Package 'RGenEDA' reference manual

Title:	Genomic Exploratory Data Analysis
Description:	Provides a unified and reproducible framework for performing genomic exploratory data analysis (EDA). Highly repetitive functions and analyses have been collated into an easy-to-use S4 object that produces quick results and customizable visualizations to assess variation in your data. See <https://github.com/mikemartinez99/RGenEDA>.
Authors:	Michael Martinez [aut, cre]
Maintainer:	Michael Martinez <[email protected]>
License:	GPL-3
Version:	0.99.0
Built:	2026-10-30 02:15:07 UTC
Source:	https://github.com/BiocStaging/RGenEDA

Convert a SummarizedExperiment to a geneda object

Description

Builds a geneda object from a SummarizedExperiment, including DESeqDataSet objects from DESeq2 and DESeqTransform objects returned by DESeq2::vst() or DESeq2::rlog(). Raw counts, normalized values, and sample metadata (colData) are carried over, so the object is ready for FindVariableFeatures(), RunPCA(), and the plotting functions.

Usage

as.geneda(x, ...)

## S4 method for signature 'SummarizedExperiment'
as.geneda(
  x,
  normalized = NULL,
  counts = "counts",
  transform = c("vst", "logcpm"),
  DEGs = NULL,
  ...
)
as.geneda(x, ...)

## S4 method for signature 'SummarizedExperiment'
as.geneda(
  x,
  normalized = NULL,
  counts = "counts",
  transform = c("vst", "logcpm"),
  DEGs = NULL,
  ...
)

Arguments

x

A SummarizedExperiment (or subclass such as DESeqDataSet or DESeqTransform) with features as rows and samples as columns.

...

Not used.

normalized

Where the normalized values come from. One of:

NULL (default): computed from the counts assay using transform. If x is a DESeqTransform, its transformed values are used instead.
A single assay name or index in x.
A SummarizedExperiment with the same features and samples as x, for example the output of DESeq2::vst(x). Its first assay is used.
A matrix with the same row and column names as x.

counts

Name or index of the raw counts assay in x, or NULL to leave the counts slot empty. Default "counts". If the assay is not present, the counts slot is left empty, unless counts are needed to compute normalized.

transform

Normalization used when normalized = NULL: "vst" (variance stabilizing transformation from DESeq2, blind = TRUE) or "logcpm" (log2 counts per million with a pseudocount of 1). Default "vst", which requires the DESeq2 package and integer counts.

DEGs

Optional named list of DEG tables (data.frame or DESeqResults) to store with SetDEGs(), using the list names as assay names.

Details

Sample metadata are taken from colData(x) and converted to a data.frame. Columns added by DESeq2, such as sizeFactor, are kept and can be tested with PlotEigenCorr() like any other covariate.

Assays stored as sparse or delayed matrices are converted to base matrices with as.matrix().

Value

A geneda object.

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample

se <- SummarizedExperiment::SummarizedExperiment(
    assays = list(counts = counts),
    colData = meta
)
obj <- as.geneda(se, transform = "logcpm")
obj

# From a DESeqDataSet, with VST-normalized values and DEGs
if (requireNamespace("DESeq2", quietly = TRUE)) {
    meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
    dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
    dds <- DESeq2::DESeq(dds, quiet = TRUE)
    obj <- as.geneda(dds,
        normalized = DESeq2::vst(dds),
        DEGs = list(KO_vs_WT = DESeq2::results(dds))
    )
    obj
}
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample

se <- SummarizedExperiment::SummarizedExperiment(
    assays = list(counts = counts),
    colData = meta
)
obj <- as.geneda(se, transform = "logcpm")
obj

# From a DESeqDataSet, with VST-normalized values and DEGs
if (requireNamespace("DESeq2", quietly = TRUE)) {
    meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
    dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
    dds <- DESeq2::DESeq(dds, quiet = TRUE)
    obj <- as.geneda(dds,
        normalized = DESeq2::vst(dds),
        DEGs = list(KO_vs_WT = DESeq2::results(dds))
    )
    obj
}

DEDashboard

Description

Generate a multi-panel differential expression dashboard for a single assay in a geneda object. The dashboard includes:

