Changes in version 1.1.5                        

New features

  - get_foldchange_matrix() gains display_id, display_from and
    display_orgdb, matching [get_foldchange_heatmap()]. genes may now be
    given as display labels, and the returned matrix is labelled with
    them. Because symbols are not unique, duplicated labels are made
    unique with a warning rather than left to collide -- duplicated
    rownames would make m["SYM", ] return whichever row came first and
    silently hide the other. Rows keep the object's identifiers unless
    display_id is supplied, so existing calls are unaffected.

Bug fixes

  - get_foldchange_heatmap(annotate_columns = TRUE) annotated nothing.
    The columns of a fold-change heatmap are comparisons, but the
    annotation was built by filtering colData for samples named in those
    columns -- an intersection that is always empty -- so the track
    carried no levels and no colours. It now annotates by comparison,
    coloured from the object's comparison palette, and accepts
    column_anno_colors like the expression heatmap.
  - A display_id supplied without a display_orgdb failed with missing
    value where TRUE/FALSE needed. The internal comparison to_type ==
    from_type is NA when from_type is NA and logical(0) when it is NULL,
    and || can evaluate neither. It now names the missing argument.
  - get_expression_lineplot() accepted display_from/display_orgdb and
    ignored them -- its private copy of the identifier mapping handled
    only the rowData() path. All gene-taking plots now share one
    resolver.

                        Changes in version 1.1.4                        

Internal

  - Plot-stability snapshots for PCA and MDS no longer depend on
    eigenvector sign. prcomp() and cmdscale() may return an axis or its
    negation for identical input, decided by the LAPACK/BLAS build, so
    the snapshots could fail on one machine and pass on another with
    nothing in the package changed. Each axis is now canonicalised
    before comparison. No user-visible behaviour changes; only the test
    suite is affected.

                        Changes in version 1.1.3                        

Bug fixes

  - Statistical annotations failed under ggpubr 1.0.0. Passing
    stats_group = TRUE produced a plot that constructed normally and
    then died when drawn, with could not find function "create_p_label".
    ggpubr 1.0.0 builds p.label = "p.format" into an expression calling
    that package-internal helper, which is not visible from the
    environment of the mapping VISTA supplies, so the annotation layer
    could not be evaluated. VISTA now names the statistic's own computed
    columns via ggplot2::after_stat(). The rendered label text is
    unchanged on every ggpubr version, and an unrecognised p.label still
    falls through to ggpubr rather than silently dropping the
    annotation.
    
    Affects get_expression_boxplot(), get_expression_violinplot(),
    get_expression_barplot(), get_expression_raincloud(),
    get_foldchange_boxplot() and get_foldchange_raincloud() — anywhere
    stats_group = TRUE was combined with the default p.label =
    "p.format". Users on ggpubr < 1.0.0 were unaffected.

Documentation

  - Figure cropping is disabled (crop = NULL). BiocStyle's crop hook
    shells out to magick; without it every figure emitted a warning.

                        Changes in version 1.1.2                        

Targeted at Bioconductor 3.24 (VISTA 1.2.0). This release fixes several
defects that produced silently wrong results, makes VISTA behave like a
proper Bioconductor object, and begins a deprecation cycle for a number
of inconsistent argument names. No existing script stops working: every
renamed argument still functions and warns.

Data-integrity fixes

Check whether you were affected:

  - get_expression_matrix(genes = ..., summarise = TRUE) mislabelled
    rows. Rows were subset in assay order but labelled in the order
    genes was supplied, so whenever those differed every row carried
    another gene's identifier and values.
    
    Scope: no other VISTA function called get_expression_matrix(), so no
    plot, heatmap, DEG table, export or generated report was affected.
    Internal group averaging goes through norm_counts(summarise = TRUE),
    which labels rows from the matrix itself and was never affected by
    this. Only code that called get_expression_matrix() directly could
    be wrong.
    
    Were you affected? Only if you passed genes and summarise = TRUE and
    your gene vector was in some order other than the object's own row
    order. Passing genes already in rownames(x) order (or omitting
    genes) always produced correct output. Note that ranked inputs — for
    example get_genes_by_regulation(top_n = ...), which sorts by
    absolute fold change — are normally not in row order, so those calls
    were affected. Re-run them.
    
    The bug dates back to the first release, so it is present in 1.0.0
    and every 0.99.x.

