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Get started with GeneExprLupusMx

What the package contains and where to start

2026-10-31

Source: vignettes/intro_en.Rmd
intro_en.Rmd

What is GeneExprLupusMx?

GeneExprLupusMx is an R package with transcriptomic (RNA-seq) data from Mexican women with systemic lupus erythematosus (SLE) and from healthy controls. It is designed for research and teaching: besides the data, it includes step-by-step lessons to learn how to analyze them.

  • 33 participants: 10 healthy controls (Ctrl) and 23 patients with lupus (SLE).
  • 3 cell types per participant: monocytes, monocyte-derived dendritic cells (moDC), and tolerogenic dendritic cells (tolDC).
  • 99 samples in total (33 × 3).

The data come from the article:

Hernández-Ledesma AL, Coss-Navarrete EL, et al (2026) Gene expression profiling of dendritic cell tolerance dysfunction in women with systemic lupus erythematosus. Front. Immunol. 17:1771959. doi: 10.3389/fimmu.2026.1771959.

Overview of the study and of the package contents.

Overview of the study and of the package contents.

Installation

Install the package from GitHub. Some lessons also use DESeq2, available on Bioconductor:

install.packages(c("remotes", "BiocManager", "tidyverse"))
remotes::install_github("EveliaCoss/GeneExprLupusMx", build_vignettes = TRUE)
BiocManager::install("DESeq2")

Once installed, load it:

What data does it contain?

The package includes three main datasets. metadata_cleaned and volunteer_info are available as soon as you load the package. filtered_txi is larger, so it is not installed with the package: it is downloaded with download_data() the first time you use it, and then read from your computer.

Object What it contains One row per Size
filtered_txi RNA-seq counts quantified with Salmon and imported with tximport gene 21,718 genes × 99 samples
metadata_cleaned Group, cell type, and clinical data for each sample sample 99 samples
volunteer_info Age, diagnosis, treatments, and recruitment date volunteer 33 volunteers

It also includes reference gene lists used by some lessons:

Object What it contains Used in
biotype_summary Biotype of each gene (protein-coding, lncRNA…) Lesson 4
sle_genes Genes associated with lupus in the literature Lesson 4
isg_genes Interferon-stimulated genes (Yu et al., 2025) Lesson 4
moDC_genes Genes described in the differentiation from monocyte to moDC Lesson 5
dc_genes Genes associated with dendritic cell differentiation Lesson 5
tolDC_genes Transcriptional signature of tolerogenic dendritic cells (Robertson et al., 2021) Lesson 6

It also includes checkpoints: objects that are built in one lesson and saved in the package so that the next lesson can start from there, without repeating the steps. The two largest, dds and zscore_mat, are also downloaded with download_data().

Object What it contains Built in
dds Filtered and normalized DESeqDataSet (13,596 genes × 99 samples); requires DESeq2 Lesson 2
zscore_mat Matrix of z-scores of VST expression, for heatmaps (13,596 genes × 99 samples) Lesson 2
de_results SLE vs Ctrl differential expression results for each cell type (DESeq2) Lesson 3
go_results Enriched functional terms of the DEGs (g:Profiler, version e113) Lesson 4
de_celltype_results Differential expression of moDC and tolDC against monocytes, in controls and patients (DESeq2) Lesson 5
go_celltype_results Enriched functional terms of those DEGs (g:Profiler) Lesson 5
de_interaction_results Group:Cell_type interaction: genes whose change from monocyte to dendritic cell is different in patients (DESeq2) Lesson 7
go_interaction_results Enriched functional terms of those genes (g:Profiler) Lesson 7

A quick look at each one:

filtered_txi <- download_data("filtered_txi") # only downloads the first time
dim(filtered_txi$counts)                        # genes x samples
#> [1] 21718    99
head(metadata_cleaned[, c("sample_ID", "Group", "Cell_type")], 3)
#>         sample_ID Group Cell_type
#> QR011_0   QR011_0  Ctrl  monocyte
#> QR011_1   QR011_1  Ctrl      moDC
#> QR011_3   QR011_3  Ctrl     tolDC
head(volunteer_info[, c("Volunteer_ID", "Group", "Age_years", "Time_of_recruitment")], 3)
#>   Volunteer_ID Group Age_years Time_of_recruitment
#> 1        QR011  Ctrl        25          2022-06-01
#> 2        QR013  Ctrl        23          2022-06-01
#> 3        QR014   SLE        32          2022-06-01

