Changelog

Source: NEWS.md

VISTA 1.1.7

Internal

• The test that PCA and MDS plots do not depend on the order genes are supplied in no longer fails when an axis comes back negated. Shuffling the input genes can make prcomp() return the mirror image of the same embedding on some LAPACK/BLAS builds (it did on Linux CI), which the test read as an order dependence. Axes are now oriented with the same canonicalisation the 1.1.4 snapshots use, and a new test checks that a sign flip passes while a moved point still fails. No user-visible behaviour changes; only the test suite is affected.

VISTA 1.1.6

New features

• edgeR’s quasi-likelihood F-test. create_vista(), run_edger_analysis(), benchmark_vista_equivalence() and validate_vista_deep() gain edger_test = c("LRT", "QL"). "QL" runs glmQLFit()/glmQLFTest() (edgeR v4 pipeline), which the edgeR authors recommend for bulk RNA-seq; the likelihood-ratio test treats the dispersion as known and is liberal with few replicates. The test used is recorded in cutoffs(v)$edger_test, and the report config accepts edger_test:; for an edgeR object made before 1.1.6, which does not record the test, the report states the likelihood-ratio test and marks it as inferred. The equivalence check runs its own QL reference. The vignettes now set edger_test.

Default changing in VISTA 1.4.0

• edger_test will default to "QL". It stays "LRT" in this release so that no results change without warning. A call that runs edgeR (method = "edger" or "both") without setting edger_test, or with edger_test = NULL, prints a message, classed vista_default_change and throttled like the deprecation warnings, saying so. Set edger_test = "LRT" to keep current results after the change, or edger_test = "QL" to use the new default now; either silences the message. DESeq2 and limma-voom analyses are not affected. See ?VISTA-deprecated.

Bug fixes

• tximport abundance or lengths could be analysed as counts. read_vista_counts(format = "tximport", counts_from = "abundance") imported TPM, and counts_from = "length" transcript lengths, as if they were counts, and edgeR and limma tested them without complaint. Both now raise an error.

• tximport counts were analysed without transcript-length correction. tximport’s default countsFromAbundance = "no" leaves estimated counts unscaled and expects the DE method to use length offsets, which VISTA does not. Such input that carries abundance and length (also when it does not say how it was scaled) is now converted to length-scaled TPM counts (tximport’s countsFromAbundance = "lengthScaledTPM", recommended when the method does not use offsets), with a message. With tx2gene, transcript-level input is summarised to genes as tximport’s summarizeToGene() does, which rebuilds gene counts from abundance and length even when the transcript counts were already scaled; summing them, as VISTA did, undid the gene-level length correction. The results agree with tximport’s own to within numerical precision (relative differences below 1e-14). abundance and length are matched to counts by name, and missing or negative values, a sample with zero total abundance, or a tx2gene that matches no transcript are clear errors, as is an unrecognised countsFromAbundance value. Without abundance/length, VISTA warns and uses the counts as given. Were you affected? If you imported tximport output made with the default countsFromAbundance = "no", a hand-built list without that field, or transcript-level "lengthScaledTPM"/"dtuScaledTPM" output with tx2gene (scaledTPM sums to the same gene counts); re-run those analyses.

• DESeq2 stopped on estimated counts. Non-integer counts from tximport or RSEM made create_vista(method = "deseq2") fail with “some values in assay are not integers”. DESeq2 now receives them rounded, with a message, as DESeqDataSetFromTximport() does. Filtering is applied to the counts as given, as for edgeR and limma, so method = "both" keeps one set of genes, and its counts(v) holds DESeq2’s rounded counts whichever source is active.

• Values that are not counts could be analysed without comment. All three engines now refuse negative values, and warn (class vista_not_counts_warning) about non-integer values with an unmistakable sign of not being counts: every sample summing to exactly one million (TPM, CPM) or small, almost all fractional values (log scale). These are warnings because no rule on the values alone separates counts from normalised data: estimated counts from libraries of about a million reads must not be refused, and TMM-CPM, FPKM or filtered TPM cannot be recognised. Supply raw or estimated counts. Integer counts are unaffected.

• RSEM TPM or FPKM could be imported as counts. Where count_column selects the values read from RSEM output, only RSEM’s count columns, expected_count (the default) and posterior_mean_count, are now accepted. A merged table whose sample columns are named in sample_names is read as given.

• Untested genes were stored with pvalue = 1. DESeq2 reports NA for genes it does not test (Cook’s-distance count outliers); VISTA replaced that with 1 when it stored the comparison, beside a padj that stayed NA, so an untested gene read as tested and the row disagreed with itself. On airway this affected 51 genes. Tables from comparisons() and those written by export_vista_assets() now show NA, as the engine reported, and so do the per-backend pvalue_deseq2/pvalue_edger columns of a consensus table. Plots and rankings already substituted their own value for a missing p-value, so figures are unchanged.