  - GSEA ranked vectors slid scores onto the wrong genes. get_gsea()
    reassigned names(rank_vec) from a mapping helper that drops unmapped
    and duplicate identifiers, so the shorter vector was padded with NA
    and every score after the first gap moved to a different gene. The
    resulting NES values and leading-edge gene lists were meaningless.
    
    Scope: only set_type = "kegg" was affected in practice. That path
    always converts to ENTREZID, and any real dataset contains
    identifiers with no ENTREZ mapping, so the vector always shortened.
    set_type = "msigdb" converted an identifier type to itself, which
    short-circuits before querying the annotation database and cannot
    shorten. set_type = "go" never reassigned the names at all. Verified
    on a 22-gene input containing two identifiers absent from the OrgDb:
    "kegg" returned 20 names for 22 scores, "msigdb" returned 22.
    
    No other VISTA function called get_gsea(), so nothing else in the
    package consumed a corrupted ranking. Re-run any get_gsea(set_type =
    "kegg") results; MSigDB and GO results are unaffected.

  - get_corr_heatmap() drew a staircase, not a triangle. The triangle
    mask was computed against input order while the axes were
    re-levelled to clustered order, under the default cluster_by =
    "correlation".

  - Multi-file count importers bound columns positionally. The STAR,
    HTSeq and RSEM importers took gene identifiers from the first file
    only and never checked that row i described the same gene in the
    others, so files with equal row counts but different ordering were
    silently misaligned and every count for those samples was attributed
    to the wrong gene.
    
    Scope: only read_vista_counts() given a vector of two or more file
    paths with format of "star", "htseq" or "rsem", and only when those
    files did not already list the same genes in the same order. A
    single file, a data frame, a matrix, "featurecounts" and "tximport"
    all take a name-keyed path and were never affected. Files written by
    one pipeline run normally do share an order, so a uniform set of
    inputs was fine; the risk is mixing files produced at different
    times or by different pipeline versions, where HTSeq's trailing
    __no_feature/__ambiguous rows or STAR's four leading N_* rows may
    differ.
    
    Imports now match by gene identifier and error, naming the file and
    up to five missing identifiers, when a file does not cover the
    reference gene set. This can surface an error where a script
    previously appeared to work; it was producing wrong numbers.
    Anything built from a multi-file STAR, HTSeq or RSEM import should
    be re-imported and re-analysed.

  - run_vista_report() reported its own defaults as your parameters.
    When a prebuilt object was supplied via vista_rds, the report
    printed deseq2 / 1 / 0.05 / padj regardless of how the object was
    built.

  - run_cell_deconvolution()'s SYMBOL retry told xCell2 the wrong
    identifier type. When the first scoring attempt failed, VISTA
    retried with a SYMBOL-keyed matrix built from rowData, but carried
    the original gene_id_type (for example "ensembl") into the retry --
    handing xCell2 symbols while declaring them Ensembl IDs. The retry
    therefore either failed with the real error masked behind a generic
    message, or scored against a near-empty gene overlap.

  - Deconvolution was broken for ENSG...:SYMBOL rownames — the common
    case reached automatically by gene_id_type = "auto".

  - Fixed get_ma_plot() reading a non-existent metadata key for its
    threshold fallback, get_expression_lollipop() failing to resolve
    symbols through an OrgDb, comparison colours collapsing to
    character(0) and crashing get_foldchange_lineplot(), NaN columns
    when summarising over a factor with unused levels, and YAML/filename
    escaping in report and asset output.

  - Hardened the DE-table alignment helper, which reindexes every
    comparison onto the counts matrix and so underpins the whole
    package. It now rejects a duplicated reference gene list instead of
    emitting rows whose names R had silently de-duplicated (g2 -> g2.1)
    away from the gene_id column they came from, coerces tibble input to
    a plain data frame — a tibble ignores rownames<-, so every gene read
    as absent and was replaced by an NA row with a perfectly correct
    label — and asserts as a post-condition that the returned rownames
    and gene_id column both match the reference exactly. No shipped code
    path reached the tibble case; both are closed against future
    callers, because a wrong answer here is invisible in the output's
    shape.

