| #!/usr/bin/env bash | |
| set -e | |
| set -o nounset | |
| # This script uses a GTF file (e.g. generated by Stringtie) and | |
| # 1. retrieves BAM files for individual samples from AWS S3 | |
| # 2. quantifies the exons defined in the GTF file with featureCounts, | |
| # e.g. for differential splicing analysis with limma::diffSplice | |
| # | |
| # Alignments are expected to be reverse-stranded and paired-end. |
| # Commented out IPython magic to ensure Python compatibility. | |
| # %%capture | |
| # !pip install awswrangler --quiet | |
| import awswrangler as wr | |
| import pandas as pd | |
| import boto3 | |
| from google.colab import data_table, userdata | |
| data_table.enable_dataframe_formatter() |
| import boto3 | |
| from google.colab import userdata | |
| # refresh the temporary credentials via the key symbol in the left hand tab | |
| s3_resource = boto3.resource('s3', | |
| aws_access_key_id=userdata.get('AWS_ACCESS_KEY_ID'), | |
| aws_secret_access_key=userdata.get('AWS_SECRET_ACCESS_KEY'), | |
| aws_session_token=userdata.get('AWS_SESSION_TOKEN') | |
| ) |
| library(glue) | |
| library(RSQLite) | |
| con <- dbConnect(RSQLite::SQLite(), ":memory:") | |
| var = "test" | |
| glue_sql("{var}", .con = con) # <SQL> 'test' | |
| glue_sql("{`var`}", .con = con) # <SQL> `test` | |
| glue_sql("{DBI::SQL(var)}", .con = con) # <SQL> test (unquoted) | |
| glue_sql("`{var}`", .con = con) # <SQL> `'test'` NOT USEFUL |
| # The following code is based on the shiny::conditionalPanel() help page. The `ui` and `server` | |
| # components have been compartmentalized into the `mod_histogram` module. The `histogramApp` | |
| # function shows an example of using this module. | |
| # Note that the `ns = ns` argument needs to be passed ot the conditionalPanel() call. | |
| library(shiny) | |
| mod_histogram_ui <- function(id){ | |
| ns <- NS(id) | |
| fluidPage( |
| library(glue) | |
| library(httr) | |
| library(jsonlite) | |
| library(xml2) | |
| #' Query ENA's REST API for information about records | |
| #' | |
| #' @param accessions Character vector of one or more ENA record identifiers | |
| #' @return An `xml_document` object | |
| get_records <- function(accessions) { |
| library(palmerpenguins) | |
| library(shiny) | |
| library(shinyjqui) | |
| categories <- colnames(penguins)[vapply(penguins, is.factor, logical(1))] | |
| server <- function(input, output) { | |
| lapply(categories, \(category) { | |
| output[[category]] <- renderPrint({ print(input[[category]]) }) | |
| }) |
| #' Create an image tag with an example image | |
| #' | |
| #' @param width Scalar integer, the width of the image | |
| #' @param height Scalar integer, the height of the image | |
| #' @param title Scalar character, the title of the image | |
| #' @return A `shiny.tag` with the URL to a random image from | |
| #' [Lorem Picsum](https://picsum.photos/) | |
| #' @export | |
| #' @importFrom htmltool tags | |
| #' @importFrom checkmate assert_count assert_character |
There is a lot of confusing information about virtual environments in python out there, in part because the tool chain has evolved over many years.
I decided to follow the advice of Real python and The hitchhiker's guide to python and manage both multiple python versions and multiple virtual environments with pyenv
| --- | |
| title: "Embedding R into Quarto documents with quarto-webr" | |
| subtitle: "Example: intersecting differential expression results" | |
| author: "Thomas Sandmann" | |
| date: '2023/11/18' | |
| format: html | |
| engine: knitr | |
| webr: | |
| show-startup-message: true | |
| packages: ['ggvenn', 'huxtable'] |