Gene-Bulk data analysis tool
The Gene-Bulk data analysis tool contain eight key functions for discovering and exploring cancer immunotherapy-related genes including differential expression analysis (DEA), global differential expression analysis (GDEA), box plotting, correlation analysis, network construction, function annotation, function enrichment and survival analysis. The datasets in current Gene-Bulk data analysis tool are obtained from GEO containing 1045 samples from 35 datasets of cancer immunotherapy across 16 cancer types. Furthermore, the survival datasets were integrated from TCGA which containing 10209 tumor samples from 33 cancer types. The Gene-Bulk data analysis tool enables users to flexibly discovery and explore the association between genes and cancer immunotherapy, and analyze the function, mechanism and survival prognosis of immunotherapy-related genes. 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 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 datasets. This feature allows user to compare the degree and direction of differential expression of genes in different immunotherapy datasets.
Box Plotting
This function generates box plots for comparing expression of a specific gene between immunotherapy treatment vs no treatment samples or immunotherapy response vs no response samples.
Correlation Analysis
This function provides correlation analysis for expression of a specific gene and immune, inflammation, EMT and stemness scores in immunotherapy treatment vs no treatment (response vs no response) samples respectively. The correlation was estimated by using Spearman method and the immune, inflammation, EMT and stemness scores in samples were estimated by using GSVA method. In addition, this function also provides box plot for comparing immune, inflammation, EMT and stemness scores between immunotherapy treatment vs no treatment samples or immunotherapy response vs no response samples.
Network Construction
This function provides interacted gene-gene co-expressed networks in different conditions including immunotherapy treatment vs no treatment or immunotherapy response vs no response. This feature allows user to apply global network construction or network construction for a specific gene under different immunotherapy related conditions. User can also flexibly select correlation threshold for the network construction. View our Help page for further detailed information.
Function Annotation
This function provides annotation information for a specific gene in immune pathways, Cancer hallmarks and Immune signatures. User cancer exploring annotation information for an interested immunotherapy related gene in these three different function classes.
Function Enrichment
This function provides function enrichment analysis for differentially expressed genes between 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.
Survival Analysis
This function performs overall survival (OS) analysis based on the median expression value of a gene in various cancer types of TCGA. User cancer exploring the clinical relevance of an interested immunotherapy related gene across different cancer types.
This function allows user to obtain differential expression analysis and heatmap for genes in a specific immunotherapy
related 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 dataset under the corresponding immunotherapy condition for differential analysis.
FC: select a threshold value of fold change for differential analysis.
Pvalue: select a threshold value of pvalue for differential analysis. The pvalue of differential analysis was calculated by using Wilcoxon Signed Rank Test.
Immunotherapy conditions
Dataset Select
Pvalue
FC
This function allows user to obtain differential expression analysis and heatmap for genes by globally considering
multiple different datasets. This feature allows user to compare the degree and direction of differential expression
of genes in different immunotherapy 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 datasets that genes were differential (pvalue <= 0.05 & (FC >=2 | FC <= 0.5)).
Differ number up: the minimum number of 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 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 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 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
This function provides correlation analysis for expression of a specific gene and immune, inflammation, EMT and stemness
scores in immunotherapy treatment vs no treatment (response vs no response) samples respectively. The correlation was
estimated by using Spearman method and the immune, inflammation, EMT and stemness scores in samples were estimated by
using GSVA method. In addition, this function also provides box plot for comparing immune, inflammation, EMT and
stemness scores between immunotherapy treatment vs no treatment samples or immunotherapy response vs no response samples.
Immunotherapy conditions: select an interested immunotherapy condition (T/NT or R/NR) for correlation analysis.
T/NT: immunotherapy treatment vs no treatment; R/NR: immunotherapy response vs no response.
Dataset Select: select a specific dataset under the corresponding immunotherapy condition for drawing the correlation plot and boxplot.
Gene name: input a gene symbol for drawing the correlation plot and boxplot.
Immunotherapy conditions
Dataset Select
Gene name
This function provides interacted gene-gene co-expressed networks in different conditions including immunotherapy
treatment (drug) vs no treatment (nodrug) or immunotherapy response vs no response. This feature allows user to apply global network
construction or network construction for a specific gene under different immunotherapy conditions. User can also
flexibly select correlation threshold for the network construction. View our Help page for further detailed information.
Immunotherapy conditions: select an interested immunotherapy condition (T/NT or R/NR) for correlation analysis.
T/NT: immunotherapy treatment (drug) vs no treatment (nodrug); R/NR: immunotherapy response vs no response.
Dataset Select: select a specific dataset under the corresponding immunotherapy condition for drawing the correlation plot and boxplot.
Gene name: input a gene symbol or ‘All’ for drawing the correlation plot and boxplot.
R value: select the spearman correlation coefficient R threshold for the network construction.
Pvalue: select the spearman correlation Pvalue for the network construction.
Immunotherapy conditions
Dataset Select
Gene name
R value
Pvalue
This function provides annotation information for a specific gene in immune pathways, Cancer hallmarks and
Immune signatures. User cancer exploring annotation information for an interested immunotherapy related
gene in these three different function classes.
Gene name: input a gene symbol for function annotation.
Gene name
This function provides function enrichment analysis for differentially expressed genes between immunotherapy
treatment vs no treatment (response vs no response) samples in a specific immunotherapy 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 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 function performs overall survival (OS) analysis based on the median expression value of a gene in various
cancer types of TCGA. User cancer exploring the clinical relevance of an interested immunotherapy related
gene across different cancer types.
Dataset select: select a cancer for drawing the survival curve.
Gene name: input a gene symbol for drawing the survival curve.
Methods: select a threshold value for drawing the survival curve.