Reproducibility

  - Pathway gene capping is now deterministic.
    get_pathway_heatmap(max_genes) and get_enrichment_chord(max_genes)
    truncated the pathway gene set positionally. Pathway membership is a
    set, and clusterProfiler's ordering of it is arbitrary and, verified
    here, differs between R sessions -- so the same call could plot a
    different subset of genes on different runs. The heatmap now ranks
    candidates by expression variance (most informative rows first) and
    the chord by pathway participation, both breaking ties on the gene
    identifier.
  - Deconvolution scores are no longer aligned to samples by position
    when their own labels disagree. run_cell_deconvolution() accepted
    scores whose row count matched the sample count and stamped the
    object's sample names onto them, even when the scores carried
    different labels of their own -- which would attach every sample's
    cell fractions to the wrong sample. Alignment is now by name
    whenever names are available in either orientation; contradictory
    labels are an error; and position is used only when the scores carry
    no labels at all, with a message saying so.
  - The airway vignette sets a seed. GSEA estimates p-values by
    permutation and ComplexHeatmap's row k-means is unseeded, so the
    rendered document previously changed on every build for reasons
    unrelated to the code.

Analysis changes

  - The edgeR and limma backends now filter with the same predicate as
    DESeq2 (a gene is kept when at least min_replicates samples reach
    min_counts); they previously ignored min_counts and used a hardcoded
    cpm > 1. They also filter before calcNormFactors(), per the edgeR
    user's guide. edgeR and limma results shift slightly relative
    to 1.1.1.
  - min_replicates is documented accurately: it counts samples across
    the whole experiment, never within each group.

New features

  - Raw counts are retained as a counts assay, reachable with counts(v).
    as_deseq_dataset() converts a VISTA object back into a DESeqDataSet.
    Roughly doubles the assay footprint; opt out with
    create_vista(keep_raw_counts = FALSE).
  - [ method. Subsetting now keeps metadata consistent: row subsetting
    reindexes every DE table and recounts DEG summaries, column
    subsetting prunes orphaned colour maps and warns when a comparison
    loses its samples. Previously v[1:10, ] left the DE tables
    describing all original genes and validObject() still returned TRUE.
  - show() method. Objects now display as class: VISTA with their
    grouping, comparisons, active DE source, cutoffs and schema version.
  - updateObject() migrates objects from older metadata schemas.
  - ?VISTA resolves, and there is a package overview page.
  - Unknown arguments passed through ... are now rejected with a
    did-you-mean suggestion. A typo such as get_expression_heatmap(v,
    gene = my_genes) previously plotted the default gene set instead of
    erroring.
  - get_foldchange_lineplot() gains return_type. Its default stays
    "both" (the list it has always returned); pass "plot" for a bare
    ggplot.
  - get_enrichment_chord() gains return_type. Its default stays "data"
    because it draws to the active device and has always returned its
    table invisibly; "plot" returns a recorded plot that
    save_vista_plot() can now write to a file. VISTA enables the device
    display list before drawing, since png() and pdf() leave it off and
    would otherwise yield an empty recording that silently saved
    nothing.

Deprecations

All still work and warn once per session. See ?VISTA-deprecated for the
full table and timelines; each becomes defunct in 1.4.0.

| Function | Deprecated | Use instead | |---|---|---| |
get_deg_count_pieplot(), get_deg_count_donutplot() | label | label_type
| | get_corr_heatmap() | cluster_by | order_by | | get_corr_heatmap() |
show_corr_values, col_corr_values | label, label_color | |
get_celltype_group_dotplot() | error | errorbar | | four
get_expression_*() plots | comparisons | stat_comparisons | |
get_pca_plot(), get_mds_plot(), get_umap_plot() | top_n_genes | top_n |
| get_expression_lollipop(), get_foldchange_lollipop() | line_size |
linewidth | | get_volcano_plot() | col_up, col_down, col_other(s),
lab_size | colors, label_size | | get_pca_plot() | sample.seed |
removed; it never had any effect | | get_expression_barplot(),
get_expression_lollipop() | facet_scale | facet_scales | |
get_expression_violinplot(), get_expression_lineplot() | value_transform
| log_transform |

return_type values are unified on c("plot", "data", "both"). The legacy
spellings still work and warn: "heatmap"/"clusters" (both heatmaps),
"matrix" (get_celltype_heatmap()), "genes" (get_pathway_heatmap()).
Arguments that select the shape of a purely tabular result --
get_pathway_genes(), get_genes_by_regulation(), read_vista_counts(),
derive_vista_metadata() -- keep their own vocabularies, because they
have no plot component.

Undocumented gene caps (20 for embeddings, 15 for lollipop, 25 for
barplot) are now the max_genes argument, with the existing values as
defaults.