P-value histogram
MA plot
Volcano plot

Usage

DEDashboard(object, assay, alpha = 0.05, l2fc = 1, num = NULL, den = NULL)
DEDashboard(object, assay, alpha = 0.05, l2fc = 1, num = NULL, den = NULL)

Arguments

object

A geneda object containing DEGs in the DEGs slot.

assay

Character. Name of the assay to visualize.

alpha

Numeric. Significance threshold for highlighting p-values and DEGs (default 0.05).

l2fc

Numeric. Log2 fold-change threshold for MA and volcano plots.

num

Character. Name of the numerator group for volcano plot labeling. If NULL (default), the legend uses "numerator".

den

Character. Name of the denominator group for volcano plot labeling. If NULL (default), the legend uses "denominator".

Value

A patchwork object combining plots.

Examples


data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
db <- DEDashboard(obj,
    assay = "KO_vs_WT", alpha = 0.05, l2fc = 1,
    num = "Snai1_KO", den = "WT"
)
# The dashboard needs a large device, e.g.
# ggplot2::ggsave("dashboard.png", db, width = 14, height = 10)
class(db)

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
db <- DEDashboard(obj,
    assay = "KO_vs_WT", alpha = 0.05, l2fc = 1,
    num = "Snai1_KO", den = "WT"
)
# The dashboard needs a large device, e.g.
# ggplot2::ggsave("dashboard.png", db, width = 14, height = 10)
class(db)

Access DEGs container

Description

Access DEGs container

Usage

DEGs(object, assay)
DEGs(object, assay)

Arguments

object

A geneda object

assay

The name of the DEGs slot to return

Value

The DEG data.frame stored under assay, or NULL if it is not set

Examples


data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
head(DEGs(obj, "KO_vs_WT"))

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
head(DEGs(obj, "KO_vs_WT"))

Access dimensional reduction results list

Description

Access dimensional reduction results list

Usage

DimReduction(object)
DimReduction(object)

Arguments

object

A geneda object

Value

List with Loadings, Eigenvectors, percent_var

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
obj <- RunPCA(obj)
pca <- DimReduction(obj)
names(pca)
head(pca$Scores[, 1:3])
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
obj <- RunPCA(obj)
pca <- DimReduction(obj)
names(pca)
head(pca$Scores[, 1:3])

extractEigen

Description

Extract gene-level Eigen-vectors for PCs of interest and calculate percent variance.

Usage

extractEigen(object, component)
extractEigen(object, component)

Arguments

object

A geneda object containing PCA results in the DimReduction slot

component

Principal component to use (i.e., PC1)

Value

A data-frame containing gene loading information and percent variation per gene

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
obj <- RunPCA(obj)
pc1 <- extractEigen(obj, component = "PC1")
head(pc1[order(-abs(pc1$EigenVector)), ])
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
obj <- RunPCA(obj)
pc1 <- extractEigen(obj, component = "PC1")
head(pc1[order(-abs(pc1$EigenVector)), ])

Extract PCA Loadings and Metadata

Description

This function extracts PCA loadings stored in the DimReduction slot of a geneda object and combines them with the associated metadata. It ensures that the metadata has valid rownames and aligns the PCA loadings accordingly.

Usage

ExtractPCA(object)
ExtractPCA(object)

Arguments

object

A geneda object containing PCA results in the DimReduction slot and sample-level metadata in the metadata slot.

Details

The function performs several checks:

Ensures the input object is of class geneda.
Verifies that the DimReduction slot contains PCA loadings.
Confirms that the metadata has valid rownames.
Reorders the PCA loadings to match the order of metadata rows.

If metadata rownames are missing or invalid, the function throws an error.

Value

A data.frame combining PCA loadings and sample metadata, where rows correspond to samples and columns include principal component loadings and metadata fields.

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
obj <- RunPCA(obj)
pca_df <- ExtractPCA(obj)
head(pca_df[, c("PC1", "PC2", "Genotype")])
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
obj <- RunPCA(obj)
pca_df <- ExtractPCA(obj)
head(pca_df[, c("PC1", "PC2", "Genotype")])

Filter DEGs by padj and absolute log2FoldChange

Description

Filters the unfiltered DEGs in DEGs[[assay]] and stores the filtered results in a new named slot DEGs[[saveAssay]]. Multiple filtered result sets can be stored with different assay names.

