Gene-Single cell data analysis tool
The Gene-Single cell data analysis tool provides key interactive and customizable functions including differential
expression analysis (DEA), global differential expression analysis (GDEA), box plotting, clustering, Cluster Activity
and function enrichment based on 13 single cell datasets of cancer immunotherapy including 307485 cells from 7 cancer types.
The Gene-Single cell data analysis tool enables users to flexibly explore the association between genes and immunotherapy,
and analyze the function and mechanism of immunotherapy-related genes base on single cell datasets of cancer immunotherapy.
In addition, user can explore gene expression in tumor microenvironment cells of immunotherapy-related samples. View our
Help page for further detailed information.
Differential Expression Analysis (DEA)
This function allows user to obtain differential expression analysis and heatmap for genes in a specific immunotherapy related single cell dataset. The DEA includes comparing immunotherapy treatment vs no treatment, and immunotherapy response vs no response. This feature allows user to apply custom thresholds on a given dataset.
Globally Differential Expression Analysis (GDEA)
This function allows user to obtain differential expression analysis and heatmap for genes by globally considering multiple different single cell datasets. This feature allows user to compare the degree and direction of differential expression of genes in different immunotherapy single cell datasets.
Box Plotting
This function generates box plots for comparing expression of a specific gene between the cells of immunotherapy treatment vs no treatment samples or immunotherapy response vs no response samples.
Clustering
This feature allows the user to perform cluster analysis on single-cell data of cancer immunotherapy.
Cluster Activity
Users can explore the activity of marker genes of different cell types in clusters to characterize different clusters.
Function Enrichment
This function provides function enrichment analysis for differentially expressed genes between cells of immunotherapy treatment vs no treatment (response vs no response) samples in a specific immunotherapy dataset of cancer. This feature allows user to apply custom function classes including immune pathways, cancer hallmarks and immune signature sets from MsigDB.
This function allows user to obtain differential expression analysis and heatmap for genes
in a specific immunotherapy related single-cell dataset. This feature allows user to apply
custom thresholds (Pvalue & FC values) on a given dataset.
Immunotherapy conditions: select an interested immunotherapy condition (T/NT or R/NR).
T/NT: immunotherapy treatment vs no treatment; R/NR: immunotherapy response vs no response.
Dataset Select: select a specific single-cell dataset under the corresponding
immunotherapy condition for differential analysis.
Immunotherapy conditions
Dataset Select
This function allows user to obtain differential expression analysis and heatmap for genes by globally
considering multiple different single-cell datasets. This feature allows user to compare the degree and
direction of differential expression of genes in different immunotherapy single-cell datasets.
Immunotherapy conditions: select an interested immunotherapy condition (T/NT or R/NR).
T/NT: immunotherapy treatment vs no treatment; R/NR: immunotherapy response vs no response.
Total differ number: the minimum number of single-cell datasets that genes were differential (pvalue <= 0.05 & (FC >=2 | FC <= 0.5)).
Differ number up: the minimum number of single-cell datasets that genes were significantly up (T/NT or R/NR) regulated
in immunotherapy treatment (response) individuals. The significance thresholds were set as pvalue <= 0.05 & (FC >=2 | FC <= 0.5).
Differ number down: the minimum number of single-cell datasets that genes were significantly down (T/NT or R/NR) regulated
in immunotherapy no treatment (no response) individuals. The significance thresholds were set as pvalue <= 0.05 & (FC >=2 | FC <= 0.5).
Immunotherapy conditions
Total differ number
Differ number up
Differ number down
This function generates box plots for comparing expression of a specific gene between cells of
immunotherapy treatment (drug) vs no treatment (nodrug) samples or immunotherapy response vs no response samples.
Immunotherapy conditions: select an interested immunotherapy condition (T/NT or R/NR) for drawing the boxplot.
T/NT: immunotherapy treatment (drug) vs no treatment (nodrug); R/NR: immunotherapy response vs no response.
Dataset Select: select a specific single-cell dataset under the corresponding immunotherapy condition for drawing the boxplot.
Gene name: input a gene symbol for drawing the boxplot.
Immunotherapy conditions
Dataset Select
Gene name
Users can explore the activity of marker genes of different cell types in clusters to characterize different clusters.
For cell type:
Immunotherapy conditions: select an interested immunotherapy condition (T/NT or R/NR). T/NT: immunotherapy treatment vs no treatment; R/NR: immunotherapy response vs no response.
Dataset Select: select a specific single-cell dataset under the corresponding immunotherapy condition.
Cell type: select a cell type to explore the activity of its maker genes in different clusters of the corresponding immunotherapy dataset.
Top N: select the number of clusters to shown according to the activity of the selected cell type (from high to low).
Immunotherapy conditions
Dataset Select
Cell type
This function provides function enrichment analysis for differentially expressed genes between
cells of cancer immunotherapy treatment vs no treatment (response vs no response) samples in
a specific immunotherapy single-cell dataset. This feature allows user to apply custom function
classes including GO/KEGG, immune pathways, cancer hallmarks and immune signature sets from MsigDB.
Immunotherapy conditions: select an interested immunotherapy condition (T/NT or R/NR) for function enrichment analysis.
T/NT: immunotherapy treatment vs no treatment; R/NR: immunotherapy response vs no response.
Dataset Select: select a specific single-cell dataset under the corresponding immunotherapy condition for function enrichment analysis.
Pvalue: select a threshold value of pvalue to identify enrichment functions. The pvalue of enrichment was calculated by
using hypergeometric test.
Function class: select a function class for function enrichment analysis.
Immunotherapy conditions
Dataset Select
Pvalue
Function class
This feature allows the user to perform clustering analysis on single-cell data of cancer immunotherapy
and to explore the expression of a specific gene in tumor microenvironment cells. The clustering function
is performed based on R package Seurat with default parameters.
Immunotherapy conditions: select an interested immunotherapy condition (T/NT or R/NR) for cell clustering.
T/NT: immunotherapy treatment (drug) vs no treatment (nodrug); R/NR: immunotherapy response vs no response.
Dataset Select: select a specific single-cell dataset under the corresponding immunotherapy condition for cell clustering.
Gene name: input a gene symbol for exploring the expression of it in tumor microenvironment cells.