Create a combinatorial annotation

First, check below categorical annotations to be combined. Second, select items in each annotation. Then, click . The max combinatorial number

Create abbreviations of annotations

Choose annotation(s):

Add genes of interest for plotting

Please input a list of genes separated by comma, semicolon, space or tab:

The following genes were added (click on a gene name to remove):

The following genes are not in the data:

User-defined gene sets:

Add gene sets of interest manually

Gene set name: Gene names:

Load gene sets from a text file

geneset1: ACTB, Gapdh
geneset2: BTK, CD4

Add gene sets from databases

Genes detected in cell selections

In selection 1 (0) cells and selection 2 (0) cells

Expression cutoff:

Marker genes of annotation categories from selected cells

Choose cell selection(s): Selection 1 (0) Selection 2 (0)

Select an annotation:

By method: with the number of genes per group:

Violin plot of a gene from selected cells

Choose cell selection(s): Selection 1 (0) Selection 2 (0)

Choose a gene ('Add Genes' first):

Expression cutoff for cell filtering:

Group by:

Plot sub-grouped violin

Choose cell selection(s): Selection 1 (0) Selection 2 (0)

Choose a gene ('Add Genes' first):

Expression cutoff (for cell filtering):

Expression cutoff (for percentage calculation):

Group by:

Sub-group by:

Stacked violin plot of genes from selected cells

Choose cell selection(s): Selection 1 (0) Selection 2 (0)

Choose genes ('Add Genes' first): Uncheck / Check All

Expression cutoff for cell filtering:

Select user-defined gene sets: Uncheck / Check All

Collapsing the above gene sets: Yes No

By: Average Median

Group by: as

Color: Reverse

Violins of selected genes splitted on two group annotations plot1cell

Choose cell selection(s): Selection 1 (0) Selection 2 (0)

Choose genes ('Add Genes' first): Uncheck / Check All

Expression cutoff for cell filtering:

Group by (row panels):

Sub-group by (within a subpanel):

Width: Height:

Heatmap of selected cells and genes

Choose cell selection(s): Selection 1 (0) Selection 2 (0)

Choose genes ('Add Genes' first): Uncheck / Check All Using protein atlas annotation

Or upload a gene annotation csv file (first column header is 'gene'):

Choose categorical annotation(s):

Choose continuous annotation(s):

Select plotting value:

Color: Reverse

Select plotting methods:

Gene as Column Row || Max legend row:

Width: Height:

Annotation fontsize adjust:

Cluster by:

Change the annotation order by dragging items up or down, cluster annotations can also be removed by clicking :

Gene specificity for groups in an annotation

Choose cell selection(s): Selection 1 (0) Selection 2 (0)

Choose genes (If no gene select, all gene will be used.): Uncheck / Check All

Select an annotation for specificity:

Dot plot shows per group, the fraction of cells expressing a gene (dot size) and the mean expression of the gene in those cell (color scale)

Choose cell selection(s): Selection 1 (0) Selection 2 (0)

Choose genes ('Add Genes' first): Uncheck / Check All

Expression cutoff for cell fraction (dot size):

Expression is averaged only over cells expressing a given gene above the cutoff: YesNo

Select user-defined gene sets: Uncheck / Check All

Collapsing the above gene sets: Yes No

By: Average Median

Group by:

Adjust the legend width:

Color: Reverse

Note: Unscaled data is used as the visualiztion is hard to interpret for scaled or corrected matrices in which zero counts had been replaced by other values. Scaling option in Global Setting does not apply to Dot plot

Embedding plots of selected genes and/or annotations

Choose cell selection(s): Selection 1 (0) Selection 2 (0)

Choose categorical annotation(s):

Choose continuous annotation(s):

Choose genes ('Add Genes' first): Uncheck / Check All

Split by:

Select the embedding layout: the number of plots per row:

Track plot represents the gene expression by height

Choose cell selection(s): Selection 1 (0) Selection 2 (0)

Choose genes ('Add Genes' first): Uncheck / Check All

Select user-defined gene sets: Uncheck / Check All

Collapsing the above gene sets: Yes No

By: Average Median

Group by:

Density plots of selected genes

Choose cell selection(s): Selection 1 (0) Selection 2 (0)

Choose genes ('Add Genes' first): Uncheck / Check All

Split by:

Color by:

Bandwidth: controls how tightly the curve fits to the data, like the bin size in a histogram.

