Cell-Bulk data analysis tool
The Cell-Bulk data analysis tool contains five key functions for mining and characterizing immunotherapy-related
cell types including differential abundance analysis (DAA), global differential abundance analysis (GDAA), box
plotting, correlation analysis and survival analysis. The datasets in current Cell-Bulk data analysis tool are
obtained from GEO containing 1045 samples from 35 immunotherapy datasets across 16 cancer types. Cell abundance
(proportion) in samples was assessed using single-sample GSEA algorithm based on marker genes of different cell
types in the CellMarker database. Furthermore, the survival datasets were integrated from TCGA which containing
10209 tumor samples from 33 cancer types.
The Cell-Bulk data analysis tool enables users to flexibly explore the association between cells and cancer
immunotherapy, and analyze the function, mechanism and survival prognosis of cancer immunotherapy-related cells.
View our Help page for further detailed information.
Differential Abundance Analysis (DAA)
This function allows user to obtain differential abundance analysis and heatmap for cells in a specific immunotherapy related dataset. The DAA 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 Abundance Analysis (GDAA)
This function allows user to obtain differential abundance analysis and heatmap for cells by globally considering multiple different cancer immunotherapy datasets. This feature allows user to compare the degree and direction of differential abundance of cells in different cancer immunotherapy datasets.
Box Plotting
This function generates box plots for comparing abundance of a specific cell type between immunotherapy treatment vs no treatment samples or immunotherapy response vs no response samples.
Correlation Analysis
This function provides correlation analysis for abundance of a specific cell type 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.
Survival Analysis
This function performs overall survival (OS) analysis based on the median abundance value of a gene in various cancer types of TCGA. User cancer exploring the clinical relevance of an interested immunotherapy related cells across different cancer types.
This function allows user to obtain differential abundance analysis and heatmap for cells in a specific
immunotherapy related dataset of cancer. 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 abundance analysis and heatmap for cells
by globally considering multiple different datasets. This feature allows user to compare
the degree and direction of differential abundance of cells in different immunotherapy datasets of cancer.
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 cells were differential (pvalue <= 0.05 & (FC >=2 | FC <= 0.5)).
Differ number up: the minimum number of datasets that the abundance of cells is significantly high (T/NT or R/NR) 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 the abundance of cells is significantly low (T/NT or R/NR) 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 abundance of a specific cell type between cancer 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.
Cell name: input a name of cell type for drawing the boxplot.
Immunotherapy conditions
Dataset Select
Cell name
This function provides correlation analysis for abundance of a specific cell type and immune,
inflammation, EMT and stemness scores in cancer 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.
Cell name: input a name of cell type for drawing the correlation plot and boxplot.
Immunotherapy conditions
Dataset Select
Cell name
This function performs overall survival (OS) analysis based on the median abundance value
of a cell type in various cancer types of TCGA. User cancer exploring the clinical relevance
of an interested immunotherapy related cell type across different cancer types.
Dataset select: select a cancer for drawing the survival curve.
Cell name: input a name of cell type for drawing the survival curve.
Methods: select a threshold value for drawing the survival curve.