Our consultation sessions are designed for you to spend your time as you would like with the support of your instructors.

You can review instruction materials, work through exercise notebooks we provide, or analyze your own data.

On this page, we’ve assembled some resources you may find helpful during these sessions. For more information about the structure of consultation sessions and how to get help, please review the Consultation sessions section of the Workshop Structure page.

Table of contents

Module cheatsheets

The modules-cheatsheets directory of our GitHub repository of training materials contains Markdown and PDF version of “cheatsheets” that contain tables with short descriptions of functions used throughout training modules and links to documentation.

You may find these helpful as you review instruction material or work through exercise notebooks.

Working with your own data on RStudio Server

If you plan on working with your own data during consultations, you may find it helpful to leverage our RStudio Server.

You can find instructions for working with your own data on RStudio Server here. Please read these instructions carefully.

We’ll reiterate some of the most important points from those instructions below:

  • As a rule of thumb, if the data you are working with would be released under controlled access, rather than made publicly available, at the time of publication of a scientific manuscript, it should not be uploaded to our RStudio Server.
  • You have 50GB of space available. If your data is larger than 50GB, please contact an instructor.

Obtaining practice datasets


The Childhood Cancer Data Lab built and maintains refine.bio, resource of uniformly processed transcriptomic data obtained from publicly available sources. You can read more about how we process data in refine.bio in our documentation.

If you’d like to practice some of the skills we cover in training or gain some additional ones like making highly customizable heatmaps with the ComplexHeatmap R package, obtaining processed data from refine.bio is a great starting point. You may find our examples for working with data from refine.bio helpful as you look to practice and expand your skills. In those examples, we use R Notebooks, which you will be familiar with from this workshop! See the “Getting Started” section for more information on utilizing our example notebooks.

You can start by searching refine.bio for keywords relevant to your scientific questions and filtering to the organism and technology (e.g., microarray vs. RNA-seq; refine.bio contains both) you’re interested in.

Microarray data on refine.bio

In this version of our workshop, we won’t work with microarray data, but there are hundreds of thousands of microarray samples available from refine.bio. The microarray datasets you can download from the refine.bio web interface are quantile normalized and are distributed as TSV files you can read into R using functions we cover in training. The metadata is included in your download in a TSV file that starts with metadata_. You may find our microarray example notebooks for working with refine.bio data helpful with your differential expression, dimension reduction, or GSEA pathway analyses, to name a few. Note that our training material is largely RNA-seq specific, so if you obtain microarray data from refine.bio, you should not expect to use the exact same code as we do in training.

Bulk RNA-seq data on refine.bio

The format of the RNA-seq data you can download from the web interface of refine.bio data will be slightly different from what we cover in training. We summarize our data to the gene-level with tximport (docs), instead of tximeta like we do in training, before you download it. When downloading your data from refine.bio, we recommend checking the box that says “Skip quantile normalization for RNA-seq samples” to obtain the non-quantile normalized data (docs). You will receive a TSV file that you can use as the counts matrix input for a DESeqDataSet. Note that we recommend using non-quantile normalized data as the DESeqDataSetFromMatrix() function requires a counts matrix and not a matrix with normalized or corrected value like TPMs. See this nice DESeq2 vignette for more information (Love et al., 2014). You can read more about using DESeq2 with refine.bio data here.

If you identify an RNA-seq experiment from refine.bio that you’d like to use with DESeq2 (specifically with DESeqDataSetFromMatrix()), you can begin by following the instructions in the “Obtain the dataset from refine.bio” section of any of our RNA-seq refinebio example notebooks and continue following the steps up until the “Create a DESeqDataset” section, as these steps remain pretty much the same across notebooks. Note that you will also need the associated metadata file, which is included in your download in a TSV file that starts with metadata_, to create a DESeqDataSet object.

Single-cell RNA-seq data

The CCDL does not currently have a repository of single-cell RNA-seq data that we can point you to for practice data sets. However, we do have a couple of sources of data that you might find useful to practice with.

Tabula Muris data

The first is a more extensive set of the Tabula Muris data (mouse tissue) that we worked with in the example datasets. These samples, already processed by salmon alevin, can be found in the ~/shared-data/training-data/tabula-muris/alevin directory. Metadata, including tissue of origin for each sample (since the sample names themselves are not informative), can be found in ~/training-modules/scRNA-seq/data/tabula-muris/TM_droplet_metadata.csv. Note that this data is given at the cell level: simplifying the table to the sample level is a good opportunity to practice some data wrangling skills! (It is also a CSV file; don’t forget to use readr::read_csv() when loading it!)

