Wormseq

Explore the transcriptional atlas of a wild type, young adult C. elegans using our single-cell app!

The app can be accessed using the following link (it may take a couple of minutes to load). For more information on the data and analyses presented here please refer to our paper.

UPDATE: the app has been updated with new datasets which include some additional cell types. The single-cell plot feature has been removed to make the app run faster and we have fixed the download issues. We have also added the results of the gene expression bootstrap resampling with replacement analysis to the gene expression by cell feature for the identification of genes that are robustly expressed in the cell type of interest. We also made the scale of the percent cell expression in the heatmap flexible for better visualization. The older and new app as well as all datasets are available through Zenodo.

Current app features

Top gene markers

Identify gene markers for cell types of interest. These markers were generated using Monocle3’s top_markers function and the table allows users to select markers based on a variety of criteria.

For users who are interested in selecting the most specific markers, we suggest using the “specificity” criterion to select their markers. On the other hand, values such as “pseudo_R2” and “marker_score” allow users to select markers that are relatively specific but also highly expressed in the cell type of interest.

We also recommend users to use this data in combination with the “Gene expression heatmap” and “Gene expression dot plots” to determine the level of expression and specificity of a certain marker. Please also note that p-values and q-values of 0 indicate a p-value or q-value that is less than 2.2×10^-6.

Gene expression by cell

Identify all the genes expressed in a cell type of interest. Gene expression values are in scaled transcripts for million (scaled TPM) as previously calculated (Packer et al. 2019). Briefly: (1) We first divided the raw UMI counts of every gene of a cell by the cell’s size factor (a value generated by CellRanger). (2) For each gene, we calculated the average normalized counts across all the cells of an annotated cell type to obtain a gene by cell type matrix. (3) We then calculated the sum of the average normalized counts across all genes for every cell type. (4) We then scaled the gene expression for every cell type to 1 by dividing the average normalized count for every gene within a cell type by the cell type’s sum calculated in (3). (5) Finally, we multiplied all values by 1,000,000 to obtain scaled TPMs.

In the latest version of the App, the gene expression table also includes the results of the gene expression bootstrap resampling with replacement analysis. Briefly, 1,000 iterations of the calculations described above were performed to obtain a median scaled TPM and 95% and 80% confidence intervals for the expression of a gene in a cell type.

Gene expression heatmap

Visualize the expression of several genes of interest across all cell types using a heatmap that gives you the levels of gene expression (color gradient) and the percentage of cells expressing the genes (dot size). Gene expression values are in scaled TPM obtained as described above. In the new version of the App, the scale of the percent cell expression in the heatmap is flexible for better visualization.

Gene expression dot plot

Visualize the expression of a gene of interest across all cell types using a dot plot that gives you the levels of gene expression (y-axis) and the percentage of cells expressing the gene (x-axis). Gene expression values are in scaled TPM obtained as described above.

Percentage of gene expression

Identify genes that are expressed in one cell type but not in another using percentage of cells. Users can set a minimum threshold for the first cell type and a maximum threshold for the second cell type. The app will produce three tables: (1) genes expressed in the first cell type above the set threshold, (2) genes expressed in the second cell type below the set threshold, (3) genes expressed above the set threshold in the first cell type and expressed below the set threshold in the second cell type.

Housekeeping gene look-up

Identify potential housekeeping genes using the following criteria: (1) Skewness score indicates abundance across cell types. A lower skewness score indicating higher abundance. (2) Gini coefficient indicates consistency of expression across cell types. A lower Gini coefficient indicating higher consistency of expression across cell types. (3) Filter genes by their identification as potential housekeeping genes in L2 data. (4) Filter genes identified as essential in RNAi screens.

Transcription factor analysis

Identify transcription factors predicted to be active in cell types of interest as well as the sites of action for transcription factors of interest. We inferred transcription factor activity by correlating transcription factor binding patterns obtained by ChIP-Seq with our cell type-specific gene expression profiles. Users can set the upper end of the color gradient to facilitate visualization (recommended range 0.04-1) and can hover over the tiles to reveal the TF-cell type correlation score. For more information on how the transcription factor analysis was performed please refer to our manuscript.

Cell-cell interaction analysis

Identify putative ligand-receptor pairs mediating the interaction between cell types of interest. The communication score reflects the level of expression of the ligands/receptors in the cell types of interest. The LR class indicates whether the ligand-receptor pairs are known to be “membrane-bound”, “secreted” or “ECM component”. Finally, the adjusted p-value indicates whether the ligand-receptor pair is significantly enriched in the cell types of interest. For more information on how the cell-cell interaction analysis was performed please refer to our manuscript.