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The workbench for single cell RNAseq (scRNAseq) is designed to allow biologists meaningful access to single cell data, even with limited informatics training. The workbench begins by selecting a dataset for analysis, and then offers analysis tools following several standard pre-processing steps.
Start by choosing a dataset on the left.
You have a selected an analysis bundled with the dataset itself, usually created by the dataset author outside of this workbench. You can use the workbench to perform actions below provided by the analyses the authors have uploaded, but all other analysis steps will be disabled until you create an entirely new analysis.
The method dropdown menu lists the available statistical tests for your comparison. The “t-test overestimated variance” option may help in situations where clusters contain few cells and variance is difficult to estimate directly, otherwise for robust clusters choose “t-test” (Assumes normally distributed data). The “Wilcoxon-Rank-Sum” option is a non-parametric test and may be helpful when a dataset includes a few genes with very high expression (outliers) or data distribution is not known. Multiple testing correction can be performed either by Benjamini-Hochberg or Bonferroni methods, where Bonferroni is more conservative.
| Name | log2FC | P-value | FDR | No data to display |
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| Name | log2FC | P-value | FDR | No data to display |
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This option will show you the marker genes within each group of cells as defined by the clustering method used. You can adjust the number of genes you would like to see for each cluster by adjusting the N.
Select desired marker genes in the table above and/or type gene symbols (separated by commas) in the field below to visualize
Enter a gene of interest to see its tSNE colored both by expression and cluster / cell type.
Unable to load this image. Perhaps that gene is not found in this dataset?
| Group | Num Cells | Markers | New label |
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The Louvain clustering is used to find the most likely groups of associated cells within a network. Here they are color-coded. The number of neighbors will have an effect on the smallest possible size for a cluster. If you are interested in groups of cells that are all larger than 20 cells, for example (based on the gene coloring in the initial PCA) – then you can try 6, 10 or 15 neighbors, for example. However, if this is a smaller dataset of regular RNA-seq, for example, with only biological triplicates, starting with two neighbors makes more sense – because the smallest ‘natural’ group should be 3 replicates. Alternatively, if some of the populations in a single cell dataset are very small, again, 3 neighbors could be a useful approach. However, the smaller the number of neighbors, the larger the number of clusters.
The resolution determines how granular the clustering will be. It is set to 1.3 by default. To decrease resolution you can drop it to 1, for example. Or increase the number for higher resolution.
Filtered shape: genes x obs
No mitochondrial genes with this prefix were found. This could be real, or it could be just because this prefix is case-sensitive. Common options are mt-, Mt- or MT-. (This should be handled for you automatically in a later release.)
Initial shape:
Filtered shape:
Apply filters as desired.
Loading initial gene/cell count plots