Single Cell Toolkit

Filter, cluster, and analyze single cell RNA-Seq data

Need help? Read the docs.


Import

(help)


Upload data in tab separated text format:

Example count file:
Gene Cell1Cell2CellN
Gene10 00
Gene2560
Gene343 8
GeneM101010
Download an example count file here.

Input Assay Type:

Example cell annotation file:
Cell Annot1
Cell1a
Cell2a
Cell3b
CellN b
Download an example annotation file here.
Example feature file:
Gene Annot2
Gene1a
Gene2a
Gene3b
GeneM b
Download an example feature file here.

Choose Example Dataset:

130 cells from (Pollen et al. 2014), 65 at high coverage and 65 at low coverage

Transcriptomes of cell populations in both of low-coverage (~0.27 million reads per cell) and high-coverage (~5 million reads per cell) to identify cell-type-specific biomarkers, and to compare gene expression across samples specifically for cells of a given type as well as to reconstruct developmental lineages of related cell types. Data was loaded from the 'scRNASeq' package.

Mouse visual cortex cells from (Tasic et al. 2016)

Subset of 379 cells from the mouse visual cortex. Data was loaded from the 'scRNASeq' package.

1920 Mouse haematopoietic stem cells from (Nestorowa et al. 2015).

Data was loaded from the 'scRNASeq' package.

2,700 peripheral blood mononuclear cells (PBMCs) from 10X Genomics

Data was loaded with the 'TENxPBMCData' package.

4,430 peripheral blood mononuclear cells (PBMCs) from 10X Genomics

Data was loaded with the 'TENxPBMCData' package.

5,419 peripheral blood mononuclear cells (PBMCs) from 10X Genomics

Data was loaded with the 'TENxPBMCData' package.

8,381 peripheral blood mononuclear cells (PBMCs) from 10X Genomics

Data was loaded with the 'TENxPBMCData' package.

33,148 peripheral blood mononuclear cells (PBMCs) from 10X Genomics

Data was loaded with the 'TENxPBMCData' package.

68,579 peripheral blood mononuclear cells (PBMCs) from 10X Genomics

Data was loaded with the 'TENxPBMCData' package.

Choose an RDS file that contains a SingleCellExperiment or Seurat object:

Please select the directory that contains your /Gene directory as your base directory.
Please select your /genecount directory as your base directory.
Please select the directory that contains your sample files as your base directory.
Please select the directory that contains the following four directories - call-MergeCountFiles, call-MergeCellMetrics, call-MergeGeneMetrics, call-RunEmptyDrops - as your base directory.

Samples to Import:

Type
Location
Sample Name
Remove


Data summary


Dataset options:

Error:

Import Gene Sets

(help)

Existing Gene Sets:

Collection Name
Source


Upload a GMT file:

Select from a database:

Import mitochondrial gene set

Paste in your gene set:

-OR-

Please fill out all the required fields
(help)

Options for editing and importing Cell Annotation data

You can either replace the existing colData or you can add/merge the new colData with the existing one.
Warning: Adding to existing colData will override the columns with same names!

Save
Changes made to the annotation must be saved before they can be used in other modules of the toolkit:
Reset
Reset annotation to point after changes were last saved:

Table of Cell Annotations

(help)

Options for editing and importing Feature Annotation data

You can either replace the existing rowData or you can add/merge the new rowData with the existing one.
Warning: Adding to existing rowData will override the columns with same names!


Save
Changes made to the annotation must be saved before they can be used in other modules of the toolkit:
Reset
Reset annotation to point after changes were last saved:

Table of Feature Annotations

Export Data

(help)


                
Choose export type
Set export specifications
Set export specifications

Remove Data

(help)

Select data to remove:

Warning: This action is inreversible.

Data QC & Filtering

(help)

Select Cell Filtering Criteria:

Annotation Name
Filter Condition
Remove


Select Feature Filtering Criteria:

Assay Name
Filter Condition
Remove




Before Filtering:

After Filtering:

Normalization & Batch Correction

Normalization Options

(help)

Options

Assay Options:

Select Options:
Normalize Options:
Transformation Options:
Pseudocounts Options:
To add a pseudovalue, must select 'Normalize' or 'Transform' options!
Scale Options:
Trim Options:

Selected Options:

Normalize
Transform
Scale
Pseudocounts
Trim

Output Data Type:


Visualization

(What are plotted?)

Only result generated in the current session will be presented.

Error:
The top two plots shows the variance explained by the grouping of batches and user specified conditions, and the bottom two plots present the low dimension representation of the datasets. Please refer to our documentation for detatil.


