Slice functions across tidyomics

CLAUDE.md

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+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## What this project is
+
+A Quarto-based community blog for the [Tidyomics ecosystem](https://github.com/tidyomics). Posts are authored in `.qmd` (Quarto Markdown) files with R or Python code, rendered locally with frozen outputs committed to `_freeze/`, and deployed to GitHub Pages.
+
+## Build commands
+
+```bash
+quarto render       # Build entire site → _site/
+quarto check        # Validate configuration
+```
+
+In RStudio: open `tidyomicsBlog.Rproj`, then click **Build → Render Website**.
+
+## Writing a blog post
+
+Posts live under `posts/YYYY-MM-DD-slug/index.qmd`. Each post directory may also contain images and other static assets.
+
+**Required front matter:**
+```yaml
+---
+title: "Post Title"
+author: "Author Name"
+date: "YYYY-MM-DD"
+description: "One-sentence summary shown in listings and RSS"
+tags:
+  - tag1
+format:
+  html:
+    toc: true
+execute:
+  freeze: true
+---
+```
+
+**Images:**
+- Compress with `pngquant --ext .png --force my_figure.png` before committing.
+- Every image needs alt text (use Quarto's `fig-alt` option for code-generated figures).
+- You must have rights to publish any image included.
+
+**Code execution (freeze):**
+- Posts set `freeze: true`, so code is run locally and outputs cached.
+- After rendering, commit everything changed inside `_freeze/` along with the `.qmd`.
+- Do **not** commit generated HTML files.
+
+**Licensing:** Text is CC-BY-4.0; code is BSD 3-Clause.
+
+## Contribution workflow
+
+1. Fork the repo and clone locally.
+2. Create the post directory and `index.qmd`.
+3. Render locally (`quarto render` or RStudio Build tab) to populate `_freeze/`.
+4. Commit: the `.qmd`, any static files in the post directory, and `_freeze/` changes.
+5. Open a PR — do not commit `_site/` or generated HTML.
+
+## Repository layout
+
+| Path | Purpose |
+|------|---------|
+| `posts/` | Blog posts (one subdirectory per post) |
+| `posts/_metadata.yml` | Default metadata applied to all posts |
+| `posts/bibliography.bib` | Shared bibliography |
+| `posts-nolist/` | Posts excluded from the listing page |
+| `_freeze/` | Cached computational outputs (committed) |
+| `_site/` | Generated site (gitignored) |
+| `_quarto.yml` | Site-wide Quarto configuration |
+| `.github/workflows/` | CI: renders site, validates RSS, deploys to GitHub Pages |
+
+## CI/CD
+
+GitHub Actions (`.github/workflows/quarto-website.yml`) runs on every push and PR:
+- Renders the site using the committed `_freeze/` cache (no code is re-executed in CI).
+- Validates the RSS feed with `xmllint`.
+- Deploys to GitHub Pages on push to `main`.

