Gene-level RNA-seq quantification of the 99 samples of the study (33 volunteers x 3 cell types: monocytes, moDCs and tolDCs). Transcripts were quantified with Salmon and summarized to genes with tximport. Genes are identified by their gene symbol; duplicated entries in gene biotypes were removed before creating this object.
Format
A list with the structure returned by tximport::tximport():
- abundance
Numeric matrix (21,718 genes x 99 samples) of gene abundances in transcripts per million (TPM).
- counts
Numeric matrix (21,718 genes x 99 samples) of estimated read counts. Values are not integers because Salmon estimates them.
- length
Numeric matrix (21,718 genes x 99 samples) with the average effective transcript length of each gene in each sample; DESeq2 uses it as a normalization offset.
- countsFromAbundance
Character,
"no": counts were not derived from abundances.
Source
Hernández-Ledesma AL, Coss-Navarrete EL, et al. (2026), Front. Immunol. 17:1771959 (doi:10.3389/fimmu.2026.1771959 ); data and analysis code deposited in Zenodo (doi:10.5281/zenodo.17742206 ).
Details
This object is not installed with the package because of its size: get
it with filtered_txi <- download_data("filtered_txi") (see download_data).
Row names are gene symbols (e.g., "A1BG") and column names
are sample identifiers (e.g., "QR011_1"), in the same order as the
rows of metadata_cleaned. Use it with
DESeq2::DESeqDataSetFromTximport().
Examples
if (FALSE) { # interactive()
filtered_txi <- download_data("filtered_txi")
names(filtered_txi)
dim(filtered_txi$counts)
filtered_txi$counts[1:4, 1:4]
}