Compare differential gene expression results with those from big datasets (e.g. CMap), allowing to infer which types of perturbations may explain the observed difference in gene expression.
Optimised to run in ShinyProxy with Celery/Flower backend with argument
shinyproxy = TRUE.
Character: path where to store data common to all sessions
Character: days until a session expires (message purposes only)
Numeric: file size limit in MiB
Character: Flower REST API's URL (
NULL to avoid using
The TCP port that the application should listen on. If the
port is not specified, and the
shiny.port option is set (with
options(shiny.port = XX)), then that port will be used. Otherwise,
use a random port between 3000:8000, excluding ports that are blocked
by Google Chrome for being considered unsafe: 3659, 4045, 5060,
5061, 6000, 6566, 6665:6669 and 6697. Up to twenty random
ports will be tried.
The IPv4 address that the application should listen on. Defaults
shiny.host option, if set, or
"127.0.0.1" if not. See
Launches result viewer and plotter (returns
Input: To use this package, a named vector of differentially expressed gene metric is needed, where its values represent the significance and magnitude of the differentially expressed genes (e.g. t-statistic) and its names are gene symbols.
Workflow: The differentially expressed genes will be compared against selected perturbation conditions by:
Spearman or Pearson correlation with z-scores of differentially
expressed genes after perturbations from CMap. Use function
method = "spearman" or
method = "pearson"
Gene set enrichment analysis (GSEA) using the (around) 12 000 genes
from CMap. Use function
method = gsea.
Available perturbation conditions for CMap include:
Perturbation type (gene knockdown, gene upregulation or drug intake).
Values for each perturbation type can be listed with
Output: The output includes a data frame of ranked perturbations based on the associated statistical values and respective p-values.