In vjcitn/biocwk312: Workshop intro for Bioconductor 3.12.

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Demo doc template for the Bioconductor 3.12 workshops at Harvard

Authors: Vince Carey^[Channing Division of Network Medicine, Brigham and Women's Hospital], Another Author^[Another Institution].
Last modified: 1 Nov, 2020.

Overview

Description

This document is a technical illustration of how workshop documents are authored and rendered. The content of this document will change completely as the workshop content is specified.

Pre-requisites [should be specd for each workshop]

• Basic knowledge of R syntax
• Familiarity with Rstudio
• Basic understanding of modern genomics. For example, the distinction between whole genome sequencing and RNA-seq should be clear.
• Basic familiarity with the concepts of statistical analysis, such as the definition of the t-test for comparing sample means, the interpretation of histograms. An understanding of experimental design is helpful.
• Readings:

Participation

Describe how students will be expected to participate in the workshop.

R / Bioconductor packages used

List any R / Bioconductor packages that will be explicitly covered.

Time outline

An example for a 45-minute workshop:

| Activity | Time | |------------------------------|------| | Brief intro to R/Rstudio | 10m | | Biological context | 10m | | Packages to be used | 10m | | Analytical approach to the question | 15m | | Simple exercises | 10m | | Review | 5m |

Workshop goals and objectives

List "big picture" student-centered workshop goals and learning objectives. Learning goals and objectives are related, but not the same thing. These goals and objectives will help some people to decide whether to attend the conference for training purposes, so please make these as precise and accurate as possible.

Learning goals are high-level descriptions of what participants will learn and be able to do after the workshop is over. Learning objectives, on the other hand, describe in very specific and measurable terms specific skills or knowledge attained. The Bloom's Taxonomy may be a useful framework for defining and describing your goals and objectives, although there are others.

Learning goals

Some examples:

• describe how to...
• identify methods for...
• understand the difference between...

Learning objectives

• analyze xyz data to produce...
• create xyz plots
• evaluate xyz data for artifacts

Workshop Content

suppressPackageStartupMessages({
  library(TnT)
  library(knitr)
  library(tibble)
  library(biocwk312)
})

Thinking about tables

In the following chunk, we retrieve and render a table produced in @Lambert2018.

lamtab = biocwk312::lamb_main_20201101
kable(lamtab[1:5,])

When rendered in HTML, the table is searchable, thanks to 'DT::datatable'.

Note that each transcription factor is accompanied by an Ensembl identifier.
We 'wrap' the identifiers to create a hyperlink, and then render using the 'escape=FALSE' setting for 'datatable'.

usw_pref = "https://uswest.ensembl.org/Homo_sapiens/Gene/Summary?g="
wr_ens = function(x, pref) {
 paste0("<A href='", pref, x, "'>", x, "</A>")
}
head(wr_ens(lamtab$ID, usw_pref)) # test
lamtab2 = lamtab
lamtab2$ID = wr_ens(lamtab2$ID, usw_pref)
DT::datatable(lamtab2, escape=FALSE)

This is all taken care of for the user of the TFutils package in the function 'browse_lambert_main()', which also includes links for PubMed IDs scattered in Lambert's published Excel table.

Exercise: How many records mention transcription factor 'YY1'?

Thinking about visualization

Visualization of genomic data is closely linked to annotation. Annotation can be unwieldy and cumbersome to interrogate. We have collected some slices of genomic data and annotation to help with exploration of new approaches to interactive visualization in reports.

Reference positions of transcripts. We have information on a selection of transcripts on chromosome 17 in the vicinity of gene ORMDL3.

head(biocwk312::txdata_near_ormdl3,3)

Positions and annotation of GWAS hits. We used the gwascat package to get a copy of the EBI/EMBL GWAS catalog on 1 November 2020, and limited the information to records pertaining to the interval 38-43 Mb on chr17. We also limited the number of metadata fields on the hits.

names(S4Vectors::mcols(biocwk312::hits_near_ormdl3_trunc10))
head(biocwk312::hits_near_ormdl3_trunc10, 3)

An interactive visualization of GWAS hits and nearby genes.

We'll use the TnT package along with some functions built for the workshop to visualize GWAS hit locations in the context of transcripts.

The display below is interactive. Clicking the mouse or trackpad near the middle will zoom in. A mouse wheel will also control zoom. At greater magnifications, annotations will be displayed, or you can click on a feature to get textual annotation. You can drag the display left or right after magnification. To restore initial state, reload the page in the browser.

# first setup transcripts
trxview = TxTrackFromGRanges(biocwk312::txdata_near_ormdl3, 
      label = "Transcript\n Structure",
      color = "grey2",height = 300)
trxview = reset_tooltip(trxview)
trxview = reset_color(trxview)
trxview = reset_display_label(trxview)
# second setup GWAS hits
hitview = TnT::PinTrack(biocwk312::hits_near_ormdl3_trunc10, color="blue")
# render
TnTGenome(list(hitview, trxview), view.range=GRanges("17", IRanges(39.5e6, 40.5e6)))

Exercise: There is a single GWAS hit on chr17 between positions 39,932,000 and 39,934,000. Use zoom and drag to find it. What is the rs number for the SNP, what is the disease/trait with which this SNP is associated, and what is the risk allele frequency? These can be determined by clicking on the head of the blue pin plotted in the interval given.

References

vjcitn/biocwk312 documentation built on Jan. 1, 2021, 12:41 p.m.