Usage

FilterDEGs(object, assay, alpha = 0.05, l2fc = 1, saveAssay)
FilterDEGs(object, assay, alpha = 0.05, l2fc = 1, saveAssay)

Arguments

object

A geneda object with a DEG table set via SetDEGs()

assay

The DEG slot to filter

alpha

Adjusted p-value threshold (<=)

l2fc

Absolute log2 fold change threshold (>=)

saveAssay

Character name for the filtered result set (e.g., "padj05_lfc1")

Value

Updated geneda object with filtered results stored in DEGs[[saveAssay]]

Examples


data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
obj <- FilterDEGs(obj,
    assay = "KO_vs_WT", alpha = 0.05, l2fc = 1,
    saveAssay = "KO_vs_WT_filtered"
)
nrow(DEGs(obj, "KO_vs_WT_filtered"))

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
obj <- FilterDEGs(obj,
    assay = "KO_vs_WT", alpha = 0.05, l2fc = 1,
    saveAssay = "KO_vs_WT_filtered"
)
nrow(DEGs(obj, "KO_vs_WT_filtered"))

Find DEGs Overlapping With HVGs

Description

Looks for DEGs after filtering that overlap with HVGs.

Usage

FindHVDEGs(object, assay, direction = c("positive", "negative", "both"))
FindHVDEGs(object, assay, direction = c("positive", "negative", "both"))

Arguments

object

A geneda object with a DEG table set via SetDEGs()

assay

The DEG slot to pull results from (typically a result of FilterDEGs)

direction

One of positive, negative, or both (indicating the direction of DEG fold change to intersect with HVGs)

Value

For direction = "positive" or "negative", a character vector of gene IDs that are both DEGs in that direction and HVGs. For direction = "both", a list with elements positive, negative, and both (all overlapping genes).

Examples


data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
obj <- FilterDEGs(obj, assay = "KO_vs_WT", saveAssay = "KO_vs_WT_filtered")
obj <- FindVariableFeatures(obj, nfeatures = 1000)
hv <- FindHVDEGs(obj, assay = "KO_vs_WT_filtered", direction = "both")
lengths(hv)

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
obj <- FilterDEGs(obj, assay = "KO_vs_WT", saveAssay = "KO_vs_WT_filtered")
obj <- FindVariableFeatures(obj, nfeatures = 1000)
hv <- FindHVDEGs(obj, assay = "KO_vs_WT_filtered", direction = "both")
lengths(hv)

Find and store highly variable genes (HVGs)

Description

Computes per-feature variance on normalized(object), ranks genes by decreasing variance, and stores the top nfeatures gene IDs into the HVGs slot. Returns the updated object.

Usage

FindVariableFeatures(object, nfeatures)
FindVariableFeatures(object, nfeatures)

Arguments

object

A geneda object

nfeatures

Number of HVGs to store

Value

Updated geneda object

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
length(HVGs(obj))
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
length(HVGs(obj))

Construct a geneda object

Description

Construct a geneda object

Usage

GenEDA(normalized, metadata, counts = NULL, DEGs = NULL)
GenEDA(normalized, metadata, counts = NULL, DEGs = NULL)

Arguments

normalized

Normalized expression matrix (features x samples).

metadata

Sample metadata data.frame with row names matching colnames(normalized).

counts

Optional counts matrix (features x samples).

DEGs

Optional named list of DEG tables (data.frame or DESeqResults), stored with SetDEGs() using the list names as assay names. Each table needs log2FoldChange and padj columns.

Details

This constructor does not compute HVGs or PCA. Use FindVariableFeatures() and RunPCA() afterward.

Value

A geneda object.

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj

geneda S4 class

Description

An S4 container for exploratory genomic data analysis. Stores optional raw counts, normalized data, sample metadata, highly variable genes (HVGs), and PCA dimensionality reduction results.

Slots

counts: Optional counts matrix (features x samples). Can be NULL.
normalized: Normalized expression matrix (features x samples).
metadata: Sample-level metadata data.frame (rows = samples).
HVGs: Character vector of selected highly variable gene IDs (row names).
DimReduction: List for PCA results with Scores, Eigenvectors, percent_var.
DEGs: List container for differential expression results.

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
is(obj, "geneda")
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
is(obj, "geneda")

generatePCs

Description

Generate principal component analysis data that can be used in downstream analyses.