Density Scatter plot shows the expression relationship of two genes. The color of each hexagon denotes the number of cells in it.

Choose cell selection(s): Selection 1 (0) Selection 2 (0)

Choose two genes ('Add Genes' first):

Expression cutoff:

Number of hexagons: The higher the number, the less cells in a hexagon.

Embedding plot of two genes from selected cells

Choose cell selection(s): Selection 1 (0) Selection 2 (0)

Expression cutoff:

Select the embedding layout:

Interactive sankey diagram of annotations and/or genes from selected cells

Choose cell selection(s): Selection 1 (0) Selection 2 (0)

Choose genes ('Add Genes' first): Uncheck / Check All

Number of equally divided gene expression ranges:

Choose annotation(s):

Change the order by dragging items up and down:

Please specify the diagram width per category: and the diagram height:

Find differentially expressed genes

Selection 1 (0) cells and selection 2 (0) cells

Differentially expressed genes will be detected between the two cell gorups defined above, unless two categories of the 'Custom_combine' annotation are selected, which enables DE analysis between those two:

Select the number of top DEG:

Select the method:

Select genes to be marked in the volcano plot: Uncheck / Check All

Define the significance in FDR > and absolute log2(FC) >

Remove genes in volcano plot with the absolute log2(FC) larger than:

Label Size: Dot Size:

Y-axis Min: Y-axis Max:

Gene Set Enrichment Analysis:

Note: Unscaled data is used. Scaling option in Global Setting does not apply to DEG.

Significance Gene Information Gene Selection

FDR: Asolute log2(FC):

Larger than Equal to smaller than

Uncheck / Check All

Gene Set Enrichment Analysis by fgsea

Plot volcano for the pre-computed DEGs

Select genes to be marked in the volcano plot: Uncheck / Check All

Select the number of top DEG:

Define the significance in FDR < and absolute log2(FC) >

Label Size: Dot Size:

Y-axis Min: Y-axis Max:

Significance Gene Information Gene Selection

FDR: Asolute log2(FC):

Larger than Equal to smaller than

Uncheck / Check All

Plot the bubble heatmap for pre-computed DEGs from multiple comparisons

Select genes to be included in the bubble heatmap (rows): Uncheck / Check All

Select comparisons from the table (shift/Ctrl for multi-selections) to be included in the bubble heatmap (columns):

Change the order by dragging items up and down:

Bubble distance in plot:

Gene Set Enrichment Analysis by fgsea

Please select the gene sets: Gene set definitions and more.
The gene sets with minimal size: and maximal size:
The significant padj cutoff in the plot:
Plot the significant top (ranked by pval) up: and down:
Collapse similar gene sets in the plot:

Interactive stacked barplot of cell distribution of selected cells over two annotations and/or genes

Choose cell selection(s): Selection 1 (0) Selection 2 (0)

Choose two from genes and annotations ('Add Genes' first):

Number of equally divided gene expression ranges:

Annotations:

Color by

Figure width: , height:

x-Axis font size: , label rotate: , label shift:

Jointly visualizing gene expression and DNA accessibility

The following tracks are available:

Selected browser tracks: Change the track orders by dragging items up or down, can also be removed by clicking

Define track region by: Coordinate Gene

Jointly visualizing histology image, cell boundaries, transcript locations

Choose cosMx captures to plot

Select coordinates: Local Global

Draw histology image

Hightlight cell boundaries with color

Choose transcripts to hightlight

Choose color for selected transcripts

Choose the size for transcript markers:

Global setting for VIP

Select cells cumulatively: Yes No

Selection 1 (0) | Selection 2 (0)

Scale gene expression to unit variance and zero mean Yes No

Genes with no variation are retained and set expression values to 0 during this operation
Clip to a maximum value of

Width of the central canvas of main window in pixel:

Figure DPI (Dots Per Inch, for PNG only):

Figure format:

Font size:

Vector friendly: Yes, plot mass components using png No, all are vectors.

