BaseAverageQualityPlot

Description

BaseAverageQualityPlot

Usage

BaseAverageQualityPlot(input_data)

Arguments

interactive BaseAverageQualityPlot

Description

interactive BaseAverageQualityPlot

Usage

BaseAverageQualityPlotly(input_data)

Arguments

BaseCompositionAreaChartPlot

Description

BaseCompositionAreaChartPlot

Usage

BaseCompositionAreaChartPlot(input_data)

Arguments

BaseCompositionLinePlot

Description

BaseCompositionLinePlot

Usage

BaseCompositionLinePlot(input_data)

Arguments

BaseQualityBoxplotPlot

Description

BaseQualityBoxplotPlot

Usage

BaseQualityBoxplotPlot(input_data)

Arguments

Bulk alignment function

Description

Bulk alignment function

Usage

BulkAlignment(
  lalista,
  nodes,
  readsPath,
  GenomeIndex,
  outBam,
  threads,
  outFormat,
  phredScore,
  maxExtractedSubreads,
  consensusVote,
  mismatchMax,
  uniqueOnly,
  maxMultiMapped,
  indelLength,
  fragmentMinLength,
  fragmentMaxLength,
  matesOrientation,
  readOrderConserved,
  coordinatesSorting,
  allJunctions,
  tempfolder
)

Arguments

Title

Description

Title

Usage

CombinedAlignment(
  lalista,
  nodes,
  readsPath,
  GenomeConcIndex,
  outBam,
  threads,
  outFormat,
  phredScore,
  maxExtractedSubreads,
  consensusVote,
  mismatchMax,
  uniqueOnly,
  maxMultiMapped,
  indelLength,
  fragmentMinLength,
  fragmentMaxLength,
  matesOrientation,
  readOrderConserved,
  coordinatesSorting,
  allJunctions,
  tempfolder,
  readsAlignedBlock
)

Arguments

CorrPlotHeatmap

Description

Plot a correlation heatmap of top variable genes across samples.

Usage

CorrPlotHeatmap(
  x,
  scale,
  Color,
  type,
  display,
  round_number,
  cutree_rows,
  cutree_cols,
  cluster,
  show_names,
  NumGenes
)

Arguments

Details

This function selects the highest-variance genes from a log-CPM matrix, computes pairwise correlation coefficients (or p-values) with "Hmisc::rcorr()", and renders a heatmap via "pheatmap", with options for clustering, scaling, and number display.

1. Compute per-gene variance and select the top "NumGenes".

2. Subset the matrix and compute correlations (and p-values) via "Hmisc::rcorr()".

3. Choose to display correlation coefficients or p-values, rounded to "round_number".

4. Determine clustering and label visibility from cluster and "show_names".

5. Render the heatmap with "pheatmap::pheatmap()", passing in custom distance, color, clustering, and "display" number settings, saving to a temporary file to suppress autosave.

Value

A "pheatmap" object representing the correlation heatmap with clustering.

CorrPlotHeatmaply

Description

Create an interactive correlation heatmap of top variable genes using Heatmaply.

Usage

CorrPlotHeatmaply(x, Color, type, cluster, scale, show_names, NumGenes)

Arguments

Details

This function selects the highest-variance genes from a log-CPM matrix, computes pairwise correlation coefficients (and p-values) with "Hmisc::rcorr()", and renders an interactive correlation heatmap via "heatmaply::heatmaply_cor()", using clustering and scaling options derived from "pheatmap" call.

1. Compute per-gene variance and select the top "NumGenes".

2. Subset the matrix and compute correlations (and p-values) via "Hmisc::rcorr()".

3. Generate a temporary static heatmap with "pheatmap" to extract dendrograms.

4. Render an interactive heatmap with "heatmaply::heatmaply_cor()", passing in color, clustering, scaling, tick-label visibility, and point size based on -log10(p-value).

Value

A Plotly object (heatmaply) representing the interactive correlation heatmap.

Server function for DEGs module in Shiny application

Description

Server function for DEGs module in Shiny application

Usage

DEGsServerLogic(id)

Arguments

UI function for DEGs module in Shiny application

Description

UI function for DEGs module in Shiny application

Usage

DEGsUserInterface(id)

Arguments

Server function for EDA module in Shiny application

Description

Server function for EDA module in Shiny application

Usage

EDAServerLogic(id)

Arguments

UI function for EDA module in Shiny application

Description

UI function for EDA module in Shiny application

Usage

EDAUserInterface(id)

Arguments

EdgerDEG

Description

Perform differential expression analysis on RNA-seq count data using edgeR.