Usage

generatePCs(mat, vars, nFeatures)
generatePCs(mat, vars, nFeatures)

Arguments

mat

A data matrix where rows are features and columns are samples

vars

A vector of gene variances (can calculate using RGenEDA::plotVariance) or gene names

nFeatures

Number of top features to generate principal components on.

Value

A list consisting of 3 slots: Loadings, Eigenvectors, and percent_var

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
vars <- apply(norm, 1, var)
pcs <- generatePCs(norm, vars, nFeatures = 1000)
pcs$percent_var
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
vars <- apply(norm, 1, var)
pcs <- generatePCs(norm, vars, nFeatures = 1000)
pcs$percent_var

Save a pheatmap Objects generated from RGenEDA to File

Description

Saves a pheatmap derived RGenEDA heatmap object to a file in various formats including PNG, JPEG, TIFF, BMP, PDF, and SVG.

Usage

GenSave(
  pheatmap_obj,
  filename,
  width = 8,
  height = 6,
  units = "in",
  res = 300,
  ...
)
GenSave(
  pheatmap_obj,
  filename,
  width = 8,
  height = 6,
  units = "in",
  res = 300,
  ...
)

Arguments

pheatmap_obj

A pheatmap object returned by an RGenEDA or pheatmap function.

filename

Character string specifying the path and filename where the heatmap should be saved.

width

Numeric, the width of the output image. Default is 8.

height

Numeric, the height of the output image. Default is 6.

units

Character, units for width and height: one of "in", "cm", "mm", or "px". Default is "in". Values are converted to inches for PDF and SVG output.

res

Numeric, the resolution (in dpi) for raster images. Default is 300.

...

Additional arguments passed to the graphics device function.

Details

This function is similar in spirit to ggsave() but works specifically with pheatmap objects.

Value

Invisibly returns the filename of the saved heatmap.

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
dists <- PlotDistances(obj, meta_cols = "Genotype")
out <- tempfile(fileext = ".png")
GenSave(dists, out, width = 6, height = 5)
file.exists(out)
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
dists <- PlotDistances(obj, meta_cols = "Genotype")
out <- tempfile(fileext = ".png")
GenSave(dists, out, width = 6, height = 5)
file.exists(out)

Access counts matrix (avoid name clash with DESeq2::counts)

Description

Access counts matrix (avoid name clash with DESeq2::counts)

Usage

getCounts(object)
getCounts(object)

Arguments

object

A geneda object

Value

Matrix or NULL

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
getCounts(obj)[1:5, ]
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
getCounts(obj)[1:5, ]

Access metadata (avoid name clash with S4 generics)

Description

Access metadata (avoid name clash with S4 generics)

Usage

getMetadata(object)
getMetadata(object)

Arguments

object

A geneda object

Value

data.frame

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
getMetadata(obj)
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
getMetadata(obj)

Access normalized matrix (avoid name clash with generics)

Description

Access normalized matrix (avoid name clash with generics)

Usage

getNormalized(object)
getNormalized(object)

Arguments

object

A geneda object

Value

Matrix

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
getNormalized(obj)[1:5, ]
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
getNormalized(obj)[1:5, ]

Access HVG IDs

Description

Access HVG IDs

Usage

HVGs(object)
HVGs(object)

Arguments

object

A geneda object

Value

Character vector

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 500)
head(HVGs(obj))
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 500)
head(HVGs(obj))

PlotCountDist

Description

Generates violin plots with embedded boxplots showing the distribution of normalized or VST-transformed RNA-Seq counts for each sample in a geneda object. Optionally, plots can be faceted by a metadata variable.

Usage

PlotCountDist(object, split_by = NULL)
PlotCountDist(object, split_by = NULL)

Arguments

object

A geneda object containing normalized expression values in ⁠@normalized⁠ and sample metadata in ⁠@metadata⁠.

split_by

Character. Optional column name from object@metadata used for faceting (default: NULL, no faceting).

Value

A ggplot2 object displaying the distribution of counts per sample.

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
PlotCountDist(obj)
PlotCountDist(obj, split_by = "Genotype")
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
PlotCountDist(obj)
PlotCountDist(obj, split_by = "Genotype")

Plot a Euclidean Distance Heatmap

Description

Compute a sample-by-sample distance matrix from a geneda object and plot a ggplot2 heatmap. Optionally subset metadata columns and reorder rows/columns by hierarchical clustering.

