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Data download

Source: vignettes/articles/download_en.Rmd
download_en.Rmd

Almost all GeneExprLupusMx data are distributed with the package: once it is loaded, they are available in R. The three largest objects (filtered_txi, dds and zscore_mat) are not installed with the package, to keep it small: they are downloaded with download_data() the first time you use them, and then read from your computer. This page explains how to get them outside R and where the raw sequencing data are stored.

1. Data included in the package

Object Content Size
filtered_txi RNA-seq counts (Salmon + tximport): counts, abundance (TPM) and length. Downloaded with download_data("filtered_txi") 21,718 genes × 99 samples
metadata_cleaned Group, cell type and clinical data of each sample 99 samples
volunteer_info Age, diagnosis, treatments and recruitment date 33 volunteers
biotype_summary Biotype of each gene 1 row per gene
sle_genes, isg_genes Lupus-associated genes and interferon-stimulated genes gene lists
moDC_genes, dc_genes, tolDC_genes Dendritic cell differentiation genes and the tolDC signature gene lists
dds, zscore_mat Normalized DESeqDataSet and z-score matrix. Downloaded with download_data() 13,596 genes × 99 samples
de_results, de_celltype_results, de_interaction_results Differential expression results (DESeq2) 1 row per gene
go_results, go_celltype_results, go_interaction_results Functional enrichment (g:Profiler) 1 row per term

The full description of each object is in the Reference section, or in R with ?object_name. The scripts that generate each object are in the data-raw/ folder of the repository.

2. Data sources

The data in this package come from the study published in Frontiers in Immunology. The data and code were obtained from two sources and reorganized here into ready-to-use R objects. If you use the data, please cite both sources (see section 7, “How to cite”):

Source What does it contain?
Original article Study design, methods, figures and supplementary material (including Supplementary Table 2 with the EGA identifiers)
Zenodo: record 17742206 Data and code of the gene expression analysis deposited by the authors: quantification with nf-core/rnaseq (Salmon) and analysis with tximport and DESeq2

The scripts in the data-raw/ folder document how each package object was generated from these sources.

3. Load the data in R 

# install.packages("remotes")
remotes::install_github("EveliaCoss/GeneExprLupusMx", build_vignettes = TRUE)

library(GeneExprLupusMx)
data(package = "GeneExprLupusMx") # list of available objects
head(metadata_cleaned)

4. Export to CSV files

To work with other software (Excel, Python, etc.), export the objects as tables:

dir.create("GeneExprLupusMx_data", showWarnings = FALSE)

# Metadata and volunteer information
write.csv(metadata_cleaned, "GeneExprLupusMx_data/metadata_cleaned.csv", row.names = FALSE)
write.csv(volunteer_info,   "GeneExprLupusMx_data/volunteer_info.csv",   row.names = FALSE)

# Count and TPM matrices (genes in rows, samples in columns)
filtered_txi <- download_data("filtered_txi") # only downloads the first time
write.csv(filtered_txi$counts,    "GeneExprLupusMx_data/counts.csv")
write.csv(filtered_txi$abundance, "GeneExprLupusMx_data/tpm.csv")

# Differential expression results: one file per cell type
for (cell in names(de_results)) {
  write.csv(de_results[[cell]], paste0("GeneExprLupusMx_data/de_SLEvsCtrl_", cell, ".csv"), row.names = FALSE)
}

5. Direct download from GitHub

Each object is also available as an .rda file in the data/ folder of the repository, and the three largest ones as .rds files in the v0.1.0 release. You can load them without installing the package:

url <- "https://github.com/EveliaCoss/GeneExprLupusMx/raw/main/data/metadata_cleaned.rda"
load(url(url))
head(metadata_cleaned)

# The large objects: first download the file, then read it
url <- "https://github.com/EveliaCoss/GeneExprLupusMx/releases/download/v0.1.0/filtered_txi.rds"
download.file(url, "filtered_txi.rds", mode = "wb")
filtered_txi <- readRDS("filtered_txi.rds")

6. Raw sequencing data

The raw data (RNA-seq FASTQ files) from patients and controls are deposited in the European Genome-phenome Archive (EGA) under controlled access. Access requires a formal request and approval by the corresponding Data Access Committee. The identifiers of each analysis are listed in Supplementary Table 2 of the article’s supplementary material.

7. How to cite

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

7.1 Citing the package

If you use the lessons, functions or objects of the package, please also cite GeneExprLupusMx:

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.

In R, citation("GeneExprLupusMx") shows the package citation.

7.2 Citing the data

The data and code come from the original article and the analysis deposited 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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