Original Status

Corrected Status

Feature Selection & Dimensionality Reduction

1. Compute variability metric

(help)

2. Select number of variable features

Plot

Scatterplot showing the variability of each feature versus its average expression across all cells


highlighted features

                        
(help)

Options

Compute ElbowPlot?
Compute JackstrawPlot?
Compute HeatmapPlot?
(help)

Options

Plot

Scatterplot of cells on a 2D embedding


Clustering

(help)
Error:
A scatter plot of the selected low-dimensionality representation of the dataset will be generated, with the selected cluster labeling colored on each dot (cell).
If you are using an expression matrix or subset for calculation, please click on the cog icon on the left to specify the dimensions to plot.


Find Marker

(help)
Error:
The heatmap plots the expression level of top markers found for each cluster


Marker Genes


Loading...
Download Results

Differential Expression

(help)

Method and Matrix

Condition Setting

Three approaches of setting provided for flexibility.

Leave unselected for all the others.

Summary

The Condition of Interests:

The Control Condition:

Parameters

Visualization

Select an analysis and click on "Update All" to show the results.


Display row labels

Error:
A heatmap of the expression of DEGs in the selected cells, columns splitted by condition setting, and rows splitted by up-/down-regulation (whether log2FC is positive or negative, respectively)


Marker Genes


Loading...
Label Top DEG
Error:
Volcano plots of all identified DEGs in the selected analysis. DEG with top Log2FC could be labeled. Colors indicates the regulation


Plot the top

x

=

genes

Error:
Violin plots of the expression of top DEGs in the selected analysis. The violin plot for each DEG will be grouped by the condition setting.


Plot the top

x

=

genes

Error:
Linear regression plots of the expression of top DEGs in the selected analysis. The regression plot for each DEG will be grouped by the condition setting.


Label Cell Type

Currently only 'SingleR' method supported.

(help)

SingleR works with a reference dataset where the cell type labeling is given. Given a reference dataset of samples (single-cell or bulk) with known labels, it assigns those labels to new cells from a test dataset based on similarities in their expression profiles.

If users want to work with their customized reference dataset, please refer to SCTK's console function: runSingleR()

Gene Set Enrichment Analysis using enrichR

(help)


Download results

Pathway Analysis

(help)

Trajectory Analysis - TSCAN

(help)
A scatter plot of the selected low-dimensionality representation of the dataset will be generated, with the calculated pseudotime colored on each dot (cell). The MST is also projected to the cells.

Visualization on top genes that have significant expression changes along the pseudotime path of insterest.

A heatmap of the expression of the top DE genes along the path in the cells on the path.

A cell scatter plot showing the expression change along the pseudotime. Genes with top significance in increasing expression along the pseudotime are displayed.

A cell scatter plot showing the expression change along the pseudotime. Genes with top significance in decreasing expression along the pseudotime are displayed.

Visualization and tables of top DE genes on different branch path of the cluster of interest.

Scatter plots on the selected low-dimension representation of cells in the selected cluster, colored by the expression of top features differentially expressed in the selected branch path. Local MST overlaid.

A table of top features differentially expressed in the selected branch path of the selected cluster, with statistical metrics displayed.

Scatter plots on the selected low-dimension representation of cells in the selected cluster, colored by the recomputed pseudotime value on each of the branching path. Local MST overlaid.

Scatter plots on the selected low-dimension representation of cells in the selected cluster, colored by the expression of selected features, with the MST overlaid.

Sample Size Calculator

(help)

Celda

(help)
Loading...
Loading...

Seurat

(help)

Options

Compute HVG


Display HVG


                            

Plot


PCA

Compute ElbowPlot?
Compute JackStrawPlot?
Compute Heatmap?

Select No. of Components


ICA

Compute Heatmap?

Select No. of Components


tSNE

Plot


UMAP

Plot

Options

Group singletons?

Options

Compute marker genes that are either differentially expressed or conserved between selected groups and visualize them from the selected plots on right panel.
Only return positive markers?

Marker Genes


Loading...



Marker Gene Plots

Click on the rows of the table above to plot the selected marker genes below!

Cell Viewer

Plotting tools for data visualization.

(help)
X-Axis
Y-Axis

Perform Binning


Use Violin Plot
Plotting Region


Heatmap

Generic heatmap plotting panel for customized figure.

(help)

Assay to Plot

Import from analysis


Cell/Feature Subsetting

Only to plot cells/features of interests


Annotation Setting

Stick additional information at sides of the plot


Heatmap Setting

Settings for split, label, dendrogram, color scheme and etc.

Color Scheme


Error: