix.

Usage

scale_counts(countsmatrix)

Arguments

countsmatrix

An input matrix whose values are scaled by Z-count.

Value

A new matrix with Z-count scaled values.

Examples

x <- 1
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
countsmatrix <- build_matrix(input_data, x)
scaled_counts <- scale_counts(countsmatrix)

Function to set row annotation for a heatmap.

Description

Function to set row annotation for a heatmap.

Usage

set_annotation(m_top_genes_matrix, top_x_genes_cluster, fontsize_rowAnnotation)

Arguments

m_top_genes_matrix

An input matrix to used for heatmap generation and choosing annotation for a heatmap.
top_x_genes_cluster

A list of the most variable x genes of each cluster in a heatmap.
fontsize_rowAnnotation

An integer defining the font size of the row annotation

Value

A numeric index from the orginal matrix.

Examples

x <- 1
number_of_annotations_per_cluster <- 5
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
matrixCounts <- build_matrix(input_data, x)
m_top_genes_matrix <- scale_counts(matrixCounts)
seed <- 1
k <- 1
fontsize_rowAnnotation <- 8
set.seed(seed)
m_kmeans <- Kmean_generation(m_top_genes_matrix, k)
top_x_genes_cluster <- most_variable_genes(
  m_kmeans,
  number_of_annotations_per_cluster, k
)
anno <- set_annotation(
  m_top_genes_matrix, top_x_genes_cluster,
  fontsize_rowAnnotation
)

Setting the row annotation

Description

Setting the row annotation

Usage

set_row_annotation(
  ncounts_matrix,
  k_row = NULL,
  row_annotation = FALSE,
  row_annotation_method = "auto",
  row_anno_names = NULL,
  row_anno_number = 5,
  fontsize_rowAnnotation = 10
)

Arguments

ncounts_matrix

An input matrix to create the heatmap.
k_row

An integer used to set number of clusters for row clustering in the heatmap, default <- NULL.
row_annotation

A Boolean switching row annotation on the heatmap on and off, default <- FALSE.
row_annotation_method

A string setting the annotation method of the heatmap, default <- "auto".
row_anno_names

A list containing choosen rownames to use for the row annotation, default <- NULL.
row_anno_number

An integer setting the number of automatic annotations assigned per cluster, default <- 5.
fontsize_rowAnnotation

An integer setting the font size of the group annotation labels in the legend, default <- 8.

Value

Numeric index from matrix for the row annotation

Examples

x <- 1
seed <- 1
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
matrixCounts <- build_matrix(input_data, x)
ncounts_matrix <- scale_counts(matrixCounts)
set.seed(seed)
annotation_for_rows <- set_row_annotation(ncounts_matrix)

Setting the sample annotation

Description

Setting the sample annotation

Usage

set_sample_annotation(
  sample_metadata = NULL,
  annotation_colors = NULL,
  annotation_name_side = "right",
  fontsize_group_annotation = 8,
  fontsize_group_annotation_legend = 10,
  fontsize_group_annotation_labels = 8
)

Arguments

sample_metadata

A dataframe containing the metadata information for the grouping of the columns, default <- NULL.
annotation_colors

A list assigning choosen colors to the corresponding groups, default <- NULL.
annotation_name_side

The side of the column annotation description, default <- "right".
fontsize_group_annotation

An integer setting the font size of the group annotation title, default <- 8.
fontsize_group_annotation_legend

An integer setting the font size of the group annotation legend name, default <- 10.
fontsize_group_annotation_labels

An integer setting the font size of the group annotation labels in the legend, default <- 8.

Value

A "HeamapAnnotation" object if "sample_metadata" is provided, otherwise NULL.

Examples

x <- 1
seed <- 1
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
matrixCounts <- build_matrix(input_data, x)
ncounts_matrix <- scale_counts(matrixCounts)
set.seed
groups <- c(
  "3_DKD_glomerulus_Geometric_S", "1B_DKD_glomerulus_Geometric_S",
  "2B_DKD_glomerulus_WT"
)
sample_names <- c(colnames(ncounts_matrix))
group_assignment <- sapply(sample_names, function(sample) {
  matched <- groups[sapply(groups, function(g) grepl(g, sample))]
  if (length(matched) > 0) matched[1] else NA
})
stopifnot(length(sample_names) == length(group_assignment))
sample_metadata <- data.frame(
  Group = group_assignment,
  row.names = sample_names
)
all(colnames(ncounts_matrix) == rownames(sample_metadata))
group_colors <- list(Group = c(
  "3_DKD_glomerulus_Geometric_S" = "#1b9e77",
  "1B_DKD_glomerulus_Geometric_S" = "#7570b3",
  "2B_DKD_glomerulus_WT" = "#e7298a"
))
col_ha <- set_sample_annotation(
  sample_metadata = sample_metadata,
  annotation_colors = group_colors
)

Function to visualize the data distribution within the data of a matrix.

Description

Function to visualize the data distribution within the data of a matrix.

Usage

show_data_distribution(scaled_counts)

Arguments

scaled_counts

An input matrix with Z-count scaled values.

Value

A plot visualizing the data distribution of Z-count scaled data.

Examples

x <- 1
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
matrixCounts <- build_matrix(input_data, x)
scaled_counts <- scale_counts(matrixCounts)
show_data_distribution(scaled_counts)

Splitting data by group column with feature, pheno or protocol data to then get the mean.

Description

Splitting data by group column with feature, pheno or protocol data to then get the mean.

Usage

split_data_by_column(demoData, vGroup, vElt)

Arguments

demoData

A NanostringGeoMxSet object, storing all expression, annotation, and probe information linked together.
vGroup

Group of features in column.
vElt

Character string of the expression matrix name.

Value

A NanostringGeoMxSet object.

Examples

demoData <- readRDS(
  system.file(
    "extdata",
    "miniGeoMx.rds",
    package = "DgeaHeatmap"
  )
)

demoData <- add_demoElem(demoData)

NanoStringNCTools::assayDataApply(
  demoData,
  MARGIN = 1,
  FUN = mean,
  elt = "demoElem"
)[seq_len(5)]

demoData <- split_data_by_column(
  demoData,
  vGroup = "aoi",
  vElt = "demoElem"
)

Summarizes columns biological replicates of a matrix into one.

Description

Summarizes columns biological replicates of a matrix into one.

Usage

summarise_bio_replicates(top_genes_matrix, probes)

Arguments

top_genes_matrix

An input matrix with columns representing biological replicates.
probes

List of strings by which columns of biological replicates can be identified.

Value

A new matrix where biological replicated are summarized as their mean.

Examples

x <- 1
input_data <- read.csv(system.file("extdata/testfile_counts.csv",
  package = "DgeaHeatmap"
))
top_genes_matrix <- build_matrix(input_data, x)
probes <- list("disease3", "disease4", "disease1B")
newMatrix <- summarise_bio_replicates(top_genes_matrix, probes)

Summarize the results of the DEA with edgeR

Description

Summarize the results of the DEA with edgeR

Usage

summarize_edgeR_DEA(
  results_edgeR,
  lfc_threshold = 1,
  fdr_threshold = 0.05,
  return_significant_genes = TRUE
)

Arguments

results_edgeR

A list containing the results of the DEA.
lfc_threshold

A Float, setting threshold value for the fold change.
fdr_threshold

A Float, setting the False Discovery Rate threshold.
return_significant_genes

A Boolean opting for the output of significantly regulated genes.

Value

A list containing the voom data, fitting of the linear model, results of the contrasts, and the generated design.

Examples

results_edgeR <- readRDS(
    system.file(
        "extdata",
        "mini_edgeR_results.rds",
        package = "DgeaHeatmap"
    )
)

summarize_edgeR_DEA(results_edgeR)