Human Cell Atlas data

Another potential source for processed single cell data is the Human Cell Atlas (HCA) Data Portal. The data here is from a mix of technologies, including both 10X, Smart-seq2, and DropSeq. The HCA has standardized processing pipelines for 10X and Smart-seq2, though it seems that most of the processed data is 10X, so we recommend focusing on those projects.

To download a data set, first browse or search to find a project of interest. Click on the project name to see an abstract and other information for the project.

You can then select “Project Matrices” from the left side to download the processed single-cell expression data. Scroll down to the “DCP Generated Matrices” section on the “Project Matrices” page, as the data here will be uniformly processed and in a standard data format. That format is called loom, and we can read it into R in a fairly straightforward way. Once you find a loom file listed (not all projects have one, unfortunately), you have two options:

  1. Click the “Copy download link” button (the tiny clipboard icon) and then use that URL to download the file directly to the RStudio server following these instructions. Be sure to put quotes around the very long URL that is provided, and specify a filename for the download with the -O option.

  2. Download the loom file to your computer (look for the tiny icon with the arrow pointing down) and upload it to the server following these instructions.

Reading loom format data in R

Once you have a .loom file on the server, you can use the following commands in R to import the data as a SingleCellExperiment-compatible object.

loomfile <-  file.path("path", "to", "file.loom")
sce <- LoomExperiment::import(loomfile, type = "SingleCellLoomExperiment")

# the first assay matrix should be named "counts"
assayNames(sce)[1] <- "counts"

The last command is to be sure that the main data matrix, which contains count data, has the name that the SingleCellExperiment commands expect.

The gene and cell identifiers are stored in rowData and colData respectively, but those identifiers aren’t used as row names and column names. To make the format a little closer to what we work with during instruction (and so we can visualize individual genes), we need to do the following:

rownames(sce) <- rowData(sce)$Gene
colnames(sce) <- colData(sce)$CellID

Once that is done, all of the SingleCellExperiment commands that we have demonstrated should work! You will want to be sure to look at rowData() and colData(), as some of the contents will be different from what we have seen in previous data sets (and may vary among projects). Some of the QC calculations may have already been performed, but the data will not be filtered or normalized. You will need to perform those steps on your own.

Transcriptome indices for common organisms

During the introduction to bulk RNA-seq module, we used human data and included a transcriptome index for human in training-modules/RNA-seq/index/.

If you have non-human RNA-seq data you would like to quantify, or want to experiment with slightly different index parameters, we have prepared indices for select organisms relevant to the study of childhood cancer. Note that for most of these, you will need to perform a few extra steps to read in the quantification data with tximeta after performing quantification. Please see the notebook RNA-seq/00c-tximeta_other_species.Rmd for details on how to set this up.

If you have RNA-seq data for an organism that is not listed, please post in the training-specific Slack channel and let your instructors know.

Homo sapiens

Ensembl GRCh38 (hg38) v95

File description File use File path
Human Salmon index -k 23 Salmon index for use with salmon quant; appropriate for reads shorter than 75bp or for increased sensitivity with --validateMappings (docs) ~/shared-data/reference/refgenie/hg38_cdna/salmon_index/short
Human Salmon index -k 31 Salmon index for use with salmon quant; appropriate for reads 75bp or longer (docs) ~/shared-data/reference/refgenie/hg38_cdna/salmon_index/long

Mus musculus

Ensembl GRCm38 (mm10) v95

File description File use File path
Mouse Salmon index -k 23 Salmon index for use with salmon quant; appropriate for reads shorter than 75bp or for increased sensitivity with --validateMappings (docs) ~/shared-data/reference/refgenie/mm10_cdna/salmon_index/short
Mouse Salmon index -k 31 Salmon index for use with salmon quant; appropriate for reads 75bp or longer (docs) ~/shared-data/reference/refgenie/mm10_cdna/salmon_index/long

Danio rerio

Ensembl GRCz11 v95

File description File use File path
Zebrafish Salmon index -k 23 Salmon index for use with salmon quant; appropriate for reads shorter than 75bp or for increased sensitivity with --validateMappings (docs) ~/shared-data/reference/refgenie/z11_cdna/salmon_index/short
Zebrafish Salmon index -k 31 Salmon index for use with salmon quant; appropriate for reads 75bp or longer (docs) ~/shared-data/reference/refgenie/z11_cdna/salmon_index/long

Canis lupus familiaris

Ensembl CanFam3.1 v95

File description File use File path
Dog Salmon index -k 23 Salmon index for use with salmon quant; appropriate for reads shorter than 75bp or for increased sensitivity with --validateMappings (docs) ~/shared-data/reference/refgenie/CanFam3p1_cdna/salmon_index/short
Dog Salmon index -k 31 Salmon index for use with salmon quant; appropriate for reads 75bp or longer (docs) ~/shared-data/reference/refgenie/CanFam3p1_cdna/salmon_index/long