Usage

EdgerDEG(
  gr,
  WD_samples,
  WD_DEGs,
  colIDgene,
  colCounts,
  skip_preN,
  grContrast,
  filter,
  model,
  normMethod,
  min_count,
  min_total_count,
  large_n,
  min_prop,
  adjustPvalue,
  Th_logFC,
  Th_Pvalue
)

Arguments

Details

This function reads raw count tables, applies expression filtering (via "filterByExpr" or "HTSFilter"), normalizes library sizes, estimates dispersion, fits statistical models ("exactTest", "glmQLFTest", or "glmLRT"), and writes per-contrast results and diagnostic plots.

1. Reads in per-sample count files and generate a DGEList.

2. Builds the design matrix and contrast definitions from "grContrast".

3. Filters lowly expressed genes, normalizes library sizes, and logs filtering summary.

4. Estimates dispersion (standard or quasi-likelihood).

5. Runs chosen differential test per contrast, annotates each gene as "UP", "DOWN", or "NO", and writes CSV output files named by filter, model, and contrast.

6. Captures and saves BCV and QL dispersion plots as SVGs in WD_DEGs.

Value

A list invisibly returned containing any captured plots and log messages; primary results are written to CSV files in "WD_DEGs".

Filtering

Description

Filter paired-end FASTQ files in parallel based on quality and adapter trimming criteria.

Usage

Filtering(
  Nodes,
  X,
  UploadPath,
  DownloadPath,
  qualityType,
  minLen,
  trim,
  trimValue,
  n,
  Adapters,
  Lpattern,
  Rpattern,
  max.Lmismatch,
  max.Rmismatch,
  kW,
  left,
  right,
  halfwidthAnalysis,
  halfwidth,
  compress
)

Arguments

Details

This function processes raw paired-end FASTQ files to remove low-quality bases, trim adapters, and filter out short reads. It supports quality-based end trimming, sliding window trimming, and adapter removal. The processing is done in parallel across multiple nodes to enhance performance when working with large datasets.

• Paired FASTQ files must be named consistently, distinguished by "_1" and "_2" for forward and reverse reads.

• This function uses the "ShortRead" and "Biostrings" packages for FASTQ processing and quality filtering.

• Filtered files in FASTQ format".

• Log files containing read counts before and after filtering are written per sample.

Value

Filtered FASTQ files written to "DownloadPath"; one log file per sample.

Server function for filtering module in Shiny application

Description

Server function for filtering module in Shiny application

Usage

FilteringServerLogic(id)

Arguments

UI function for filtering module in Shiny application

Description

UI function for filtering module in Shiny application

Usage

FilteringUserInterface(id)

Arguments

GCcontentDistributionPlot

Description

GCcontentDistributionPlot

Usage

GCcontentDistributionPlot(input_data)

Arguments

interactive GCcontentDistributionPlot

Description

interactive GCcontentDistributionPlot

Usage

GCcontentDistributionPlotly(input_data)

Arguments

GetEdgerY

Description

Calculate and return filtered DGEList object and log-CPM matrices using edgeR and optional HTSFilter

Usage

GetEdgerY(
  gr,
  WDpn,
  colIDgene,
  colCounts,
  skip_preN,
  filterMethod,
  min_count,
  min_total_count,
  large_n,
  min_prop,
  normMethod
)

Arguments

Value

A list with total/kept gene counts, filtered DGEList objects, and log-CPM matrices

HeatmapExp

Description

Plot a heatmap of the top variable genes across samples.

Usage

HeatmapExp(
  x,
  ColorPanel,
  scale,
  cutree_rows,
  cutree_cols,
  cluster,
  show_names,
  NumGenes
)

Arguments

Details

This function selects the highest-variance genes from a log-CPM matrix, transposes the data, and renders a heatmap with customizable clustering, scaling, and color palettes using pheatmap.

1. Compute per-gene variance and select the top "NumGenes".

2. Transpose the subsetted matrix so samples are rows.

3. Apply the specified color palette (n = 50) via paletteer::paletteer_c().

4. Determine clustering and name-display options from "cluster" and "show_names".

5. Render the heatmap with "pheatmap::pheatmap()", saving to a temporary file to suppress autosave.

Value

A "pheatmap" object containing the heatmap and clustering information.

HeatmapExpPlotly

Description

Create an interactive heatmap of top variable genes using Heatmaply.

