Package: phiper 0.4.1

phiper: Automated PhIP-seq Analysis and Reporting

Provides an end-to-end toolkit for Phage ImmunoPrecipitation- sequencing (PhIP-seq) data. Functions import raw peptide-sample count matrices, apply quality control filters, normalise library sizes, compute enrichment statistics and diversity metrics, and identify differentially enriched peptides or motifs. Results can be explored through tidy data frames, visualised with publication-ready ggplot2 graphics, or rendered into fully fledged HTML reports via R Markdown.

Authors:Mateusz Kolek [aut, cre, cph], Alon Alexander [ctb, cph], Thomas Vogl [cph, fnd]

phiper_0.4.1.tar.gz
phiper_0.4.1.zip(r-4.7)phiper_0.4.1.zip(r-4.6)phiper_0.4.1.zip(r-4.5)
phiper_0.4.1.tgz(r-4.6-x86_64)phiper_0.4.1.tgz(r-4.6-arm64)phiper_0.4.1.tgz(r-4.5-x86_64)phiper_0.4.1.tgz(r-4.5-arm64)
phiper_0.4.1.tar.gz(r-4.7-arm64)phiper_0.4.1.tar.gz(r-4.7-x86_64)phiper_0.4.1.tar.gz(r-4.6-arm64)phiper_0.4.1.tar.gz(r-4.6-x86_64)
phiper_0.4.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
phiper/json (API)

# Install 'phiper' in R:
install.packages('phiper', repos = c('https://polymerase3.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/polymerase3/phiper/issues

Pkgdown/docs site:https://polymerase3.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

5.65 score 2 stars 33 scripts 37 exports 83 dependencies

Last updated from:688367ea19 (on main). Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
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linux-devel-x86_64OK265
source / vignettesOK768
linux-release-arm64OK290
linux-release-x86_64OK306
macos-release-arm64OK232
macos-release-x86_64OK350
macos-oldrel-arm64OK223
macos-oldrel-x86_64OK435
windows-develOK298
windows-releaseOK370
windows-oldrelOK278
wasm-releaseOK159

Exports:compute_alphacompute_alpha_significancecompute_capscalecompute_deltacompute_dispersioncompute_distancecompute_pcoacompute_pcoa_feature_associationscompute_permanovacompute_popcompute_tsnedeltaplotdeltaplot_interactiveecdf_plotecdf_plot_interactiveforestplotforestplot_interactiveget_example_pathload_example_dataphip_paletteplot_alphaplot_alpha_interactiveplot_alpha_significanceplot_capplot_dispersionplot_enrichment_countsplot_pcoaplot_screeplot_tsnescale_color_phipscale_colour_phipscale_fill_phipscatter_interactivescatter_statictheme_phipvolcano_interactivevolcano_static

Dependencies:askpassbase64encblobbslibcachemchkcliclustercpp11crosstalkcurldata.tableDBIdbplyrdigestdplyrduckdbevaluatefarverfastmapfontawesomefsgenericsggplot2gluegtablehighrhtmltoolshtmlwidgetshttrisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixmemoisemgcvmimenlmeopensslotelpermutephiperiopillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcppRcppParallelrlangrmarkdownRtsneS7sassscalesshowtextshowtextdbstringistringrsyssysfontstibbletidyrtidyselecttinytexutf8vctrsveganviridisLitewithrxfunyaml

Beta Diversity Analysis in PhIP-seq
Overview | Setup | Step 1 — Distance matrix | Choosing a distance and normalisation | Step 2 — Unconstrained ordination (PCoA) | Computing PCoA | Scree plot | Feature associations | Plotting PCoA | Basic plot — colour by group | Adding a time factor | Centroids and centroid trajectories | Confidence ellipses | Plotting alternative axes | Step 3 — Constrained ordination (CAP / db-RDA) | Plotting CAP | Step 4 — PERMANOVA | Step 5 — Beta dispersion | Plotting dispersion | Group dispersion | Time dispersion | Step 6 — t-SNE | 2D plot | 3D interactive plot | Putting it all together | Session info
Rendered frombeta-diversity.Rmdusingknitr::rmarkdown

Last update: 2026-04-15
Started: 2026-04-15

Delta Analysis in PhIP-seq
Overview | Setup | Visualising prevalence shift | deltaplot() — static ggplot2 | Adjusting the smooth | Jitter and appearance | deltaplot_interactive() — plotly | Testing global shift: compute_delta() | Unpaired design — group comparison | What's in the output? | Choosing the per-peptide statistic | Aggregation and stratification | Weighting | Paired design — timepoint comparison | Forest plots | forestplot() — static ggplot2 | Diverging colours | Filtering to significant features | forestplot_interactive() — plotly | Putting it all together | Session info
Rendered fromdelta-analysis.Rmdusingknitr::rmarkdown