Transparent figure: Yes No

Colormap of Embedding Plot (all available), Density Scatter (limited to top 4 colormaps): Reverse

viridis
plasma
inferno
magma
spring
summer
Fall
winter

Image resolution: High Low

Move embedding and image together: Yes No

Image on the front: Yes No

Image Opacity: 0.75

Embedding Opacity: 1

Choose slides to arrange: Uncheck / Check All

Click on an image to operate:

Flip X Flip Y Rotate: 0 degree

Brushed ranges

CLI to operate on data directly

Choose cell selection(s): Selection 1 (0) Selection 2 (0)

Choose genes ('Add Genes' first): Uncheck / Check All

Choose annotation(s):

Choose layout:

Test VIP functionalities

Create a test case

by:

The process will
1. Split cells with selected annotation into two groups randomly;
2. Select 500 cells from each group;
3. Perform DEG (Differentially Expressed Genes) method to those two groups of cells;
4. Select top DEGs randomly into groups;
5. Choose plotting options in VIP randomly;
6. Perform plotting functions one by one;
7. Save the test case on the server for future use (overwrite previous).

The test generation will ignore the first 2 steps in the above process and run the rest.

The user has selected all options on VIP including the genes and gene sets (or loaded previous session). This process will process all VIP plots one by one and save it as a test case on the server (overwrite previous).

Load saved session

Load from a url? Yes No

Run plotting after loading? Yes No

from pyarrow import feather import pandas as pd X = adata.to_df() meta = pd.concat([adata.obs,adata.obsm.to_df()],axis=1) meta[meta.select_dtypes(['category']).columns] = meta.select_dtypes(['category']).apply(lambda x: x.astype('object')) feather.write_feather(X,strPath+'.feather') feather.write_feather(meta,strPath+".obs.feather") %%R library(Seurat) library(patchwork) library(ggplot2) D <- t(tibble::column_to_rownames(arrow::read_feather(paste(strPath,".feather",sep="")), var="__index_level_0__")) meta <- as.data.frame(arrow::read_feather(paste(strPath,".obs.feather",sep=""))) colnames(D) <- meta[,"name_0"] X <- CreateSeuratObject(counts=D) X@meta.data <- cbind(X@meta.data,meta) X[['RNA']]['data'] <- X[['RNA']]['counts'] %Rdevice png %%R -r 600 -w 7200 -h 3600 X[["percent.mt"]] <- PercentageFeatureSet(X, pattern = "^MT-") VlnPlot(X, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) plot1 <- FeatureScatter(X, feature1 = "nCount_RNA", feature2 = "percent.mt") plot2 <- FeatureScatter(X, feature1 = "nCount_RNA", feature2 = "nFeature_RNA") plot1 + plot2 %%R -r 300 -w 3000 -h 1600 X <- subset(X, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5) X <- FindVariableFeatures(X, selection.method = "vst", nfeatures = 2000,verbose=F) top10 <- head(VariableFeatures(X,verbose=F), 10) plot1 <- VariableFeaturePlot(X) plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE) plot2 %Rdevice svg %%R -w 8 -h 4 all.genes <- rownames(X) X <- ScaleData(X, features = all.genes,verbose=F) X <- RunPCA(X, features = VariableFeatures(object = X)) print(X[["pca"]], dims = 1:5, nfeatures = 5) DimPlot(X, reduction = "pca") %Rdevice png %%R -r 150 -w 1000 -h 1000 DimHeatmap(X, dims = 1, cells = 500, balanced = TRUE) %%R -r 150 -w 2000 -h 2400 DimHeatmap(X, dims = 1:15, cells = 500, balanced = TRUE) %Rdevice svg %%R -w 6 -h 8 sink(stdout(),type="message") suppressMessages(library(dplyr)) X <- FindNeighbors(X, dims = 1:10, verbose=FALSE) X <- FindClusters(X, resolution = 0.5) X.markers <- FindAllMarkers(X, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25) top10 <- X.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC) DoHeatmap(X, features = top10$gene, label=FALSE) + theme(text = element_text(size = 8)) sessionInfo()