Usage

HeatmapExpPlotly(x, ColorPanel, scale, cluster, show_names, NumGenes)

Arguments

Details

This function selects the highest-variance genes from a log-CPM matrix, transposes the data, and renders an interactive heatmap via "heatmaply", using "pheatmap" call.

1. Compute per-gene variance and select the top NumGenes.

2. Transpose the subsetted matrix so samples are rows.

3. Generate a temporary static heatmap with pheatmap to extract dendrograms.

4. Render an interactive heatmap with heatmaply::heatmaply().

Value

A Plotly object (heatmaply) representing the interactive heatmap.

Bulk indexing

Description

Bulk indexing

Usage

IndexingBulk(basename, reference, gappedIndex, indexSplit, memory, TH_subread)

Arguments

Indexing bulk server logic

Description

Indexing bulk server logic

Usage

IndexingBulkServerLogic(id)

Arguments

Indexing bulk ui

Description

Indexing bulk ui

Usage

IndexingBulkUserInterface(id)

Arguments

Combined indexing

Description

Combined indexing

Usage

IndexingComb(
  basename,
  reference,
  gappedIndex,
  indexSplit,
  memory,
  TH_subread,
  gen1,
  gen2,
  outfolder,
  tempfolder = file.path(fs::path_temp(), "TempDirSum_3738"),
  tag1,
  tag2
)

Arguments

Indexing combined server logic

Description

Indexing combined server logic

Usage

IndexingCombinedServerLogic(id)

Arguments

Indexing combined ui

Description

Indexing combined ui

Usage

IndexingCombinedUserInterface(id)

Arguments

Indexing sequential parallel

Description

Indexing sequential parallel

Usage

IndexingSequentialParallel(
  basename,
  reference,
  gappedIndex,
  indexSplit,
  memory,
  TH_subread
)

Arguments

Indexing sequential progressive

Description

Indexing sequential progressive

Usage

IndexingSequentialProgressive(
  outfolder1,
  outfolder2,
  refgen1,
  refgen2,
  gappedIndex,
  indexSplit,
  memory,
  TH_subread
)

Arguments

Indexing sequential server logic

Description

Indexing sequential server logic

Usage

IndexingSequentialServerLogic(id)

Arguments

Indexing sequential ui

Description

Indexing sequential ui

Usage

IndexingSequentialUserInterface(id)

Arguments

QUALITY CONTROL ANALYSIS

Description

QUALITY CONTROL ANALYSIS

Usage

QualityCheckAnalysis(
  directoryInput,
  inputFormat,
  Nodes,
  ReadsNumber,
  directoryOutput,
  tempFolder
)

Arguments

Saturation

Description

Generate a saturation curve plot showing gene detection versus sequencing depth.

Usage

Saturation(matrix, method, max_reads, palette)

Arguments

Details

This function estimates how many genes are detected at increasing read depths using a rarefaction-based approach ( "estimate_saturation() from RNAseQC package https://github.com/BenaroyaResearch/RNAseQC.git"), and plots the saturation curves for each sample. It supports two estimation methods: “division” for a fast analytic approximation and “sampling” for more realistic approach.

1. Internally, "extract_counts() " (from countSubsetNorm) extracts a counts matrix from various input classes (matrix, DGEList, EList, ExpressionSet).

2. "estimate_saturation() " (from RNAseQC package https://github.com/BenaroyaResearch/RNAseQC.git) rarefies each library at multiple depths:

• “division” divides counts by scale factors;

• “sampling” performs repeated random sampling to simulate read down sampling.

1. The resulting data frame contains one row per sample per depth, with the number of detected genes ( "sat ") and, for sampling, its variance ( "sat.var ").

2. The function then plots gene saturation curves ( "sat" vs. "depth") colored by sample.

Extract counts matrix from different types of expression objects

Estimate saturation of genes based on rarefaction of reads

Value

A "ggplot " object showing saturation (genes detected) versus sequencing depth for each sample.

SequenceLengthDistributionPlot

Description

SequenceLengthDistributionPlot

Usage

SequenceLengthDistributionPlot(input_data)

Arguments

interactive SequenceLengthDistributionPlot

Description

interactive SequenceLengthDistributionPlot

Usage

SequenceLengthDistributionPlotly(input_data)

Arguments

Sequential alignment function

Description

Sequential alignment function

Usage

SequentialAlignment(
  lalista,
  nodes,
  readsPath,
  GenomeFirstIndex,
  GenomeSecondIndex,
  outBam1,
  outBam2,
  threads,
  outFormat,
  phredScore,
  maxExtractedSubreads,
  consensusVote,
  mismatchMax,
  uniqueOnly,
  maxMultiMapped,
  indelLength,
  fragmentMinLength,
  fragmentMaxLength,
  matesOrientation,
  readOrderConserved,
  coordinatesSorting,
  allJunctions,
  tempfolder,
  readsAlignedBlock
)

Arguments

Summarization

Description

Summarizes read counts from multiple BAM/SAM files in parallel using feature annotations.

