Single Cell Toolkit v2.8.1

Upload data in tab separated text format:

Example count file:

Gene	Cell1	Cell2	…	CellN
Gene1	0	0	…	0
Gene2	5	6	…	0
Gene3	4	3	…	8
…	…	…	…	…
GeneM	10	10	…	10

Download an example count file here.

Input assay (eg. counts, required): *

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Input Assay Type:

Example cell annotation file:

Cell	Annot1	…
Cell1	a	…
Cell2	a	…
Cell3	b	…
…	…	…
CellN	b	…

Download an example annotation file here.

Cell annotations (optional):

Browse...

Example feature file:

Gene	Annot2	…
Gene1	a	…
Gene2	a	…
Gene3	b	…
…	…	…
GeneM	b	…

Download an example feature file here.

Feature annotations (optional):

Browse...

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:

SingleCellExperiment or Seurat RDS file:

Browse...

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.

Upload a GMT file:

Choose GMT File

Browse...

Collection Name

Select from a database:

Check the gene sets you want to import

Import mitochondrial gene set

Species

human mouse

ID Type

Collection Name

Paste in your gene set:

Create a new collection (enter collection name)

-OR-

Add to an existing collection

Please enter values separated by new lines

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:

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:

Choose export type

Download as RDS file

Python annData object

File Name

Download

Set export specifications

Select input matrix:

Warning: This action is inreversible.

Filter Condition

Normalization Options

(help)

Select normalization method:

Options

Select input matrix:

Set scaling factor:

Assay Name:

Scale data after normalization?

Trim data after scale?

Upper trim value:

Lower trim value:

Assay Options:

Select input matrix:

Assay Name

Select Options:

Normalize

Normalize Options:

Select normalization method:

Transform

Transformation Options:

Select transformation method:

Psuedocounts

Pseudocounts Options:

before normalization

Enter a pseudovalue to add:

before transformation

Enter a pseudovalue to add:

To add a pseudovalue, must select 'Normalize' or 'Transform' options!

Scale

Scale Options:

Select scaling method:

Trim

Trim Options:

Specify upper trim value

Specify lower trim value

Selected Options:

Normalize

Transform

Scale

Pseudocounts

Trim

Output Data Type:

Visualization

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

1. Compute variability metric

(help)

Select method for calculating variability of features:

Select input matrix:

2. Select number of variable features

Select variability metric:

Select the number of highly variable features:

Name of the feature subset:

Plot

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

highlighted features

Options

Select input matrix:

Select method:

Select HVG list:

Scale

Number of dimensions:

Compute ElbowPlot?

Compute JackstrawPlot?

Compute HeatmapPlot?

Select number of features for heatmap plot:

Seed value for reproducibility of result:

Options

Select input matrix:

Select method:

Log Normalize the data

Use HVG list

Scale assay data

Run PCA on assay data

Number of dimensions to use:

# of iterations

# of nearest neighbors

minimum distance between points

learning rate(alpha)

spread

No. of iterations:

Set theta value:

Set perplexity:

Select reduction method:

Set perplexity:

Set min.dist:

Set n.neighbors:

Set spread:

Seed value for reproducibility of result:

Plot

Scatterplot of cells on a 2D embedding

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.

Heatmap
Result Table

The heatmap plots the expression level of top markers found for each cluster

Marker Genes

Download Results

Choose analysis method

Select input matrix:

Choose Annotation Class:

Condition 1
Condition 2

Columns to display

Leave unselected for all the others.

Summary

The Condition of Interests:

Cell IDs:

The Control Condition:

Cell IDs:

Output FDR less than:

Only up-regulated genes

Name of Differential Expression Analysis:

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)

Aboslute log2FC value greater than:

FDR value less than

Label Top DEG

N top DEG to label

Display ID Type

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

genes

Label features by

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

genes

Label features by

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.

Select input matrix:

Choose a reference

Feature type:

symbol ensembl

Label by

Each cell Clusters

Download results

Select input dimension reduction:

Select clustering result:

Seed value for reproducibility of result:

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.

Select input matrix:

Select path terminal node:

Select cluster(s) to discard (OPTIONAL):

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.

Select branched cluster of interest:

Select input matrix:

FDR less than:

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.

Select expression matrix:

Select feature(s):

Select 2D embedding for visualization:

Show cluster(s) of interest:

Display ID Type

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.

Select input matrix:

Minimum readcount to detect gene

Minimum number of cells with nonzero expression to detect gene

Number of bootstrap iterations.

Maximum log10(number of simulated reads)

Number of values to simulate

Condition for diffex

Select input matrix:

Minimum number of cells to simulate

Maximum number of cells to simulate

Number of bootstrap iterations per cellcount.

Estimated number of aligned reads

Use the same number of reads as in original dataset

Condition for diffex

Minimum readcount to detect gene

Minimum number of cells with nonzero expression to detect gene

Number of dataset sizes to simulate

Select input matrix:

How many simulated cells?

How many total reads?

