Simple usage for RAD-Bood

Results presentation

1 mRNA-seq

The mRNA-seq module of RAD-Blood performs mRNA expression, immune abundance, and TCR analysis between AD/MCI and normal samples, including five sub-modules:

1.1 DEG

This sub-module calculates the differentially expressed genes (DEGs) between AD/MCI and normal samples.

By selecting the 'ACOM' and 'AD vs control' in the DropDownLists, users can get the volcano plot summarizing the distribution of DEGs between AD and normal samples of ACOM.

By clicking 'APOC2' in the result table, users can get the rain cloud plot showing the differential expression of the APOC2 gene between AD and normal samples of ACOM (FDR = 1.269e-29).

1.2 GSEA

This sub-module analyzes the Gene Set Enrichment Analysis (GSEA) of DEGs between AD/MCI and normal blood samples.

By selecting the 'GO' and 'BP' in the DropDownLists, users can get the Ridge plot which shows the fold change distribution core enrichment genes from the top 20 (p-value < 0.05, arranged by p-value) enrichment pathways.

By clicking 'GO:0097006' in the result table, the user can get a graphical view of the enrichment score of the gene set of this pathway.

1.3 Immune abundance

This sub-module provides the immune cells' abundance between AD/MCI and normal samples from ImmuCellAI. The estimates of the abundance of immune cells were based on gene set signature.

By clicking 'NK' in the result table, users can get the rain cloud plot showing the difference in immune abundance of NK cells between AD and normal samples (FDR = 0.056).

1.4 Immune & Expression

This sub-module estimates the association between gene expression and immune cells’ abundance.

By selecting the 'DC' in the cell type DropDownList and clicking 'A1CF' in the result table, users can get the scatter plot showing the expression of the A1CF gene correlated with DC cells abundance.

1.5 TCR

This sub-module is used to estimate the usage of V(D)J genes and CDR3 amino acid in AD/MCI and normal samples.

In this sub-module, we provide four types of TCR analysis visualization results: Exploratory analysis, Clonality analysis, Gene usage analysis, and Diversity estimation.

1.5.1 Exploratory analysis

The Exploratory analysis is used to compute basic statistics, such as number of clones or distributions of lengths and counts.

By selecting 'Homo' and 'AD vs control' in the DropDownLists and clicking 'Exploratory analysis', users can obtain plots of the relative abundance, rare clonal fraction, and top clonal fraction of Homo between the AD/MCI and control samples.

1.5.2 Clonality

The Clonality is used to compute the clonality of repertoires.

By clicking 'Clonality', users can obtain plots of the CDR3 lengths, number of clonotypes, and clonotype abundances of Homo between the AD/MCI and control samples.

1.5.3 GeneUsage

The GeneUsage is used to analyse the distributions of V or J genes.

By clicking 'Diversity estimation', users can obtain plots of the K-means clustering and TRBV/TRBD/TRBJ gene usage of Homo between the AD/MCI and control samples.

1.5.4 Diversity estimation

The Diversity estimation is used to estimate the diversity of repertoires. By clicking on 'Diversity Estimation', the user can obtain plots of the estimated repertoire diversity between AD/MCI and control samples by chao1/hill number of Homo.

2 miRNA-seq

The miRNA-seq module of RAD-Blood performs miRNA expression, and pathway enrichment between AD and normal samples (or between different age group of mouse), including three sub-modules:

2.1 DEG

This sub-module analyze the miRNA differential expression between AD and normal samples (or between different age group of mouse).

Through selecting the 'Homo' and 'AD vs control' in the DropDownLists, users can get the volcano plot summarizing the distribution of differential miRNA expression between AD and normal samples of Homo.

By clicking 'hsa-miR-99b-5p' in the result table, users can get the rain cloud plot showing the differential expression of hsa-miR-99b-5p between AD and normal samples of Homo (FDR = 2.410e-4).

2.2 Pathway enrichment by miEAA

This sub-module analyzes the pathway enrichment between AD and normal samples (or between the different age groups of the mouse) from miEAA.

Through selecting the 'GO' in the DropDownList, users can get the Ridge plot which showes the expression distribution of the core enrichment genes of the top20 (pvalue < 0.05, arranged by p.value) enrichment pathways.

By clicking 'endocrine pancreas development GO0031018' in the result table, the user can get a graphical view of the enrichment score of the gene set of this pathway.

2.3 Pathway enrichment by mirPath

This sub-module analyzes the pathway enrichment between AD and normal samples (or between the different age groups of the mouse) from mirPath.

Through selecting the 'GO-up' in the DropDownList, users can get the bar plot showing the top20 (p-value <= 0.05) pathways from the table of GO-up results.

3 scRNA-seq

The scRNA-seq module of RAD-Blood provides cell marker analysis, DEGs, and GSEA for AD blood scRNA-seq data, including five sub-modules:

3.1 Cell annotation

This sub-module annotates the cell type of scRNA-seq data in blood. By selecting 'SRP309935' in the DropDownLists and clicking 'Cell annotation', users can obtain three type of plots: the tSNE scatter diagram, pie chart, and stacked column chart.

The tSNE scatter diagram summarizes the clustering display of tsne after cluster analysis. Each color represents a cell group identified after cluster analysis, and scattered points represent each cell. The subgroups close to each other represent the connection between the two.