Usage

Summarization(
  NodesSum,
  Xsum,
  UploadPathSum,
  DownloadPathSum,
  annot.ext,
  isGTFAnnotationFile,
  GTF.featureType,
  GTF.attrType,
  useMetaFeatures,
  allowMultiOverlap,
  minOverlap,
  fracOverlap,
  fracOverlapFeature,
  largestOverlap,
  countMultiMappingReads,
  fraction,
  minMQS,
  primaryOnly,
  ignoreDup,
  strandSpecific,
  requireBothEndsMapped,
  checkFragLength,
  minFragLength,
  maxFragLength,
  countChimericFragments,
  autosort,
  nthreads,
  tmpDir,
  verbose
)

Arguments

Details

This function run Rsubread::featureCounts() on each input file, capturing count statistics, annotation data, and per-sample summary logs. Results are written to the specified output directory.

1. A socket cluster of NodesSum workers is created.

2. Each worker invokes featureCounts() on one sample, using the annotation and counting parameters.

3. Outputs per sample:

   • A text summary (⁠*_summary.txt⁠) capturing the console output.

   • A CSV of count statistics (⁠*_stat.csv⁠).

   • A CSV of feature annotations (⁠*_annotation.csv⁠).

   • A tab-delimited count matrix saved under ⁠Counts/<sample>.tab⁠.

4. The cluster is terminated once all samples complete.

Value

Writes files to DownloadPathSum.

Server function for Summarization module in Shiny application

Description

Server function for Summarization module in Shiny application

Usage

SummarizationServerLogic(id)

Arguments

UI function for Summarization module in Shiny application

Description

UI function for Summarization module in Shiny application

Usage

SummarizationUserInterface(id)

Arguments

UpSetPlot

Description

Generate an UpSet plot of overlapping DEGs across multiple contrasts.

Usage

UpSetPlot(
  WD_samples,
  Th_logFC,
  Th_Pvalue,
  collapseName,
  nintersects,
  st_significance,
  scale
)

Arguments

Details

This function reads DEG CSV files from a directory, filters genes by log-FC and p-value thresholds (adjusted or raw), optionally simplifies file names, and visualizes the intersections of gene sets using an UpSet plot.

1. Validates thresholds (Th_logFC >= 0, 0 < Th_Pvalue <= 1).

2. Lists all CSV files in WD_samples and reads each into a data frame.

3. Checks for duplicate IDs and standardizes to columns ID, logFC, and adjustPvalue or PValue.

4. Filters each set of results by |logFC| >= Th_logFC and p-value < Th_Pvalue.

5. Renames each gene-ID column to the (optionally collapsed) file name.

6. Converts the list of filtered ID sets to an UpSetR input and calls UpSetR::upset().

Value

An UpSet plot.

UpsetjsPlot

Description

Create an interactive UpSet plot of overlapping DEGs using "UpsetJS".

Usage

UpsetjsPlot(
  WD_samples,
  Th_logFC,
  Th_Pvalue,
  collapseName,
  nintersects,
  st_significance
)

Arguments

Details

This function reads DEG CSV files from a directory, filters genes by log-FC and p-value thresholds (adjusted or raw), optionally simplifies file names, and visualizes the intersections of gene sets using the "UpsetJS" package.

1. Lists all CSV files in "WD_samples" and reads each into a data frame.

2. Checks for duplicate IDs and selects "ID", "logFC", and either "adjustPvalue" or "PValue".

3. Filters each set by "|logFC| >= Th_logFC" and p-value < "Th_Pvalue".

4. Renames each gene-ID list to the (optionally collapsed) file name.

5. Feeds the list of gene sets into "upsetjs::upsetjs()"

Value

An interactive "UpsetJS" object.

Server function for workflow module in Shiny application

Description

Server function for workflow module in Shiny application

Usage

WorkflowServerLogic(id)

Arguments

UI function for workflow module in Shiny application

Description

UI function for workflow module in Shiny application

Usage

WorkflowUserInterface(id)

Arguments

barplotExp

Description

Create a barplot of library sizes per sample, optionally using effective library sizes.

