vjcitn

#' produce an ontology_index instance from semantic sql sqlite connection
#' @param con DBI::dbConnect value for sqlite table
#' @return result of ontologyIndex::ontology_index evaluated for the labels and 
#' parent-child relations in tables statements and edge of the semantic sql resource
#' @export
semsql_to_oi = function(con) {
# sqlite> select * from statements where predicate = 'rdfs:label' limit 40;
 alltabs = DBI::dbListTables(con)
 stopifnot(all(c("edge", "statements") %in% alltabs))
 labdf = dplyr::tbl(con, "statements") |> dplyr::filter(predicate == "rdfs:label") |>

vjcitn

library(shiny)
library(imager)
mpath = "https://mghp.osn.xsede.org/bir190004-bucket01/BiocGeneralData/mast.jpg"
happ = function () {
  ui = fluidPage(
   sidebarLayout(
   sidebarPanel(
    helpText("slider for cannyEdges alpha"), 
    sliderInput("alpha", "alpha", min = 0, max = 1, step = 0.05, 

vjcitn

library(shiny)
colorapp2 = function () 
{
    colordot = function (r, g, b) {
        plot(0, 0, col = grDevices::rgb(r, g, b), pch = 19, cex = 15)
    }

    ui = fluidPage(
          sidebarLayout(
           sidebarPanel(

vjcitn

FROM ubuntu:jammy

LABEL org.label-schema.license="GPL-2.0" \
      org.label-schema.vcs-url="https://github.com/rocker-org/" \
      org.label-schema.vendor="Rocker Project" \
      maintainer="Dirk Eddelbuettel <edd@debian.org>"

## Set a default user. Available via runtime flag `--user docker`
## Add user to 'staff' group, granting them write privileges to /usr/local/lib/R/site.library
## User should also have & own a home directory (for rstudio or linked volumes to work properly).

vjcitn

# this code will process 4k PBMCs from TENx with SingleR and scater
# compute some approximate PCs and produce an interactive scatterplot
# in PC space with a biplot
ii = rownames(installed.packages())
if (!("BiocManager" %in% ii))install.packages("BiocManager")
req = c("celldex", "TENxPBMCData", "SingleR", "irlba", "scater",
   "scales", "ggplot2", "devtools", "plotly")
needed = setdiff(req, ii)
if (length(needed)>0) BiocManager::install(needed, ask=FALSE, update=FALSE)
library(celldex)

vjcitn

filtered_biplot = function (prcomp_output, sce, sampvar = "Barcode", colorvar = "label.main", 
    which = c(1, 2), nvar = 5, shr = 0.6, ...) 
{
    rownames(prcomp_output$x) = sce[[sampvar]]
    rownames(prcomp_output$rotation) = rownames(sce)
    proj = prcomp_output$x[, which]
    rot = prcomp_output$rot[, which]
    sss = function(x) sum(x^2)
    lens = apply(rot, 1, sss)
    kprot = rot[order(lens, decreasing = TRUE)[1:nvar], ]

vjcitn

> r1 = run_cifar100()  # pip3 install tensorflow; BiocManager::install("vjcitn/littleDeep", dependencies=TRUE)
No non-system installation of Python could be found.
Would you like to download and install Miniconda?
Miniconda is an open source environment management system for Python.
See https://docs.conda.io/en/latest/miniconda.html for more details.

Would you like to install Miniconda? [Y/n]: n
Installation aborted.
2023-04-29 18:14:44.950515: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.

vjcitn

# process .bib file into a batch query to pubmed (https://pubmed.ncbi.nlm.nih.gov/batchcitmatch/)

# get first page
fixp = function(x) gsub("--.*", "", x)

# produce a batch query
build_query = function (x) 
paste(x$journal, x$year, x$volume, fixp(x$pages), x$author[[1]]$family, 
    paste0(x$author[[1]]$family, x$year), sep = "|")

vjcitn

as_renv_entry = function(x) { 
  stopifnot(all(c("Package", "git_url", "git_last_commit") %in% names(x))) 
  class(x) = c("renv_entry", class(x)) 
  x 
  }

print.renv_entry = function(x, ...) { 
  cat(sprintf("renv entry for %s %s\n", x$Package, x$git_branch)) 
  }

vjcitn

#' produce matrices of AD values from VcfFile instance for a given genomic interval
#' @param vf instance of VcfFile, should be tabix indexed
#' @param rng compatible GRanges instance
#' @param genome character(1) obligatory for readVcf
#' @return list with matrices allele1 and allele2, similar to the AD matrix, and ref and alt as obtained directly
#' @examples
#' x = VariantAnnotation::VcfFile("ALL.chrX.BI_Beagle.20100804.genotypes.vcf.gz")
#' param = GRanges("X", IRanges(60000, width=10000))
#' m = ad_mats(x, param)
#' m$allele1[1:4,1:10]