The pie chart summarizes the proportion of different cell types in the project. Different sectors represents different cell types. When you put the mouse on different sectors, you can see the cell names, cell numbers and the proportion of different cell types in this project.

The stacked column chart summarizes the proportion of different cell types in each sample. This is a stacked histogram of percentages. Each layer of the column represents the percentage of each cell type in each sample. When you put the mouse on different levels of the column, you can see the cell names, cell numbers and the proportion of different cell types in this sample.

3.2 Cell marker

3.2.1 DEG

This sub-module of cell marker calculates the specific expressed genes of one cell type and other cell types.

By selecting 'B cell_1' in the DropDownList and then clicking 'CD79A' in the result table, users can get the feature plot and violin plot of CD79A gene expression between different cell types in B cell_1.

By selecting '|avg_log2FC|' and 'top5' in the DropDownLists, users can get the bubble plot and heatmap summarize the expression distribution of the first five genes in each cluster with |avg_log2FC| as the screening criterion in B cell_1.

3.2.2 GSEA

This sub-module analyzes the GSEA of the specific expressed genes between one cell type and other cell types.

By selecting 'B cell_1', 'GO' and 'BP' in the DropDownLists, users can get the ridge plot showing the distribution of core-enriched genes of the first 20 (p < 0.05, arranged by p-value) enrichment pathways of the specifically expressed genes of B cell_1.

By clicking 'GO:0045047' in the result table, the user can get a graphical view of the enrichment score of the gene set of this pathway.

3.3 Comparison between cell types

3.3.1 DEG

This sub-module of comparison between cell types module calculates the DEGs between selected cell type and another cell type.

By selecting 'B cell_1' and 'Plasmacytoid dendritic cell_3' in the DropDownLists and then clicking 'DLGAP5' in the result table, users can get the feature plot and violin plot of DLGAP5 gene differential expression between B cell_1 and Plasmacytoid dendritic cell_3.

Through select '|avg_log2FC|' and 'top5' in the DropDownLists, users can get the bubble plot and heatmap summarize the expression distribution of the first five differentially expressed genes in B cell_1 and Plasmacytoid dendritic cell_3 with |avg_log2FC| as the screening criterion.

3.3.2 GSEA

This sub-module analyzes the GSEA of the DEGs between the selected cell type and another cell type.

By selecting 'B cell_1' and 'Plasmacytoid dendritic cell_3', 'GO', and 'BP' in the DropDownLists, users can get the ridge plot showing the distribution of core-enriched genes of the first 20 (p < 0.05, arranged by p-value) enrichment pathways of differentially expressed genes between B cell _1 and plasmacytoid dendritic cell _3.

By clicking 'GO:0030449' in the result table, the user can get a graphical view of the enrichment score of the gene set of this pathway.

3.4 DEG

3.4.1 DEG

This sub-module analyzes the differential expression between AD/MCI and normal samples in each cell type.

By selecting 'B cell_1' and 'AD vs control' in the DropDownLists and then clicking 'RPS3A' in the result table, users can get the rain cloud plot showing the differential expression of RPS3A gene between AD and normal sample in B cell_1 (FDR = 1.061e-123).

3.4.2 GSEA

This sub-module analyzes the GSEA of the DEGs between AD/MCI and normal samples in each cell type.

By selecting 'B cell_1' and 'AD vs control', 'GO' and 'BP' in the DropDownLists, users can get the ridge plot showing the distribution of core-enriched genes of the first 20 (p-value < 0.05, arranged by p-value) enrichment pathways of differentially expressed genes between AD/MCI and normal samples in B cell_1.

By clicking 'GO:0001906' in the result table, the user can get a graphical view of the enrichment score of the gene set of this pathway.

3.5 Cell communition

3.5.1 Cell interation

This sub-module calculates the general principle of cell communication.

By selecting 'AD vs control' in the DropDownList, users can get a table of prediction results of intercellular communication network, a histogram showing the total number of interactions and interaction strength for each group, a circle plot showing the total number of interactions for each group, a circle plot showing the total interaction strength for each group, and a scatter diagram showing the comparison of outgoing and incoming interaction strength in 2D space.

3.5.2 Function and structure

This sub-module calculates the joint multiple learning and classification of inferred communication networks based on their functional and topological similarities.

In this sub-module, users can get the plots to identify signal groups according to their functional/structural similarity.

3.5.3 Signal flow

This sub-module identifies and visualizes conservative and environment-specific signal pathways. In this sub-module, the rankNet function was used to compare the overall signal flow of each signaling pathway.

In this sub-module, users can get the histogram to compare the overall information flow of each signal pathway and the heatmaps to comparing incoming/outgoing/overall signals associated with each cell population.

3.5.4 L-R pairs

This sub-module compares the communication probability of ligand-receptor pair regulation from some cell groups to other cell groups.

By selecting 'B cell_1' in the DropDownList, users can get the plot to identify the up-regulated and down-regulated signal ligand-receptor pairs in B cell_1.

3.5.5 Signal pathway

This sub-module compares the distribution of signal gene expression between different data sets.

By selecting 'MHC-1' in the DropDownList, users can get the violin plot showing the gene distribution and a table showing the differentially expressed genes between AD and normal samples of the MHC-1 signal pathway.

Contact

---