Global Expression Viewer

Examples:

t-SNE representation with gene expression

Here, you can visualize the input gene's expression in each sample (right) with the support of a t-SNE representation of samples colored by body part (left). Given the heterogeneity of the Soybean Expression Atlas, these plots are particularly useful to find out if the input gene is expressed in only a subset of the samples of particular tissues. Color scales can be adjusted in the box above.

Expression per body part

Body parts are colored based on median expression values in TPM. As the Soybean Expression Atlas is highly heterogeneous, this figure should never be interpreted alone. Always look at the t-SNE representation above. Color scales can be adjusted in the box above.

Median and mean expression

As the Soybean Expression Atlas is highly heterogeneous, this figure should never be interpreted alone. Always look at the t-SNE representation above.

Search gene list

Examples:

Gene expression heatmap

Here, you can visualize the expression of the input genes as a heatmap.

Download options

Data summary
Download data (.tsv)
Expression matrix

Sample metadata

Download by body part


Data summary

Download data (.tsv)
Expression matrix Sample metadata

Download by project

Click the row corresponding to a BioProject to be able to download its expression matrix and sample metadata in the 'Download options' box. You can use the filter boxes above each column to filter based on a particular variable, or the global search box (above all columns) to find matches in any column.
NOTE: You can only download 1 BioProject at a time to avoid server overload.

Download options

Data summary
Download data (.tsv)
Expression matrix

Sample metadata

Explore gene metadata

Genes with null expression (median TPM <1 in all parts) do not have Tau statistics calculated. InterPro domains and gene ID correspondences were obtained from PLAZA Dicots 5.0.

Frequently Asked Questions

  1. What was the pipeline used to create this atlas?

The pipeline used to create this atlas is summarized below.

  1. 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.

  1. Can I obtain transcript-level abundance estimates with this web application?

Yes. Transcript-level abundances can be obtained in the Download by body part tab.

  1. 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 project, in RData files named se_atlas_gene.rda and se_atlas_transcript.rda, respectively. These RData files store SummarizedExperiment objects 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 SummarizedExperiment objects, check the package’s documentation.

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.

  1. How do I report a bug or issue?

You can open an issue in this GitHub repository that was specifically created as a communication channel with our users.