Single-cell atlas of the Entorhinal Cortex in Human Alzheimer’s Disease
One can visualize the single-cell metadata and gene expression overlaid
onto a dimension reduction plot.
Gene Expression
Cell Information
One can visualize the single-cell gene expression grouped by a specified
single-cell metadata. The resulting gene expression can be presented in a
(i) bubbleplot where the colour represents the relative gene expression and
the bubble size is the proportion of cells in the group expressing the gene,
(ii) heatmap where the colour represent the average gene expression in each
group, (iii) violinplot showing the distibution of non-zero gene expression
in each group. Note: To speed up the violinplot, we only sampled a number of
cells (nCells in the smallest group) from each group for plotting and this
also ensures that there is the same number of cells within each group.
One can visualize the proportion / number of cells of a specified category
in each group.
Cell Proportion
Dimension Reduction
One can visualize the single-cell metadata and gene expression of a
particular cell type overlaid onto a dimension reduction plot.
Gene Expression
Cell Information
One can visualize the single-cell gene expression of a
particular cell type grouped by a specified
single-cell metadata. The resulting gene expression can be presented in a
(i) bubbleplot where the colour represents the relative gene expression and
the bubble size is the proportion of cells in the group expressing the gene,
(ii) heatmap where the colour represent the average gene expression in each
group, (iii) violinplot showing the distibution of non-zero gene expression
in each group. Note: To speed up the violinplot, we only sampled a number of
cells (nCells in the smallest group) from each group for plotting and this
also ensures that there is the same number of cells within each group.
One can visualize the proportion / number of cells of a specified category
in each group in a particular cell type.
Cell Proportion
Dimension Reduction
Differential expression and GSEA results for comparing the cell type of
interest against other cell types.
Differential Expression
Gene Set Enrichment
Differential expression and GSEA results for comparing AD vs Control cells
within each cell type. Cells are deemed AD / Control based on the library.
Differential Expression
Gene Set Enrichment
Differential expression and GSEA results for comparing AD vs Control cells
within each cell type. Cells are deemed AD if its corresponding subcluster
contains >80% cells from an AD-associated library and vice versa.
Differential Expression
Gene Set Enrichment
Differential expression and GSEA results for comparing the subcluster of
interest against other subclusters within the same cell type.
Differential Expression
Gene Set Enrichment
Differential expression and GSEA results for comparing between pairs of
subclusters within the same cell type. Here, we denote the DE/GSEA in a
source_target format. Thus, a positive LFC/NES for m1_m2 indicates that a
gene / pathway is upregulated in subcluster m2 as compared to subcluster m1.
Differential Expression
Gene Set Enrichment
List of GWAS genes used in this study, their Experimental Factor Ontology
(EFO) categories [
Alzheimer's disease (EFO_0000249) /
AD Biomarkers (EFO_0006514) /
LOAD (EFO_1001870) /
Neuropathologic (EFO_0006801)
] and associated PubMed IDs.
Differential expression of GWAS genes for comparing AD vs Control cells
within each cell type. Cells are deemed AD / Control based on the library.
Differential Expression
Differential expression of GWAS genes for comparing AD vs Control cells
within each cell type. Cells are deemed AD if its corresponding subcluster
contains >80% cells from an AD-associated library and vice versa.
Differential Expression
Differential expression of GWAS genes for comparing the subcluster of
interest against other subclusters within the same cell type.
Differential Expression
Differential expression of GWAS genes for comparing between pairs of
subclusters within the same cell type. Here, we denote the DE in a
source_target format. Thus, a positive LFC for m1_m2 indicates that a
gene is upregulated in subcluster m2 as compared to subcluster m1.
Differential Expression
Gene Regulatory Network (GRN) scores for TFs predicted to regulate the
transition between pairs of subclusters within the same cell type,
calculated using the CellRouter algorithm (da Rocha et al. 2018). Here, we
denote the transition in a source_target format. Thus, a positive GRN score
for m1_m2 indicates that the upregulation of the TF is involved in the
transition from m1 to m2. Note that for some transitions, no TFs are found
after pruning the results. Furthermore, the CellRouter algorithm did not
identify any TFs for endothelial cells and the algorithm was not ran on
unidentified / hybrid cells.
List of TFs and their target genes for TFs predicted to regulate the
transition between pairs of subclusters within the same cell type,
calculated using the CellRouter algorithm (da Rocha et al. 2018). Here, we
denote the transition in a source_target format. Note that for some
transitions, no TFs are found after pruning the results. Furthermore, the
CellRouter algorithm did not identify any TFs for endothelial cells and
the algorithm was not ran on unidentified / hybrid cells.
In this tab, one can download the expression data and sample metadata.