Global Expression Viewer
t-SNE representation with gene expression
Expression per body part
Median and mean expression
Search gene list
Gene expression heatmap
Download by body part
Download by project
NOTE: You can only download 1 BioProject at a time to avoid server overload.
Frequently Asked Questions
- What was the pipeline used to create this atlas?
The pipeline used to create this atlas is summarized below.
- What does “bias-corrected counts” mean?
RNA-seq software tools (including salmon, the one used here) report the number of reads mapped to each transcript, which is typically called raw read counts. However, transcript abundance estimates in raw counts are biased, because variations may be due to differences in gene length and library size. To correct for these biases, we used the “bias correction without an offset” method implemented in the Bioconductor package tximport, which scales raw counts using the average transcript length over samples, and then library size.
- Can I obtain transcript-level abundance estimates with this web application?
This application allows the exploration, visualization, and download of
gene-level transcript abundances (i.e., “gene expression”) only.
However, transcript-level abundances are available in the FigShare
repository associated with this
se_atlas_transcript.rda, an RData file that stores a
SummarizedExperiment object stores two assays named tx_TPM and
tx_counts with transcript-level abundances in TPM and read counts,
- Can I obtain a single file with all expression data in the Soybean Expression Atlas v2?
Yes. Quantitative data for gene- and transcript-level abundances can be
found in the FigShare repository associated with this
in RData files named
respectively. These RData files store
with the following assays:
se_atlas_gene.rda: assays named gene_TPM and gene_counts.
se_atlas_transcript.rda: assays named tx_TPM and tx_counts.
To load the
SummarizedExperiment object into an R session and access
the data, you would run the following R code:
library(SummarizedExperiment) # Load gene-level abundance data load("se_atlas_gene.rda") # Access the matrix with gene expression in TPM assay(se_atlas_gene, "gene_TPM") # Access the matrix with gene expression in bias-corrected counts assay(se_atlas_gene, "gene_counts") # Access sample metadata colData(se_atlas_gene)
For more information on how to work with
check the package’s
NOTE: these files are very large, as they store matrices with 52837 rows (genes) and 5481 columns (samples). As R stores data in memory, make sure you have enough memory if you want to work with the entire quantitative data.