Usage

PlotDistances(
  object,
  method = "euclidean",
  reorder = TRUE,
  meta_cols = NULL,
  palettes = NULL,
  return = c("object", "plot")
)
PlotDistances(
  object,
  method = "euclidean",
  reorder = TRUE,
  meta_cols = NULL,
  palettes = NULL,
  return = c("object", "plot")
)

Arguments

object

A geneda object

method

Distance method for stats::dist (default "euclidean")

reorder

Logical; if TRUE, reorder rows/cols by hclust on the distance matrix. Default TRUE

meta_cols

Optional character vector of metadata columns to display in the title/subtitle for quick reference (no annotation strips are drawn).

palettes

Optional named list of color vectors that can be used downstream (returned) if the caller wants to annotate elsewhere.

return

One of c("object", "plot"). If "plot", draws the heatmap immediately via grid and still returns the result list. Default "object".

Value

A list with elements: dist_matrix, order (character vector of sample names), heatmap (pheatmap object), palettes (as passed through)

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
colorList <- list(Genotype = c(WT = "grey40", Snai1_KO = "firebrick3"))
dists <- PlotDistances(obj,
    meta_cols = "Genotype", palettes = colorList,
    return = "plot"
)
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
colorList <- list(Genotype = c(WT = "grey40", Snai1_KO = "firebrick3"))
dists <- PlotDistances(obj,
    meta_cols = "Genotype", palettes = colorList,
    return = "plot"
)

Plot Eigen-Correlation With Metadata Heatmap

Description

Calculate Eigen-correlations from a geneda object and plot a publication-quality heatmap using ggplot2 with numeric labels and significance stars. For continuous variables, Pearson correlation is used. P values follow this convention: p < 0.001, p < 0.01, p < 0.05 = three, two, one stars, respectively.

Two-level categorical variables are coded by factor level order, so the sign of the correlation follows the level order. Categorical variables with more than two levels are summarized by the multiple correlation R (the square root of R^2 from a one-way linear model, score ~ variable). R is unsigned (0 to 1) and its p-value comes from the model F-test, so results do not depend on how levels are labelled. Variables that cannot be tested (a single level, constant values, or no residual degrees of freedom, e.g. a sample ID column) are dropped.

Requires PCA scores in DimReduction(object); does not recompute PCA.

Usage

PlotEigenCorr(object, num_pcs = 10, meta_cols = NULL)
PlotEigenCorr(object, num_pcs = 10, meta_cols = NULL)

Arguments

object

A geneda object containing normalized and metadata, and optionally DimReduction scores.

num_pcs

Number of principal components to correlate.

meta_cols

Optional character vector of metadata column names to include. Defaults to all metadata columns.

Value

A list with elements: cor_matrix, pval_matrix, stars, plot (ggplot)

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
obj <- RunPCA(obj)
ec <- PlotEigenCorr(obj, num_pcs = 5, meta_cols = "Genotype")
ec$plot
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
obj <- RunPCA(obj)
ec <- PlotEigenCorr(obj, num_pcs = 5, meta_cols = "Genotype")
ec$plot

PlotEigenHeatmap

Description

Generates a pheatmap of the top genes contributing to a specific principal component from a geneda object. Genes are selected by absolute eigenvector loading for the chosen PC, and expression values are Z-score scaled per gene. Optionally annotate columns with metadata variables and add row annotations showing gene-level percent variance.

Usage

PlotEigenHeatmap(
  object,
  pc = "PC1",
  n = 25,
  annotate_by = NULL,
  annotate_colors = NULL,
  return = c("object", "plot")
)
PlotEigenHeatmap(
  object,
  pc = "PC1",
  n = 25,
  annotate_by = NULL,
  annotate_colors = NULL,
  return = c("object", "plot")
)

Arguments

object

A geneda object containing normalized expression data and PCA in DimReduction.

pc

Character. Principal component to visualize (e.g., "PC1").

n

Integer. Number of genes to select by absolute loading (default: 25).

annotate_by

Character vector of metadata column names for column annotations (optional).

annotate_colors

Named list of color vectors for metadata columns. Names should match annotate_by.

return

One of c("object","plot"). If "plot", draws the heatmap via grid.