• Filtering a consensus table on padj returned the union, not the intersection. With consensus_mode = "intersection", a gene called by only one backend is not called, but its padj was that backend’s significant value, contrary to the documented “less-significant of the two”. Selecting padj <= 0.05 on the table, as get_foldchange_scatter() and the volcano thresholds do, therefore picked up every single-backend gene. Such genes now carry the less-significant pvalue and padj of the two backends, and their log2fc follows consensus_log2fc (the mean of the two by default) instead of the calling backend’s estimate, as for every other uncalled gene. On airway this changes log2fc for all 119 single-backend genes (by up to 0.36) and the p-values of most of them. In union mode, where they are called, they keep the calling backend’s values, as before. regulation was always correct and is unchanged.

• DEG-count plots dropped comparisons with no DEGs. get_deg_count_barplot(), get_deg_count_pieplot() and get_deg_count_donutplot() left out a comparison whose summary was all “Other”, asking for it by name aborted with “not found in DEG summary”, and when no requested comparison had any DEG they returned NULL with a warning. Such comparisons are now drawn at zero. Comparison order is unchanged. A summary that uses none of the labels Up, Down or Other (possible with as_vista()) is left out with a warning naming the labels found, rather than drawn as zero.

• limma MA plots put the wrong quantity on the x axis. For limma the table’s baseMean is AveExpr, already log2-CPM, and get_ma_plot() preferred it to the object’s mean expression, so the axis showed log10(log2-CPM + 1). It now uses the object’s mean expression for every engine, as its own comment intended.

• An explicit intercept was not removed for edgeR and limma. For design_formula = "~ 1 + cell + group", VISTA prefixed 0 +, which + 1 then undid, so the model kept its intercept. VISTA now reads the intercept from the formula’s terms. The fitted model spans the same space, so results differ only within numerical precision.

Verification

• benchmark_vista_equivalence() no longer shares code with what it checks. Its reference pipelines used VISTA’s own helpers for sample matching, design construction, contrasts, DEG calls and row alignment, so a defect in any of them was reproduced on both sides and passed – which is how the first and third defects above went unnoticed. The reference now calls only base R, stats and the engines, re-implements the documented contract from scratch (see ?benchmark_vista_equivalence), derives contrasts from the design itself, reads VISTA’s tables through its exported accessors and matches rows by identifier. It compares every numeric column the engine reports, the retained gene set and the set of comparisons; the summary gains gene_sets_identical, comparison_sets_identical, all_columns_within_tolerance and columns_compared. baseMean_within_tolerance is NA for edgeR, which reports no baseMean (it was a vacuous TRUE); a numeric column VISTA stores but the reference does not report (columns_not_in_reference) counts as a mismatch; and a mismatch between a value and NA is reported as an infinite difference instead of 0. The MA, PCA and DEG-count checks now read the data get_ma_plot(), get_pca_plot() and the DEG-count plots draw, and the PCA check compares every sample-sheet column; the volcano check still rebuilds its inputs from the stored table.
• Each method’s reference element has a new shape. Its comparisons are data frames in VISTA’s column names (gene_id, log2fc, stat, pvalue, padj, plus baseMean and lfcSE for DESeq2, logCPM for edgeR, and baseMean (limma’s AveExpr), B, P.Value and adj.P.Val for limma), not the engines’ own tables. row_data is removed (use rowMeans(reference$norm_counts)), group_column is added, and each deg_summary table has exactly two rows, Up and Down.
• New tests check that no reference function uses VISTA code – as a call, as a function value, through VISTA::/VISTA:::, or by name in a string. They inject defects into VISTA’s own helpers that the benchmark must report: a flipped contrast sign, swapped Up/Down calls, values under the wrong gene labels, samples matched to the wrong metadata rows, a dropped covariate, untested genes stored as p = 1, and a comparison dropped from DEG-count plot data. The 1.1.5 check caught two of the first five; those five were chosen to target the helpers it shared, so this shows the new check can fail where the old one could not, not a detection rate.

Other changes

• Objects record the VISTA and R versions and the installed DESeq2, edgeR and limma versions in metadata(x)$provenance, as ?VISTA-class already described. The stored object behind example_vista() was regenerated to carry them.
• ?create_vista documents how a sample sheet without sample_names is matched to count columns, and what the consensus padj is: a conservative ranking score, not an FDR-adjusted p-value of the consensus. In simulations the intersection lowered, but did not control, the single backends’ false discovery rate, and the union raised it.
• The DESCRIPTION now names limma-voom among the engines.
• The code-economy vignette now computes every tally from its own chunks at build time, states its counting rule, attaches what each workflow needs, and checks that both workflows give the same DESeq2 results.
• min_counts and min_replicates in benchmark_vista_equivalence() and validate_vista_deep() are now described as they behave: min_replicates counts samples across the whole experiment, not per group.

VISTA 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.

VISTA 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.

VISTA 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.

VISTA 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.

VISTA 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.

VISTA 1.0.0

• First Bioconductor release (Bioconductor 3.23).

VISTA 0.99.8

VISTA 0.99.7

VISTA 0.99.6

VISTA 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.

VISTA 0.99.4

VISTA 0.99.3

VISTA 0.99.2

VISTA 0.99.1

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

VISTA 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

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