Comparisons
C: control; AD - alzheimer’s diseae
To identify differences in AD from a sex perspective we tested three independent comparisons for each cell type analysed. We calculate statistics to find out whether the detected change is significant, and logFC values to know the magnitude (logFC absolute value) and the direction (logFC sign) of this change. For each comparison, the meaning of the logFC is:
Impact of disease in females (IDF comparison): we unveiled the differences among AD patients and healthy individuals being female by the comparison:
AD females - Control females
Direction of change can be represented qualitatively as:
Positive logFC (logFC > 0) indicates that there is a higher level of the analysed characteristic in AD females compared to control females (i.e. lower level of the analysed characteristic in control females compared to AD females).
Negative logFC (logFC < 0) indicates that there is a higher level of the analysed characteristic in control females compared to AD females (i.e. lower level of the analysed characteristic in AD females compared to control females).
Impact of disease in males (IDM comparison): we unveiled the differences among AD patients and healthy individuals being male by the comparison:
AD males - Control males
Direction of change can be represented qualitatively as:
Positive logFC (logFC > 0) indicates that there is a higher level of the analysed characteristic in AD males compared to control males (i.e. lower level of the analysed characteristic in control males compared to AD males).
Negative logFC (logFC < 0) indicates that there is a higher level of the analysed characteristic in control males compared to AD males (i.e. lower level of the analysed characteristic in AD males compared to control males).
Sex-differential impact of disease (SDID comparison): we unveiled sex differences among AD patients without considering the inherent sex variability in healthy individuals, that is, finding differences between IDF and IDM by the comparison:
(AD females - Control females) - (AD males - Control males)
Direction of change can be represented qualitatively as:
Positive logFC (logFC > 0) indicates a sex-differential increase of the feature in females compared to males (i.e. a sex-differential decrease of the feature in males compared to females). Patterns that could lead to this result are 1) positive logFCs in both IDF and IDM but larger in IDF, 2) positive logFCs in IDF and negative logFCs in IDM, 3) negative logFCs in both IDF and IDM but larger in IDM, 4) positive logFCs in IDF without significant changes in IDM, 5) negative logFCs in IDM without significant changes in IDF.
Negative logFC (logFC < 0) indicates a sex-differential increase of the feature in males compared to females (i.e. a sex-differential decrease of the feature in females compared to males). Patterns that could lead to this result are 6) positive logFCs in both IDF and IDM but larger in IDM, 7) negative logFCs in IDF and positive logFCs in IDM, 8) negative logFCs in both IDF and IDM but larger in IDF, 9) positive logFCs in IDM without significant changes in IDF, 10) negative logFCs in IDF without significant changes in IDM.
Differential gene expression patterns section
Gene viewer: in this tab you will explore changes in the expression of the genes of interest. You will be able to select the cell types and comparisons of interest to observe in barplots how the logFC of your interest genes changes and wheter or not these changes are significant. In addition, the sub-tab “Significant genes” shows those genes for which, in at least one cell type, there are significant differences in F, M, FM comparison.
All results: this tab displays a detailed table with all the results obtained from the differential expression analysis. The fields to explore are: genes, cell types, comparison, p.value, adjusted p.value and logFC. For each field you can set the filters of interest, and the resulting table can be downloaded.
Functional profiling section
Function viewer: in the “Select database” tab you can choose the results for the different functional databases used to explore changes in GO-BPs and REACTOME pathways. By selecting the cell types of interest you can see a dotplot. For the case of the Gene Ontology analysis, the results are divided by cell type and direction of the results, while the dot size is determined by the -log10(p.value) and dot colour is determined based on a p.value < 0.05. For the case of the REACTOME analysis, the results are divided by cell type and direction of the results, while the dot size is determined by the (lor) and the colour of the dot is determined by an adj p.value < 0.05. You can also adjust the length and width of the plot to download the image with the preferred dimensions. If you access the “Table” tab, you can download all the information from the following fields for the functions/pathways represented: GO identifiers/Path identifiers, function names, cell types, comparisons, the direction of change of the genes (UP: logFC > 0, DOWN: logFC < 0), gene ratios, p.values and the list of significant genes involved in each function/pathway.
All results: this tab displays a detailed table with all the results obtained from the functional profiling analysis. The fields to explore are: GO identifiers/Path identifiers, function names, cell types, comparisons, the direction of change of the genes (UP: logFC > 0, DOWN: logFC < 0), gene ratios, p.values and the list of significant genes involved in each function. For each field you can set the filters of interest, and the resulting table can be downloaded.
Signaling pathways section
Pathway viewer: in this tab you will explore changes in the activation of protein effectors in signaling pathways of interest. You will be able to select the cell type and pathway of interest. Thus, you will observe the circuits defined in the corresponding signaling pathway marking by a color system the significant effector subpathways. Up-regulated subpathways correspond to routes with higher activity associated with females compared to males. Down-regulated subpathways correspond to routes with higher activity associated with males compared to females.
You can also adjust the length and width of the plot to download the image with the preferred dimensions. If you access the “Table” tab, you can download all the information from the following fields for the functions represented: Path identifiers, function names, cell types, the direction of change of the genes (UP: logFC > 0, DOWN: logFC < 0), gene ratios, p.values and the list of significant genes involved in each function. Beneath you will find a detailed table with the KEGG database identifier of the effectors, the path and effector name, the p.value, the adjusted p.value and the logFC.
All results: this tab displays a detailed table with all the results obtained from the signaling pathways analysis. The fields to explore are: KEGG database identifier of the effectors, the path and effector name , cell types, lambda, p.value, adjusted p.value and the logFC. For each field you can set the filters of interest, and the resulting table can be downloaded.
Cell-cell communication networks section (ON GOING!)
Plot viewer: in this tab you will explore changes in the cell-cell communication networks of interest. You will be able to select the alzheimer’s disease .
Total interactions: we quantitatively characterise cell-cell communication networks for each group (AD female, control female, AD male and control male). This implies that for each group you will be able to know the number of significant interactions between cell types, considering the cell type that provides the ligand (source cell type) and the receptor (target cell type).
Explore by pathway: a pathway is comprised of pairs of ligand-receptor interactions. Each ligand-receptor pair has different interaction strengths. In this tab, you can investigate the significant interaction strengths of the pathways of interest. To do so, you can select the group, the pathway and the cell types you want to provide the ligand (source cell type) and the receptor (target cell type).
All results: this tab displays a detailed table with all the results obtained from the cell-cell communication analysis. The fields to explore are: cell source, cell target, ligand, receptor, pathway, alzheimer’s disease , group. interaction strength, p.value and p.adjusted. For each field you can set the filters of interest, and the resulting table can be downloaded.
Study overview section
This section includes the outline of dataset selection, the summary of selected datasets and the detailed technical description of the bioinformatic analysis.