Value

A list with topGenes (data.frame with EigenVector and PctVar), expression (scaled matrix), and heatmap (pheatmap object)

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
obj <- RunPCA(obj)
res <- PlotEigenHeatmap(obj,
    pc = "PC1", n = 25,
    annotate_by = "Genotype",
    annotate_colors = list(Genotype = c(
        WT = "grey40", Snai1_KO =
            "firebrick3"
    )),
    return = "plot"
)
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
obj <- RunPCA(obj)
res <- PlotEigenHeatmap(obj,
    pc = "PC1", n = 25,
    annotate_by = "Genotype",
    annotate_colors = list(Genotype = c(
        WT = "grey40", Snai1_KO =
            "firebrick3"
    )),
    return = "plot"
)

PlotHVGVariance

Description

Plot variance for all genes (features) on a geneda object using ggplot2.

Usage

PlotHVGVariance(object, transform = NULL, dropTopN = 0)
PlotHVGVariance(object, transform = NULL, dropTopN = 0)

Arguments

object

A geneda object created by GenEDA().

transform

One of c("log") to log-transform variance, or NULL for none. Default = NULL

dropTopN

Number of top-most variable genes to drop prior to plotting. Default = 0

Value

A ggplot2 object visualizing the variance of HVGs

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
PlotHVGVariance(obj, transform = "log")
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
PlotHVGVariance(obj, transform = "log")

PlotMA

Description

Plot MA plot from a geneda object. Requires that you have ran SetDEGs on your object following differential expression in DESeq2.

Usage

PlotMA(object, assay, alpha, l2fc, title = NULL)
PlotMA(object, assay, alpha, l2fc, title = NULL)

Arguments

object

A geneda object containing DEGs from SetDEGs method

assay

The DEG slot to use for visualization

alpha

Threshold for adjusted p-values (padj column from DESeq2)

l2fc

Absolute value log2Fold-change magnitude threshold (log2FoldChange column)

title

Optional character vector of what plot should be titled.

Details

Thresholds are inclusive (padj <= alpha, ⁠|log2FoldChange| >= l2fc⁠), matching FilterDEGs().

Value

A ggplot2 object

Examples


data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
PlotMA(obj, assay = "KO_vs_WT", alpha = 0.05, l2fc = 1)

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
PlotMA(obj, assay = "KO_vs_WT", alpha = 0.05, l2fc = 1)

Plot Ordination Correlations With Metadata Heatmap

Description

Calculate correlations between NMDS ordination axes (from beta diversity distances) and metadata, and plot a ggplot2 heatmap with numeric labels and significance stars.

For continuous variables, Pearson correlation is used. P values follow this convention: p < 0.001, p < 0.01, p < 0.05 = three, two, one stars, respectively.

Default num_mds is 10, but function will internally set max number of NMDS to be n-1 where n is your number of samples.

Usage

PlotOrdCorr(object, num_mds = 10, meta_cols = NULL, distance = "bray")
PlotOrdCorr(object, num_mds = 10, meta_cols = NULL, distance = "bray")

Arguments

object

A geneda object containing normalized and metadata.

num_mds

Number of NMDS axes to correlate. Default = 10

meta_cols

Optional character vector of metadata column names to include. Defaults to all metadata columns.

distance

Distance metric for vegdist (default "bray").

Value

A list with elements: cor_matrix, pval_matrix, stars, plot (ggplot), and variance explained (squared correlations between ordination distances and observed dissimilarities.)

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
set.seed(42)
oc <- PlotOrdCorr(obj, num_mds = 3, meta_cols = "Genotype")
oc$plot
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
set.seed(42)
oc <- PlotOrdCorr(obj, num_mds = 3, meta_cols = "Genotype")
oc$plot

Plot PCA results from a GenEDA object

Description

Visualize principal component analysis (PCA) results stored within a GenEDA object. This function extracts PCA data via ExtractPCA and provides a flexible ggplot2-based visualization interface.