The three tables are connected through their identifiers. Each sample has a name such as QR011_1: the first part (QR011) identifies the volunteer and the final number identifies the cell type (_0 = monocyte, _1 = moDC, _3 = tolDC).

flowchart LR
  T["filtered_txi
counts per gene"] -- "columns = sample_ID" --- M["metadata_cleaned
one row per sample"]
  M -- "Volunteer_ID
(prefix of sample_ID)" --- V["volunteer_info
one row per volunteer"]

# The count columns are in the same order as the metadata rows
all(colnames(filtered_txi$counts) == metadata_cleaned$sample_ID)
#> [1] TRUE
# From sample to volunteer
sub("_.*", "", "QR011_1")
#> [1] "QR011"

To learn about each column in detail, see the help pages: ?filtered_txi, ?metadata_cleaned, and ?volunteer_info.

Learning path

The lessons are designed to be followed in order, but each one can be read on its own.

Lesson You will learn to… Data Time
1. Meeting the participants Describe the cohort with descriptive statistics, tables, normality tests, and comparisons between groups volunteer_info 45 to 60 min
2. From RNA-seq counts to data exploration Import the counts, normalize them with DESeq2, explore genes, and make heatmaps and PCA filtered_txi, metadata_cleaned, volunteer_info 1 to 1.5 h
3. Differentially expressed genes between patients and controls Validate cell types with biomarkers, run differential expression with DESeq2, and interpret volcano plots filtered_txi, metadata_cleaned, zscore_mat 1.5 h
4. Biological functions of differentially expressed genes Run a functional enrichment analysis with g:Profiler, identify SLE and ISG genes, test their enrichment (hypergeometric and Fisher), and correlate ISGs with disease activity de_results, go_results, sle_genes, isg_genes, biotype_summary, zscore_mat, metadata_cleaned, volunteer_info 1.5 h
5. Differentiation of monocytes into dendritic cells Compare dendritic cells against monocytes, validate differentiation with known genes, and explore pathways and lipid metabolism de_celltype_results, go_celltype_results, moDC_genes, dc_genes 1.5 h
6. The transcriptional signature of tolerogenic dendritic cells Evaluate tolDC with a published signature, combine z-scores and LFC, choose the right reference for comparison, and identify the genes that agree with the signature tolDC_genes, de_celltype_results, zscore_mat 1 h
7. Do patient and control cells differentiate in the same way? Fit a model with an interaction, interpret a “difference of differences”, summarize the genes with Reactome processes, and plot expression trajectories de_interaction_results, go_interaction_results, zscore_mat, isg_genes 1.5 h

Each lesson includes challenges with their solutions for practice, and ends by reproducing a figure or table from the article.

In addition, the guide How to use the package functions shows how to apply get_heatmap_mat(), create_heatmap(), process_go() and reduce_go_terms() to your own data.

How to cite

There are two citations: one for the package and one for the data. If you use this resource, please include both.

1) How to cite the package (lessons, functions and R objects):

Coss E (2026) GeneExprLupusMx: Transcriptomic data of lupus in Mexican women with learning lessons. R package. https://github.com/EveliaCoss/GeneExprLupusMx doi: 10.5281/zenodo.23751369.

This DOI always points to the latest version; on Zenodo you can find the DOI of each version.

2) How to cite the data (original article and analysis in Zenodo):

Hernández-Ledesma AL, Coss-Navarrete EL, et al (2026) Gene expression profiling of dendritic cell tolerance dysfunction in women with systemic lupus erythematosus. Front. Immunol. 17:1771959. doi: 10.3389/fimmu.2026.1771959.

Coss-Navarrete EL, Hernández-Ledesma AL, et al (2025) Analysis of gene expression profiles during tolerance induction in dendritic cells from women with Systemic Lupus Erythematosus [Data set]. Zenodo. doi: 10.5281/zenodo.17742206.

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