posts/2026-06-01-slicing-in-tidyomics/index.qmd

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+---
+title: "Slicing in tidyomics"
+author: "Michael Love"
+date: "2026-06-01"
+tags:
+  - tidyomics
+  - tidyseurat
+  - tidySingleCellExperiment
+  - tidySummarizedExperiment
+  - plyranges
+  - slice
+  - dplyr
+description: "How to use dplyr-style slice operations on omics data objects in the tidyomics project."
+format:
+  html:
+    toc: true
+    toc-float: true
+execute:
+  freeze: true
+---
+
+# Introduction
+
+The tidyverse verb `slice()` lets you select observations by position, returning a subset of the data based on integer indices. There are also some related convenience functions, which return observations by:
+
+  - `slice_min()` and `slice_max()`: the smallest or largest values of a variable.
+  - `slice_head()` and `slice_tail()`: the first or last position(s).
+  - `slice_sample()`: a random subset, either by count or proportion, with optional weighted sampling.
+
+All of these support grouped operations, applying the selection within each group.
+
+In 2023, this family of `slice_*` functions was added to three core _tidyomics_ packages,
+[`tidySummarizedExperiment`](https://github.com/tidyomics/tidySummarizedExperiment) ([commit](https://github.com/tidyomics/tidySummarizedExperiment/commit/5e9bb5f98a650b0d1b42fa32c4593e6e2077a9b7)),
+[`tidyseurat`](https://github.com/stemangiola/tidyseurat) ([commit](https://github.com/stemangiola/tidyseurat/commit/1ff674d7aaee58f99265c8fb1fb8ce3f08efa2ea)), and
+[`tidySingleCellExperiment`](https://github.com/tidyomics/tidySingleCellExperiment) ([commit](https://github.com/tidyomics/tidySingleCellExperiment/commit/e6fc0abd561d6a660286057ae78d579cc7e58412)).
+More recently, in 2026, `slice()` was also introduced to
+[`plyranges`](https://github.com/tidyomics/plyranges) ([commit](https://github.com/tidyomics/plyranges/commit/41eb383b7643b5b03f1956f57c56119d645b3b9d))
+for genomic ranges objects.
+
+This post walks through how slicing works across _tidyomics_ packages.
+
+# tidyseurat
+
+`tidyseurat` lets you work with Seurat objects using familiar tidyverse verbs.
+We'll use `pbmc_small`, a small PBMC dataset bundled with Seurat (80 cells, 230 genes).
+
+```{r}
+#| message: false
+#| warning: false
+library(Seurat)
+library(tidyseurat)
+
+data("pbmc_small")
+seurat_obj <- pbmc_small |>
+  mutate(cell_id = seq_along(.cell)) |>
+  select(-contains("ident")) |>
+  select(-starts_with("RNA"))
+```
+
+With `slice()` you can select cells by position, just as you would rows in a tibble. 
+In Bioconductor packages, and in _Seurat_, cells are represented as columns in the counts matrix.
+
+```{r}
+# First 10 cells
+seurat_obj |> slice(1:5)
+```
+
+The `slice_*` helpers work as expected:
+
+```{r}
+# 20 randomly sampled cells
+seurat_obj |> slice_sample(n = 5)
+
+# Cells with the highest RNA count
+seurat_obj |> slice_max(nCount_RNA, n = 5)
+
+# Cells with the lowest RNA count
+seurat_obj |> slice_min(nCount_RNA, n = 5)
+```
+
+Grouping applies the slice within each group. Here, the top cell by count from
+each sample group:
+
+```{r}
+seurat_obj |>
+  group_by(groups) |>
+  slice_max(nCount_RNA, n=3) |>
+  select(-starts_with("PC"))
+```
+
+# plyranges
+
+`plyranges` extends dplyr-style verbs to Bioconductor `GRanges` objects.
+We construct a small ranges object to demonstrate slicing:
+
+```{r}
+#| message: false
+library(plyranges)
+
+set.seed(123)
+df <- data.frame(
+  start    = 1:50 * 1e6 + 1,
+  width    = 1e4,
+  seqnames = "chr5",
+  strand   = "*",
+  gc       = runif(50),
+  type = factor(sample(LETTERS[1:3], 50, replace = TRUE)),
+  rng_id = 1:50
+)
+rng <- as_granges(df)
+rng
+```
+
+`slice()` selects ranges by index; negative indices drop ranges:
+
+```{r}
+# First two ranges
+rng |> dplyr::slice(c(1,3,5))
+
+# Drop the last range
+rng |> dplyr::slice(-c(1:45))
+```
+
+The `slice_*` helpers work on metadata columns just as they do on data frames.
+The sampling can be in terms of `n` or `prop` (the proportion). Additionally,
+it can accept `weight_by` and a column to be used for weighted sampling.
+
+```{r}
+rng |> slice_head(n = 2)
+rng |> slice_tail(n = 2)
+rng |> slice_max(gc)
+rng |> slice_min(gc)
+rng |> slice_sample(prop = 0.3)
+```
+
+Grouping by type and then slicing:
+
+```{r}
+by_type <- rng |> group_by(type)
+
+# Last range for each type
+by_type |> slice_tail()
+
+# Range with highest GC content for each type
+by_type |> slice_max(gc)
+
+# One range from each type
+by_type |> slice_sample(n = 1)
+```
+
+# tidySE and tidySCE
+
+`tidySummarizedExperiment` (tidySE) and `tidySingleCellExperiment` (tidySCE)
+also gained `slice_*` support in 2023. 
+The functions operate on samples (columns) in the same way as
+`tidyseurat` operates on cells, so the patterns above transfer directly.
+
+# Session information
+
+```{r}
+info <- devtools::session_info()
+pkgs <- info$packages |> as_tibble()
+attached_pkgs <- pkgs |> filter(attached) |> select(package, version=loadedversion)
+# 4. Print directly to your HTML report
+knitr::kable(attached_pkgs, format = "html", row.names = FALSE)
+```