Testing

  - Added label/value integrity tests covering every exported function
    that accepts a gene set. They assert two properties the 1.0.0 defect
    violated: results do not depend on the order genes were supplied in,
    and the value reported for a gene is that gene's own value, checked
    against an independent recomputation from the object. The previous
    suite asserted shape -- is.matrix(), nrow(), class -- all of which
    the broken output satisfied.
  - Verified by mutation testing: reintroducing the exact 1.0.0 defect
    now fails five tests across three files, and replacing a name-keyed
    lookup with a positional one elsewhere fails nine.
  - The DE-table alignment helper is now covered end to end: identifier
    resolution and its fallbacks, DESeq2/edgeR/limma column
    canonicalisation, type-preserving NA padding across numeric,
    integer, character, logical, factor, Date and POSIXct columns, and
    the reordering guarantee itself. Both faults fixed above were found
    by these tests.

Internal

  - Accessor generics dispatch only on the object rather than on every
    formal.
  - Depends: R (>= 4.6.0); viridis dropped from Imports (unused);
    BiocGenerics added. grDevices, graphics, stats, tools and utils are
    now declared — all five were already used via :: without being
    listed. The maintainer's ORCID iD is recorded in Authors@R.
  - print.vista removed — no object could carry that class.
  - Plot layer data is pinned by snapshot tests so API renames cannot
    silently change output.

                        Changes in version 1.1.1                        

Bug fixes

  - get_volcano_plot() now inherits log2fc_cutoff, pval_cutoff, and the
    new p_value_type argument from cutoffs(x) instead of hard-coding the
    raw pvalue column at 0.05. Previously the volcano could colour a
    different set of genes than deg_summary(), get_deg_count_barplot(),
    and get_genes_by_regulation() reported for the same object. Pass the
    arguments explicitly to restore the old behaviour (p_value_type =
    "pvalue", pval_cutoff = 0.05). The y-axis label now reflects which
    p-value column is plotted.

  - Consensus DE tables (method = "both") no longer overwrite pvalue and
    padj with 1 for every gene that is not called by both backends. Each
    gene now carries the less-significant of the two backend values, so
    the columns stay continuous and usable for volcano/MA plots and
    ranking. Per-backend values remain available in
    pvalue_deseq2/pvalue_edger/padj_deseq2/ padj_edger, and DEG calls
    are unchanged.

  - get_ma_plot() read its threshold fallback from the non-existent
    metadata(x)$cutoffs key, silently using package defaults instead of
    the object's stored cutoffs.

Reproducibility

  - Pathway gene capping is now deterministic.
    get_pathway_heatmap(max_genes) and get_enrichment_chord(max_genes)
    truncated the pathway gene set positionally. Pathway membership is a
    set, and clusterProfiler's ordering of it is arbitrary and, verified
    here, differs between R sessions -- so the same call could plot a
    different subset of genes on different runs. The heatmap now ranks
    candidates by expression variance (most informative rows first) and
    the chord by pathway participation, both breaking ties on the gene
    identifier.
  - Deconvolution scores are no longer aligned to samples by position
    when their own labels disagree. run_cell_deconvolution() accepted
    scores whose row count matched the sample count and stamped the
    object's sample names onto them, even when the scores carried
    different labels of their own -- which would attach every sample's
    cell fractions to the wrong sample. Alignment is now by name
    whenever names are available in either orientation; contradictory
    labels are an error; and position is used only when the scores carry
    no labels at all, with a message saying so.
  - The airway vignette sets a seed. GSEA estimates p-values by
    permutation and ComplexHeatmap's row k-means is unseeded, so the
    rendered document previously changed on every build for reasons
    unrelated to the code.

Analysis changes

  - The edgeR and limma-voom backends now filter genes with the same
    predicate as the DESeq2 backend — a gene is kept when at least
    min_replicates samples reach min_counts. They previously ignored
    min_counts at this step and used a hard-coded cpm > 1 threshold, so
    the same arguments produced different feature sets across backends.

  - The edgeR and limma-voom backends now filter before calling
    edgeR::calcNormFactors(), per the edgeR user's guide; normalization
    factors were previously estimated from genes that were about to be
    discarded. Both changes shift edgeR/limma results slightly relative
    to 1.1.0.

Documentation

  - min_replicates is documented accurately: it counts samples across
    the whole experiment, not within each group, which is what all
    backends have always implemented.
  - create_vista() now documents the consensus table's column semantics,
    including the support column.
  - Removed a reference to get_enrichment_network() from the README; no
    such function exists. Corrected the citation block and listed the
    missing expression-plot entries.

