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 Single Cell Type section 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 (nTPM) in each cell type cluster. Hovering over the cluster name reveals nTPM 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 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
Intestinal goblet cells
Intestinal goblet 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 nTPM 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.