Usage

PlotPCA(
  object,
  x = 1,
  y = 2,
  color_by,
  colors = NULL,
  split_by = NULL,
  shape_by = NULL,
  return_data = FALSE
)
PlotPCA(
  object,
  x = 1,
  y = 2,
  color_by,
  colors = NULL,
  split_by = NULL,
  shape_by = NULL,
  return_data = FALSE
)

Arguments

object

A GenEDA object containing PCA results in the DimReduction slot.

x

Numeric or character. The principal component to plot on the x-axis (e.g., 1 or "PC1").

y

Numeric or character. The principal component to plot on the y-axis (e.g., 2 or "PC2").

color_by

Character. Column name in the metadata used to color points.

colors

Vector. Custom colors to use for plotting

split_by

Character (optional). Column name in metadata used for faceting (creates separate panels).

shape_by

Character (optional). Column name in metadata used to control point shape.

return_data

Logical (default = FALSE), whether or not to return pca dataframe for more custom plotting.

Value

A ggplot object displaying the PCA scatter plot, or a list of pca_df and plot if return_data = TRUE

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
obj <- RunPCA(obj)
PlotPCA(obj,
    x = 1, y = 2, color_by = "Genotype",
    colors = c(WT = "grey40", Snai1_KO = "firebrick3")
)
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
obj <- RunPCA(obj)
PlotPCA(obj,
    x = 1, y = 2, color_by = "Genotype",
    colors = c(WT = "grey40", Snai1_KO = "firebrick3")
)

PlotPValHist

Description

Generate a p-value histogram from a DEG table inside a geneda object. This function uses raw p-values only and highlights p-values below a given threshold (default 0.05).

Usage

PlotPValHist(object, assay, bins = 40, alpha = 0.05, title = NULL)
PlotPValHist(object, assay, bins = 40, alpha = 0.05, title = NULL)

Arguments

object

A geneda object containing DEGs in the DEGs slot.

assay

The name of the DEG table to use (must contain a "pvalue" column).

bins

Number of histogram bins (default 40).

alpha

Significance threshold (default 0.05). Bars representing p-values < alpha will be highlighted.

title

Optional title for the plot.

Value

A ggplot2 object

Examples


data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
PlotPValHist(obj, assay = "KO_vs_WT")

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
PlotPValHist(obj, assay = "KO_vs_WT")

PlotScree

Description

Generate a scree plot

Usage

PlotScree(object)
PlotScree(object)

Arguments

object

A geneda object containing PCA results in the DimReduction slot.

Value

A ggplot2 object

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
obj <- RunPCA(obj)
PlotScree(obj)
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
obj <- RunPCA(obj)
PlotScree(obj)

PlotVolcano

Description

Plot volcano plot from a geneda object. Requires that you have ran SetDEGs on your object following differential expression in DESeq2.

Usage

PlotVolcano(object, assay, alpha, l2fc, den = NULL, num = NULL, title = NULL)
PlotVolcano(object, assay, alpha, l2fc, den = NULL, num = NULL, title = NULL)

Arguments

object

A geneda object containing DEGs from SetDEGs method

assay

The DEG slot to use for visualization

alpha

Threshold for adjusted p-values (padj column from DESeq2)

l2fc

Absolute value log2Fold-change magnitude threshold (log2FoldChange column)

den

Denominator (reference level for comparison, num vs. den). If NULL, the legend uses "denominator".

num

Numerator (numerator level for comparison, num vs. den). If NULL, the legend uses "numerator".

title

Optional character vector of what plot should be titled.

Details

Thresholds are inclusive (padj <= alpha, ⁠|log2FoldChange| >= l2fc⁠), matching FilterDEGs(). Genes with a missing log2FoldChange or padj are not plotted.

Value

A ggplot2 object

Examples


data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
PlotVolcano(obj,
    assay = "KO_vs_WT", alpha = 0.05, l2fc = 1,
    den = "WT", num = "Snai1_KO"
)

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
PlotVolcano(obj,
    assay = "KO_vs_WT", alpha = 0.05, l2fc = 1,
    den = "WT", num = "Snai1_KO"
)

Run PCA and store in `DimReduction`

Description

Uses generatePCs() under the hood. If HVGs are empty, selects HVGs via FindVariableFeatures(object, nfeatures) with default nfeatures = 2000. If HVGs are present, the PCA uses NFEATURES = length(HVGs(object)). This aligns the PCA feature count with the HVG selection while allowing an explicit override of nfeatures when empty.