Condition for diffex

Number of bootstrap iterations

Choose an Assay:

Choose Feature Selection Method:

Select HVG method:

Select number of variable features:

Keep features with this many counts:

In at least this many cells:

Select Number of Initial Feature Modules:

Select Number of Maximum Feature Modules:

Select Number of Feature Modules:

Select Number of Initial Cell Modules:

Select Number of Maximum Cell Modules:

Select Number of Cell Clusters:

Plot Type:

UMAP

TSNE

Max.cells: Maximum number of cells to plot

Min.cluster.size: Do not subsample cell clusters below this threshold

Seed:

Min.dist: Effective minimum distance between embedded points

Spread:

nNeighbors:

Max.cells: Maximum number of cells to plot

Min.cluster.size: Do not subsample cell clusters below this threshold

Perplexity:

Max.iter: Maximum number of iterations in tSNE generation

Seed:

Select module to display on heatmap:

Select number of cells to display on heatmap:

Options

Select input matrix:

Select normalization method:

Set scaling factor:

Compute HVG

Select HVG method:

Select number of features to find:

Display HVG

Select number of features to display:

PCA

Select number of components to compute:

Compute ElbowPlot?

Compute JackStrawPlot?

Compute Heatmap?

Set number of features for heatmap:

Seed value for reproducibility of result:

Select No. of Components

Select number of components for downstream analysis:

ICA

Select number of components to compute:

Compute Heatmap?

Set number of features for heatmap:

Seed value for reproducibility of result:

Select No. of Components

Select number of components for downstream analysis:

UMAP

Select reduction method:

Set min.dist:

Set n.neighbors:

Set spread:

Seed value for reproducibility of result:

tSNE

Select reduction method:

Set perplexity:

Seed value for reproducibility of result:

Options

Select reduction method:

Select clustering algorithm:

Set resolution:

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.

Select type of markers to identify:

markers between all groups

markers differentially expressed between two selected groups

markers conserved between two selected groups

Select biological phenotype:

Select first group of interest:

Select second group of interest:

Select test:

Only return positive markers?

Marker Genes

Heatmap Plot

Select number of top genes from each cluster/group to visualize in the heatmap below based on highest average log fold change value:

Marker Gene Plots

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

Options

Select input matrix:

Specify targetSum:

Specify maxFraction:

Compute HVG

Select HVG method:

Select number of features to find:

Specify minMean:

Specify maxMean:

Specify minDisp:

Set maxDisp to infinite?

Specify maxDisp:

scanpy PCA

Select PCA method:

Select number of components to compute:

Select No. of Components

Select number of components for downstream analysis:

UMAP

Set min.dist:

Set n.neighbors:

Set spread:

Set alpha:

Set gamma:

tSNE

Select reduction method:

Set perplexity:

Options

Select clustering algorithm:

Set resolution:

Set no. of neigbours:

Select correlation metric:

Options

Select biological phenotype:

Select test:

Multiple testing correction method:

Marker Genes

Heatmap Plot

Select number of top genes from each cluster/group to visualize in the heatmap below based on highest average log fold change value:

Marker Gene Plots

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

X-Axis

Data

Approach

Dimension

Select input matrix:

Feature

Annotation

Y-Axis

Data

Approach

Dimension

Select input matrix:

Feature

Annotation

Color By

Approach

Dimension

Select input matrix:

Feature

Annotation

Categorical

Continuous

High Color

Middle Color

Low Color

Color Scheme

Perform Binning

Yes

Number of Bins

Use Violin Plot

Plotting Region

Title:

Legend title:

X-axis label:

Y-axis label:

Legend title size:

Legend size:

Axis size:

Axis label size:

Opacity:

Dot size:

Use default theme

Select input matrix:

Import from analysis

Match input cell identifiers by:

Exact Match

First Match

Enter Cell Identifiers

Match input feature identifiers by:

Exact Match

First Match

Enter Feature Identifiers

Add cell/feature labels

cells features

Add cell labels from

Add feature labels from

Show dendrograms for

cells features

Z-Score Scale

Single Cell Toolkit

Import

Upload data in tab separated text format:

Example count file:

Input Assay Type:

Example cell annotation file:

Example feature file:

Choose Example Dataset:

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:

Data summary

Dataset options:

Import Gene Sets

Existing Gene Sets:

Upload a GMT file:

Select from a database:

Import mitochondrial gene set

Paste in your gene set:

-OR-

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!

Changes made to the annotation must be saved before they can be used in other modules of the toolkit:

Reset annotation to point after changes were last saved:

Table of Cell Annotations

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!

Changes made to the annotation must be saved before they can be used in other modules of the toolkit:

Reset annotation to point after changes were last saved:

Table of Feature Annotations

Export Data

Choose export type

Set export specifications

Remove Data

Select data to remove:

Warning: This action is inreversible.

Data QC & Filtering

Choose which algorithms to run:

General

Contamination Estimation

Doublet Detection

General Parameters

Quick UMAP Parameters

Select Cell Filtering Criteria:

Select Feature Filtering Criteria:

Before Filtering:

After Filtering:

Normalization & Batch Correction

Normalization Options

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:

Parameters

Visualization

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

2. Select number of variable features

Plot

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