Testing

  - Added label/value integrity tests covering every exported function
    that accepts a gene set. They assert two properties the 1.0.0 defect
    violated: results do not depend on the order genes were supplied in,
    and the value reported for a gene is that gene's own value, checked
    against an independent recomputation from the object. The previous
    suite asserted shape -- is.matrix(), nrow(), class -- all of which
    the broken output satisfied.
  - Verified by mutation testing: reintroducing the exact 1.0.0 defect
    now fails five tests across three files, and replacing a name-keyed
    lookup with a positional one elsewhere fails nine.

Internal

  - Removed duplicate definitions of .prepare_sample_metadata() and
    .filter_genes(), which existed in both utils-internal.R and
    viz_related.R, with collation order silently deciding which one ran.
  - Removed unreachable helpers .plot_pca(), .plot_mds(),
    .prepare_mds_dataframe(), .prepare_corr_matrix(),
    .plot_corr_heatmap(), and .cluster_log2fc_matrix() along with their
    generated man pages.
  - pkgdown-only articles under vignettes/guides/ and
    vignettes/workflows/ are no longer shipped in the source tarball.

                        Changes in version 1.0.0                        

  - First Bioconductor release (Bioconductor 3.23).

                       Changes in version 0.99.8                        

                       Changes in version 0.99.7                        

                       Changes in version 0.99.6                        

                       Changes in version 0.99.5                        

  - Added read_vista_counts(), read_vista_metadata(), and
    match_vista_inputs() to standardize common RNA-seq input formats
    without changing the existing create_vista() API.
  - Added derive_vista_metadata() to bootstrap starter sample metadata
    from count-derived sample names using split- or regex-based parsing.
  - Added lightweight import support for plain count tables,
    featureCounts, STAR gene counts, HTSeq-count, tximport-like inputs,
    and RSEM gene result files.

                       Changes in version 0.99.4                        

                       Changes in version 0.99.3                        

                       Changes in version 0.99.2                        

                       Changes in version 0.99.1                        

  - example_vista() now uses a precomputed default object to reduce
    example, test, and package-check runtime while preserving the
    existing API.

                       Changes in version 0.99.0                        

Submitted to Bioconductor 2026-02-11

Overview

VISTA (Visualization Toolkit for Transcriptomic Analysis) provides a
unified S4-based framework for differential expression analysis of
RNA-seq data, wrapping DESeq2 and edgeR workflows with consistent
metadata management and rich visualization capabilities.

Key Features

Core Infrastructure

  - S4 VISTA class extending SummarizedExperiment for standardized data
    management
  - Unified differential expression workflow supporting DESeq2 and edgeR
    backends
  - Consistent metadata structure for comparisons, cutoffs, and group
    information
  - Flexible color palette system for visualizations

Visualization Suite (28+ functions)

  - Dimension reduction: PCA, MDS plots with customizable aesthetics
  - DE results: Volcano plots, MA plots, DEG count barplots
  - Expression: Barplots, boxplots, violin plots, density plots,
    joyplots, heatmaps
  - Comparisons: Venn diagrams, alluvial plots, correlation heatmaps,
    pairwise plots
  - Fold-change: Scatter plots, barplots, matrix visualizations,
    chromosome plots

Functional Analysis

  - MSigDB enrichment with flexible ID mapping (SYMBOL, ENSEMBL,
    ENTREZID)
  - GO enrichment analysis (BP, MF, CC ontologies)
  - KEGG pathway enrichment
  - GSEA support with customizable gene sets
  - Integrated visualization functions for enrichment results

Optional Features

  - Cell-type deconvolution via xCell2 integration
  - Automated report generation with Quarto
  - Accessor functions for all metadata components

Implementation Details

  - Comprehensive input validation and edge case handling
  - Extensive test suite (>70% coverage)
  - Complete roxygen2 documentation with runnable examples
  - BiocStyle vignettes demonstrating complete workflows
  - Proper namespace management and import declarations

Bug Fixes

  - Fixed contradictory roxygen documentation markers in internal
    utilities
  - Added missing @importFrom declarations across all modules
  - Improved error messages for invalid inputs
  - Enhanced edge case handling in visualization functions
  - Heatmap utilities now validate non-character genes input explicitly,
    support minimal-call defaults, and allow custom colours for
    multi-column annotations
  - DEG count pie/donut plots now optionally include non-DE genes as an
    "Other" slice and support configurable label text colour
  - get_genes_by_regulation() now supports top-gene ranking by
    abs(log2fc) and optional annotated table output
  - PCA/MDS/UMAP plots now accept the standardized use_group_colors
    argument while keeping use_vista_colors as a deprecated
    compatibility alias