Usage

RunPCA(object, nfeatures = 2000)
RunPCA(object, nfeatures = 2000)

Arguments

object

A geneda object

nfeatures

Number of features to use when HVGs are empty. Default = 2000

Value

Updated geneda object with DimReduction filled

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- RunPCA(obj, nfeatures = 1000)
DimReduction(obj)$percent_var
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- RunPCA(obj, nfeatures = 1000)
DimReduction(obj)$percent_var

Set unfiltered DEGs on the object

Description

Set unfiltered DEGs on the object

Usage

SetDEGs(object, deg_table, assay)
SetDEGs(object, deg_table, assay)

Arguments

object

A geneda object

deg_table

A data.frame of DESeq2-like results containing at least columns log2FoldChange and padj

assay

What to name the DEG slot

Value

Updated geneda object with DEGs[[assay]] set

Examples


data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
obj

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
obj

Show method for geneda

Description

Prints a short summary of a geneda object: the number of features, samples, and HVGs, whether PCA results and counts are present, and the names of stored DEG tables.

Usage

## S4 method for signature 'geneda'
show(object)
## S4 method for signature 'geneda'
show(object)

Arguments

object

A geneda object

Value

Invisibly returns object; called for its printed summary.

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- RunPCA(obj, nfeatures = 1000)
show(obj)
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- RunPCA(obj, nfeatures = 1000)
show(obj)

Snail1KO dataset

Description

Dataset from Maturi, et al. 2018, (ENA:ERP019920, E-MTAB-5244)

Usage

data(Snail1KO)
data(Snail1KO)

Format

A list containing raw counts and metadata

rawCounts: A dataframe of raw counts expression matrix
metadata: A dataframe of associated metadata

Source

Maturi et al., 2018; ENA: ERP019920; E-MTAB-5244

Examples

data(Snail1KO)
dim(Snail1KO$rawCounts)
Snail1KO$metadata
data(Snail1KO)
dim(Snail1KO$rawCounts)
Snail1KO$metadata

Summarize DEGs Across Selected Assays

Description

Summarizes the number of DEGs in each assay of a geneda object according to thresholds for adjusted p-value and log2 fold-change, separately reporting upregulated and downregulated genes. Thresholds are inclusive (padj <= alpha, ⁠|log2FoldChange| >= lfc⁠), matching FilterDEGs(). Genes with a missing log2FoldChange or padj are not counted; missing values in other columns (e.g., gene annotations) are ignored.

Usage

SummarizeDEGs(object, alpha = 0.05, lfc1 = 1, lfc2 = 2, assays = NULL)
SummarizeDEGs(object, alpha = 0.05, lfc1 = 1, lfc2 = 2, assays = NULL)

Arguments

object

A geneda object containing DEGs in its DEGs slot.

alpha

Numeric; adjusted p-value threshold. Default is 0.05.

lfc1

Numeric; lower log2 fold-change threshold. Default is 1.

lfc2

Numeric; higher log2 fold-change threshold. Default is 2.

assays

Optional character vector of assay names to summarize. Defaults to all assays in object@DEGs.

Value

A data.frame of DEG counts by assay and criteria.

Examples


data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
SummarizeDEGs(obj, alpha = 0.05, lfc1 = 1, lfc2 = 2)

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
SummarizeDEGs(obj, alpha = 0.05, lfc1 = 1, lfc2 = 2)

Package 'RGenEDA'

Help Index

Convert a SummarizedExperiment to a geneda object

Description

Usage

Arguments

Details

Value

See Also

Examples

DEDashboard

Description

Usage

Arguments

Value

Examples

Access DEGs container

Description

Usage

Arguments

Value

Examples

Access dimensional reduction results list

Description

Usage

Arguments

Value

Examples

extractEigen

Description

Usage

Arguments

Value

Examples

Extract PCA Loadings and Metadata

Description

Usage

Arguments

Details

Value

Examples

Filter DEGs by padj and absolute log2FoldChange

Description

Usage

Arguments

Value

Examples

Find DEGs Overlapping With HVGs

Description

Usage

Arguments

Value

Examples

Find and store highly variable genes (HVGs)

Description

Usage

Arguments

Value

Examples

Construct a geneda object

Description

Usage

Arguments

Details

Value

Examples

geneda S4 class

Description

Slots

Examples

generatePCs

Description

Usage

Arguments

Value

Examples

Save a pheatmap Objects generated from RGenEDA to File

Description

Usage

Arguments