Usage

barplotExp(x, palette, main, selectOrder, effecLibSize)

Arguments

Details

This function extracts library size information from an "edgeR" "DGEList", computes effective library sizes if requested, orders samples by group or name, and plots library sizes (in millions) colored by group.

1. Extracts or computes (effecLibSize = TRUE) the library size for each sample.

2. Orders samples by group or sample name per selectOrder.

3. Plots bar heights as library size (×10^6) with white fill and colored borders.

Value

A "ggplot" object showing per-sample barplots of library size in millions.

boxplotExp

Description

Generate a boxplot of log-CPM expression values per sample, colored by group.

Usage

boxplotExp(x, y, palette, main, selectOrder)

Arguments

Details

This function orders samples by group or sample name, and produces a ggplot2 boxplot with a horizontal line at the overall median.

1. Extract sample metadata (Samples, Groups) from "x$samples".

2. Order columns of y by group or sample name per "selectOrder".

3. Melt the ordered matrix to long format and join with metadata.

4. Plot boxplots with no outliers, colored by group, and include a dashed line at the overall median.

Value

A ggplot object showing per-sample boxplots of log-CPM values.

checkMetadata

Description

Validate and extract non-empty annotation fields from a GTF file.

Usage

checkMetadata(gtfPath, typeFilter)

Arguments

Details

This function imports a GTF file, filters entries by a specified feature type, and identifies metadata columns that contain at least one non-missing value.

1. Imports the GTF into a data frame via "rtracklayer::import()".

2. Filters rows by "type" == typeFilter.

3. Tests each column for all-NA or empty-string entries.

4. Returns names of columns with at least one non-missing, non-empty value.

Value

Character vector of column names in the GTF annotation that are not entirely NA or empty.

COUNTING SEQUENCES

Description

COUNTING SEQUENCES

Usage

counting_Reads(input_data)

Arguments

getDegMerged

Description

Merge multiple DEG result CSVs with GTF annotations into a single data frame.

Usage

getDegMerged(path, gtfPath, columns, collapseName, typeFilter, selectUpDown)

Arguments

Details

This function reads all CSV files in a directory, validates presence of required columns ("ID", and optionally "diffExp"), filters for up/down regulated genes if requested, extracts annotation fields from a GTF, and returns a merged table of selected annotation columns alongside all DEG metrics (with optional file-based column prefixes).

Value

A combined data frame

inDAGO

Description

A Shiny app for dual and bulk RNA‑sequencing analysis.

Usage

inDAGO()

Details

This function allows to launch inDAGO Shiny interface.

Value

No return value, called for side effects

Mapping bulk server logic

Description

Mapping bulk server logic

Usage

mappingBulkServerLogic(id)

Arguments

Mapping bulk ui

Description

Mapping bulk ui

Usage

mappingBulkUserInterface(id)

Arguments

Mapping combined server logic

Description

Mapping combined server logic

Usage

mappingCombinedServerLogic(id)

Arguments

Mapping combined ui

Description

Mapping combined ui

Usage

mappingCombinedUserInterface(id)

Arguments

Mapping sequential server logic

Description

Mapping sequential server logic

Usage

mappingSequentialServerLogic(id)

Arguments

Mapping sequential ui

Description

Mapping sequential ui

Usage

mappingSequentialUserInterface(id)

Arguments

mdsPlot

Description

Generate a multidimensional scaling (MDS) plot based on expression data.

Usage

mdsPlot(
  x,
  Sample,
  Group,
  title,
  palette,
  maxOverlaps,
  sizeLabel,
  top,
  gene.selection
)

Arguments

Details

This function performs MDS analysis using limma's "plotMDS() " and visualizes the sample relationships in two dimensions using "ggplot2 " and "ggrepel ".

Value

A "ggplot " object representing the MDS plot.

mdsPlottly

Description

Generate an interactive MDS plot using Plotly based on expression data.

Usage

mdsPlottly(x, Sample, Group, title, palette, top, gene.selection)

Arguments

Details

This function computes multidimensional scaling (MDS) coordinates with limma's "plotMDS() " and then renders an interactive scatterplot via "plotly::ggplotly() ".

1. Compute MDS on the input data with "limma::plotMDS() ".

2. Extract eigenvalues and first two dimensions for variance annotation.

3. Build a ggplot2 scatterplot with axis labels showing percent variance explained.

4. Convert the ggplot to an interactive Plotly graph.

Value

A Plotly object ( "plotly::ggplotly ") representing the interactive MDS scatterplot.

mdsinfo

Description

Compute MDS coordinates for expression data using limma's plotMDS.

