R programming lesson #2: merging pdf files

Use R package "pdftools" to merge separate pdf into one pdf file. You will never need to use sketchy websites or pay for software to do this ever again! It's free and easy with R package pdftools.

R programming lesson #3: merging datasets (RNA-seq and Human Protein Atlas example)

Lesson goal: take your dataset and add columns from another dataset. You will add columns by matching shared values between the two datasets (usually columns with gene names, Ensembl Gene IDs, or other accession numbers).

Download the following files to folder "data":

1. "Additional File 2: Table S1" Excel file from Gonzalez et al. "Sex differences in the late first trimester human placenta transcriptome", Biology of Sex Differences (2018). It is under section "Additional files". 
2. "proteinatlas.tsv.zip" from The Human Protein Atlas downloadable data page. 
3. Save the Human Protein Atlas page as a pdf (e.g. print to pdf from Chrome browser) or manually record the database version that you are using. It is good practice to document the database version you are using for later citation and methods sections.

Save the files to a data folder that you can access through R Studio. 

Setting up Ubuntu 24.04.x LTS desktop and server, RStudio server, and JypyterLab at home

Why? Set up a Linux server computer on a home network if:

• You want to run code that takes a long time to complete. Let it run on the server computer so you can use your main computer for other things.
• Your main computer is low on storage space. Upload all data files to the server and you can run analysis on the server remotely, e.g. from a laptop while you sit on your couch.
• You want a server specifically for Linux (e.g. for bioinformatics or programming purposes), whereas your main computer is Windows or MacOS.

Who? This tutorial is intended for computer-savvy people with enough patience and knowledge to troubleshoot their own computer by searching the internet for error codes and following instructions. Ideally, you should have some programming experience (beginner level in any language is enough) and know about terminals or command prompt windows, but you can learn what you need as you go. This is a great hands-on way to learn to use Linux. Expert Linux knowledge is not a prerequisite. 

What? Ubuntu Desktop 24.04.x LTS "Noble Numbat" (or the latest version), OpenSSH for server capabilities, RStudio server, and Jupyter Lab. Ubuntu is a specific version of Linux.

Cost? All the software is free. For hardware, you need the following:

How to format final figures for publication

General figure guidelines

File types and file sizes

• TIFF images with LZW compression to reduce the file size
• PDF files for vector images
• Not PowerPoint or Word
• Max 10 MB file size for each full figure (including all subparts)
• Usually I output to .png from R code, create the multi-part figure with Inkscape (free but slow) or Adobe Illustrator (fast but requires license), output as high resolution .png, then use Irfanview (free) to crop out white space and convert to .tiff image.

Resolution

• 300 dpi for microscopy images, gel images, and camera photos
• 600 dpi for plots, graphs, and computer-made images with color
• 1200 dpi for figures that are entirely text and lines
• Resolution is measured at the final size

Bookmarks: single cell RNA-seq tutorials and tools

These are my bookmarks for single cell transcriptomics resources and tutorials. Whenever I find something useful, I add it.

scRNA-seq introductions

How to make R objects for single cell data, e.g. SingleCellExperiment, SummarizedExperiment  

• How to take a spreadsheet with a matrix and convert it to the format needed for many other single cell RNA-seq tutorials, e.g. if you download a .csv.gz file from NCBI GEO

Getting Started with Seurat v4  (Satija lab tutorials list)

• Many tutorials here, for different scRNA-seq goals

Guided clustering tutorial with 3000 PBMC cells

• Setup Seurat object
• Standard pre-processing workflow & quality control
• Data normalization
• Identifying highly variable features (genes)
• Clustering, UMAP/tSNE plots
• Differential gene expression analysis

Basics of single cell analysis with Bioconductor

University of Cambridge intro to single cell RNA-seq analysis

• Identification of low-quality cells using MADs values

Orchestrating Single-Cell Analysis with Bioconductor

 

A Practical Guide to Single-Cell RNA-Seq Cluster Annotation

• "Annotation inherits every upstream decision, so good labels start before labelling. A standard scRNA-seq workflow moves from cell isolation and barcoding to a gene-by-cell count matrix, followed by filtering, normalisation, dimensionality reduction with PCA, and graph-based clustering with Leiden or Louvain on a k-nearest-neighbour graph. If quality control is weak, ambient RNA remains uncorrected, doublets stay in the data, or batch structure dominates, those problems become part of the clusters. Careful labelling cannot rescue a bad clustering structure."

How to get MD5 checksums to detect data corruption (for bioinformatics data curation)

What are MD5 checksums? Checksums are nonsense text strings used to "summarize" a file version. No matter the size of the file (1 kb or 30 GB), the checksum algorithm gives you a conveniently short nonsense string of letters and numbers. The exact same file will give you the exact same checksum every time. If you change a single character or pixel, you will get a different checksum.

MD5 is a specific popular algorithm to get checksums.

Why use checksums? The purpose of checksums is to notice data corruption, especially when downloading files from or uploading files to a server. Every time you transfer files between computers, there is risk of data corruption. For small files, the risk is small and you'll most likely notice, for example if your email attachment download fails due to an internet interruption. 

For large files such as raw sequencing data files, it's a bigger issue and you might not notice right away (or ever) if the last few RNA-seq reads of a >30 million reads file are missing. Therefore, the best practice when downloading new sequencing is to create MD5 checksums yourself and compare them with the MD5 checksum created by the originating computer (the sequencing core's server). They should be the same. If not, something went wrong during file transfer! Try re-downloading the data.

Similarly, when you upload sequencing data to a public repository (e.g. NCBI GEO), you provide MD5 checksums so that the receivers (NCBI's data curators) can confirm the upload was successful.

How to get an MD5 checksum for an individual file? See example below using the Linux terminal. I created a text file containing only the phrase "hello pretend this is sequencing data". The checksum for that file is "b088d8d4d1d831af2d8d16147389aa7d". If I change the first letter to uppercase, the checksum completely changes.

 

Intro to human blood components, cell types, and cell markers

Blood components as separated by density gradient

1. Plasma: yellow layer on top, 55% of blood volume

2. Buffy coat (leukocytes and platelets): white layer in the middle, <1% of blood volume

• White blood cells (leukocytes) = granulocytes (neutrophils, eosinophils, basophils) and PBMCs/agranulocytes (monocytes, dentritic cells, T cells, B cells, NK cells)
• Platelets (thrombocytes) = the blood clotting cells, the smallest blood cell type
• Related: buffy cone refers to the leukocyte-rich waste product from plateletpheresis procedures. It isn't the same as buffy coat , which is a layer from the density gradient, but both are sources of leukocytes.

3. Red blood cells (erythrocytes): red layer at the bottom, 44-45% of blood volume. 

• Adult red blood cells are anucleated (don't have a nucleus). The nucleus is lost during cell maturation.
• Fetal red blood cells do have a nucleus . This affects cell density makes it more difficult to use density gradient medium protocols to separate cord blood into the three layers, compared to adult peripheral blood.

Learn more about blood components and blood cell types:

• Blood (human), MACS Handbook by Miltenyi Biotec. Includes useful figures and tables.
• "What is buffy coat in blood? Buffy coat preparation and buffy coat cell extraction" by Akadeum Life Science
  

Buffy coat components (leukocytes and platelets)