RNA expression in the single cell type clusters identified in this tissue visualized by a UMAP plot (top) and a bar chart (bottom).
- UMAP PLOT visualizes the cells in each cluster; where each dot corresponds to a cell. For each individual cell, mouseover reveals read count and which cluster the cell belongs to. Hovering over a cluster name will highlight the corresponding cluster in the bar chart below. There are 2 options for color schemes: 1) cell type color, which is based on cell type groups used in the Cell Type Atlas and 2) cluster color, which assigns a unique color to each cluster. Thre are 3 options for cell intensities: 1) Intensity, which color the individual cells according to % of max (log2(read_count+1)/log2(max(read_count)+1)*100) in five different bins (<1%,<25%,<50%,<75%,≥75%) 2) Interval, which color the individual cells according to fixed interval of read count (0,1,2-4,5-9,>10) 3) Hexagon, which color the hexagon on average read count of all cells inside the hexagon
- The BAR CHART shows RNA expression (pTPM) in each cell type cluster. Hovering over the cluster name reveals pTPM value and number of included cells. Hovering over a bar highlights the corresponding cluster in the UMAP plot above. Color-coding can be toggled on the top of the page, between 2 options: 1) cell type color, which is based on cell type groups used in the Cell Type Atlas and 2) cluster color, which assigns a unique color to each cluster.
Scatter plot, all cells color scale - fixed intervals
Hexagon, average of all cells
CELL TYPE MARKERSi
The heatmap in this section shows expression of the currently selected gene (on top) and well-known cell type markers in the different single cell type clusters of this tissue. The panel on the left shows which cell type each marker is associated with. Color-coding is based on cell type groups, each consisting of cell types with functional features in common.
Hover the mouse-pointer over the individual data points (squares) to see pTPM level and Z-score. Clicking on a gene name redirects to the corresponding gene page. Z-score is when you normalize a variable such that the standard deviation is 1 and the mean is 0. Thus, all the genes are easier to compare, as they have the same center and distribution.