Usage

mdsinfo(matrix, top, gene.selection)

Arguments

Details

This function performs multidimensional scaling (MDS) on a DGEList or log-expression matrix using limma's "plotMDS() " function. It returns the MDS object containing coordinates and eigenvalues without generating a plot.

Value

A list object from "plotMDS() " containing MDS coordinates and eigenvalues.

pcaPlot

Description

Create a PCA scatter plot from log-expression data with sample labels.

Usage

pcaPlot(
  logcounts,
  Sample,
  Group,
  title,
  palette,
  maxOverlaps,
  sizeLabel,
  center,
  scale
)

Arguments

Details

This function performs Principal Component Analysis (PCA) on a log-count matrix and visualizes the first two principal components using ggplot2 and ggrepel. Each point represents a sample, colored by group, with hover labels.

1. Transposes the "logcounts" matrix so samples are rows.

2. Runs PCA via "stats::prcomp()" with centering and scaling options.

3. Calculates percent variance explained by PC1 and PC2.

4. Builds a scatter plot with black‐bordered points and non‐overlapping labels.

Value

A "ggplot" object displaying the PCA scatter plot of PC1 vs PC2.

pcaPlottly

Description

Create an interactive PCA scatter plot using Plotly from log-expression data.

Usage

pcaPlottly(logcounts, Sample, Group, title, palette, center, scale)

Arguments

Details

This function performs Principal Component Analysis (PCA) on a log-count matrix and generates an interactive plot of the first two principal components via "plotly::ggplotly()".

1. Transposes the "logcounts " matrix so samples are rows.

2. Runs PCA with "stats::prcomp() ", using centering and scaling as specified.

3. Computes percent variance explained by PC1 and PC2.

4. Builds a ggplot2 scatterplot and converts it to an interactive Plotly graph.

Value

A Plotly object ( "plotly::ggplotly ") representing the interactive PCA scatterplot.

pcainfo

Description

Perform Principal Component Analysis (PCA) on log-expression data.

Usage

pcainfo(logcounts, center, scale)

Arguments

Details

This function transposes a log-count matrix (samples as columns, genes as rows) and runs PCA using "stats::prcomp() ", with options to center and scale variables.

Value

An object of class "prcomp " containing the PCA results, including loadings, scores, and explained variance.

Quality control server logic

Description

Quality control server logic

Usage

qualityControlServerLogic(id)

Arguments

Quality control ui

Description

Quality control ui

Usage

qualityControlUserInterface(id)

Arguments

volcanoPlot

Description

Create a volcano plot of differential expression results.

Usage

volcanoPlot(
  x,
  palettePoint,
  maxOverlaps,
  sizeLabel,
  Th_logFC,
  Th_Pvalue,
  subsetGenes,
  st_significance
)

Arguments

Details

This function reads a CSV of DEGs, classifies genes as up/down/no change based on log-fold change and p-value thresholds, and plots –log10(p-value) versus log-FC using ggplot2.

1. Reads the input CSV and checks for duplicate IDs.

2. Standardizes columns to "ID", "logFC", and "adjustPvalue" or "PValue".

3. Optionally subsets to the top N genes by p-value.

4. Classifies each gene as "UP", "DOWN", or "NO" based on thresholds.

5. Plots points with manual fill, size, and alpha scales, adds threshold lines, and repels labels using "ggrepel".

Value

A "ggplot" object displaying the volcano plot.

volcanoPlottly

Description

Create an interactive volcano plot of differential expression results using "Plotly".

Usage

volcanoPlottly(
  x,
  palettePoint,
  Th_logFC,
  Th_Pvalue,
  subsetGenes,
  st_significance
)

Arguments

Details

This function reads a CSV of DEGs, classifies genes as up/down/no change based on log-fold change and p-value thresholds, and renders an interactive volcano plot via "plotly::ggplotly()".

1. Reads the input CSV and checks for duplicate IDs.

2. Standardizes columns to "ID", "logFC", and "adjustPvalue" or "PValue".

3. Optionally subsets to the top N genes by p-value.

4. Classifies each gene as "UP", "DOWN", or "NO" based on thresholds.

5. Plots points with manual fill, size, and alpha scales, adds threshold lines, and converts to an interactive Plotly graph.

Value

A Plotly object ("plotly::ggplotly") representing the interactive volcano plot.