Last update: 2026-04-15
Started: 2026-04-15

Prevalence of Presence (POP) Analysis in PhIP-seq
Overview | Setup | Computing prevalence: compute_pop() | Unpaired design — group comparison | What's in the output? | Multiple group columns | Multiple ranks | k-of-n presence threshold | Paired design — timepoint comparison | Scatter plots | scatter_static() — ggplot2 | scatter_interactive() — plotly | Volcano plots | volcano_static() — ggplot2 | Cutoffs | BH correction | volcano_interactive() — plotly | Putting it all together | Session info
Rendered frompop-analysis.Rmdusingknitr::rmarkdown

Last update: 2026-04-15
Started: 2026-04-15

Alpha Diversity Analysis in PhIP-seq
Overview | Setup | Computing alpha diversity | Basic usage: peptide-level diversity by group | What's in the output? | Multiple grouping columns | Multiple ranks | Group interactions | Selecting a metric subset | Shannon base | Presence modes | mode = "binary" (default) | mode = "threshold" | mode = "abundance" | Accessing result attributes | Visualising alpha diversity | plot_alpha() — static boxplots | All five metrics | Faceting across multiple ranks | Filtering and ordering groups | Customising appearance | Plotting the interaction table | plot_alpha_interactive() — plotly | Statistical testing | Default: Kruskal-Wallis + Wilcoxon | Global test | Pairwise comparisons | Choosing the test | p-value adjustment | Restricting to a subset of metrics | Multi-rank significance | Visualising significance | Table mode | Heatmap mode | Heatmap across metrics | Significance brackets on boxplots | Putting it all together | Session info
Rendered fromalpha-diversity.Rmdusingknitr::rmarkdown

Last update: 2026-03-28
Started: 2026-03-28

Readme and manuals

Help Manual

Help pageTopics
Compute alpha diversity per sample / group across rankscompute_alpha
Compute statistical significance of alpha diversity between groupscompute_alpha_significance
Constrained Ordination (db-rda / cap) on Distance Matrixcompute_capscale
Global Shift in Peptide-level Prevalence via Subject-level Permutationcompute_delta
Test Homogeneity of Dispersion (Beta Dispersion)compute_dispersion
Compute Pairwise Sample Distancescompute_distance
Principal Components Analysis (PCoA) on a Distance Matrixcompute_pcoa
Compute Feature Associations to PCoA Vectorscompute_pcoa_feature_associations
PERMANOVA with Global and Post-hoc Tests on Beta Diversitycompute_permanova
Prevalence comparison by group (POP framework)compute_pop
Compute t-SNE Embeddings for Sample Distancescompute_tsne
Delta-prevalence vs Pooled Prevalencedeltaplot
Interactive Delta-prevalence vs Pooled Prevalencedeltaplot_interactive
ECDF of Per-peptide Prevalences for Two Groupsecdf_plot
ECDF of Per-peptide Prevalence for Two Groupsecdf_plot_interactive
Forest Plot of Top/Bottom Raw Stouffer T by Rankforestplot
Interactive Forest Plot of Top/Bottom DELTA/Stouffer Statisticsforestplot_interactive
Path to Example PhIP-Seq Datasetsget_example_path
Load Example PhIP-Seq Dataset as <phip_data>load_example_data
PHIP default colour palettephip_palette
Plot alpha diversity (richness/Shannon/Simpson) from precomputed resultsplot_alpha
Plot alpha diversity (precomputed) — interactive (plotly)plot_alpha_interactive
Visualise alpha diversity significance resultsplot_alpha_significance
Plot CAP/db-RDA Results (Constrained Ordination)plot_cap
Plot beta dispersion (distance to centroid)plot_dispersion
Plot enrichment counts per group (and optional interaction)plot_enrichment_counts
Plot Principal Coordinates Analysis (PCoA)plot_pcoa
Scree Plot for PCoA Eigenvaluesplot_scree
Plot t-SNE embeddingsplot_tsne
Discrete colour scale using the PHIP palettescale_color_phip scale_colour_phip
Discrete fill scale using the PHIP palettescale_fill_phip
Interactive prevalence scatter for prevalence resultsscatter_interactive
Static scatterplot of percent1 vs percent2 from 'compute_pop()'scatter_static
Theme 'theme_phip'theme_phip
Interactive volcano plot (log2 ratio vs -log10 p)volcano_interactive
Static volcano plot (log2 ratio vs -log10 p)volcano_static