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text = "Voyager to SpatialFeatureExperiment (SFE) aims to be analogous scater to SFE, implementing basic exploratory spatial data analysis (ESDA) and plotting. SFE inherits from SCE and SpatialExperiment (SPE), so all methods written for SCE and SPE can be used for SFE as well.

In this first version, ESDA is based on the classic geospatial package spdep, but future versions will incorporate methods from GWmodel, adespatial, and etc.

These are the main functionalities of the Voyager at present:

Univariate global spatial statistics, such as Moran’s I, Geary’s C, permutation testing of I and C, correlograms, global G, and semivariogram.
Univariate local spatial statistics, such as local Moran’s I, local Geary’s C, Getis-Ord Gi*, Moran scatter plot, and local spatial heteroscedasticity (LOSH).
Multivariate spatial statistics, such as MULTISPATI PCA and a multivariate generalization of local Geary’s C.
Bivariate spatial statistics, such as Lee’s L (global and local) and cross variograms.

vjcitn

library(tQTLExperiment)
data(mageSEfilt, package="CSHLvc2026")
plink_paths <- cache_mage_chr17_plink()
##  [ cache hit   ] CCDG_mage_chr17.fam
##  [ cache hit   ] CCDG_mage_chr17.bim
##  [ cache hit   ] CCDG_mage_chr17.bed
##  [ validate    ] Checking .bed magic bytes ...
##  [ validate    ] .bed magic bytes OK.
plpre <- tools::file_path_sans_ext(plink_paths[["bed"]]) # works but not a list!
cd <- as.data.frame(colData(mageSEfilt))

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#' Validate PLINK .bed magic bytes
#'
#' @param path Character. Local path to the .bed file.
#' @return Invisibly returns \code{TRUE}; stops on failure.
#' @keywords internal
.validate_bed_magic <- function(path) {
    con <- file(path, open = "rb")
    on.exit(close(con))
    magic    <- readBin(con, what = "raw", n = 3L)
    expected <- as.raw(c(0x6c, 0x1b, 0x01))

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processing file: bkg-python-interoperability.qmd
1/41
2/41 [unnamed-chunk-1]
3/41
4/41 [libraries]
5/41
6/41 [venv-]
7/41
8/41 [req]
9/41

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an instance of the Orchestrating Spatial Transcriptomics Analysis book

Jun 8 2026: use us.gcr.io/broad-dsp-gcr-public/terra-base:1.0.1 to have sudo capabilities.

To install the infrastructure, et a machine with 4 CPUs and 150 GB disk.

We use r2u to get packages and ubuntu infrastructure. Specifically, we use the script

#!/bin/bash

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#' sfe_efa_bridge.R
#'
#' Bridge between SpatialFeatureExperiment cell boundary polygons
#' and Elliptic Fourier Analysis (Momocs).
#'
#' Extracts cell segmentation polygons from an SFE object,
#' computes EFA coefficients per cell, stores them as a
#' reducedDim or colData, and provides reconstruction/visualization.
#'
#' Dependencies:

vjcitn

# Set seed for reproducibility
set.seed(123)

# Generate 15 uniform points in the unit circle
n <- 15

# Method: Generate points uniformly in unit circle using polar coordinates
# We need to use square root to ensure uniform distribution
r <- sqrt(runif(n))  # Square root to get uniform distribution
theta <- runif(n, 0, 2*pi)

vjcitn

library(shiny)
library(plotly)
library(SpatialData)
library(SpatialData.plot)
library(ggplot2)

load("abund500.rda")
gene_choices <- sort(abund500)
dd <- readSpatialData("data.zarr", images = FALSE)

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> library(ellmer)
1/4 packages newly attached/loaded, see sessionInfo() for details.
> ch = chat_ollama(model="qwen3-coder:30b")
> ch$chat("Discuss how to evaluate the integral of the Gaussian density function over the whole real line.")
# Evaluating the Gaussian Integral over the Entire Real Line

The integral of the Gaussian density function over the whole real line is a fundamental result in mathematics and 
statistics:

$$\int_{-\infty}^{\infty} e^{-x^2/2} dx = \sqrt{2\pi}$$

vjcitn

library(littleDeep)
library(processx)
tf = tempfile()
fstr = paste0(">>", tf)
#file.create(tf)
basecmd = sprintf("/Users/vincentcarey/INSTRUMENTATION/macmon/target/release/macmon")
pp = process$new(basecmd, c("pipe"), stdout=fstr)
suppress_keras_warnings()
r1 = run_cifar100(nEpochs=3)
pp$kill()