Comparisons
C: control; MS - multiple sclerosis
To identify differences in MS disease 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 MS patients and healthy individuals being female by the comparison:
MS 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 MS females compared to control females (i.e. lower level of the analysed characteristic in control females compared to MS females).
Negative logFC (logFC < 0) indicates that there is a higher level of the analysed characteristic in control females compared to MS females (i.e. lower level of the analysed characteristic in MS females compared to control females).
Impact of disease in males (IDM comparison): we unveiled the differences among MS patients and healthy individuals being male by the comparison:
MS 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 MS males compared to control males (i.e. lower level of the analysed characteristic in control males compared to MS males).
Negative logFC (logFC < 0) indicates that there is a higher level of the analysed characteristic in control males compared to MS males (i.e. lower level of the analysed characteristic in MS males compared to control males).
Sex-differential impact of disease (SDID comparison): we unveiled sex differences among MS patients without considering the inherent sex variability in healthy individuals, that is, finding differences between IDF and IDM by the comparison:
(MS females - Control females) - (MS 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 multiple sclerosis subtype, cell types and comparisons of interest to observe in barplots how the expression of your favorite genes changes. Additionally, it will be indicated whether the observed changes are significant or not.
All results: this tab displays a detailed table with all the results obtained from the differential expression analysis. The fields to explore are: multiple sclerosis subtype, 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 “Plot” tab you will explore changes in the biological functions of interest. You will be able to select the multiple sclerosis subtype, cell types and comparisons of interest to observe in dotplots the gene ratio and statistical significance of your favourite biological functions. 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: GO identifiers, function names, multiple sclerosis subtypes, 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.
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, function names, multiple sclerosis subtypes, 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 multiple sclerosis subtype, cell type, comparison and pathway of interest. Thus, you will observe the circuits defined in the corresponding signaling pathway marking the significant effector subpathways.
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: GO identifiers, function names, multiple sclerosis subtypes, 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. 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, multiple sclerosis subtypes, cell types, comparisons, 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
Plot viewer: in this tab you will explore changes in the cell-cell communication networks of interest. You will be able to select the multiple sclerosis subtype.
Total interactions: we quantitatively characterise cell-cell communication networks for each group (MS female, control female, MS 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, multiple sclerosis subtype, 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.