TrailmakerTM is a user-friendly tool that supports data processing, analysis and figure generation for single cell RNA sequencing (scRNA-seq) data from multiple technologies, including Parse Biosciences’ EvercodeTM WT kits. As an alternative option to running the FASTQ file processing pipeline using Trailmaker, the pipeline can be run on a Linux operating system, either locally or on a server (for more details, see here). This article outlines the recommendations for using Trailmaker figures, accurately reporting the methods used by Trailmaker for data processing, and for correctly citing the Trailmaker platform and/or the Parse Biosciences pipeline in your publication.
Reporting the methods used in Trailmaker and/or the Parse pipeline
When including scRNA-seq data in a publication, it is essential to report the details of the data analysis methods, such that other researchers can reproduce your findings.
An example methods statement for FASTQ file processing in the Parse Biosciences Pipeline, either in Trailmaker’s Pipeline module or by local installation of the pipeline is provided below.
FASTQ files were processed using TrailmakerTM pipeline module (https://app.trailmaker.parsebiosciences.com/; pipeline v1.5.0, Parse Biosciences).
OR
FASTQ files were processed using the Parse Biosciences Pipeline (v1.5.0, Parse Biosciences).
The pipeline version used to process your FASTQ files is stated at the bottom of the HTML reports that are output from a successful pipeline run. In Trailmaker, these are displayed in the Pipeline Outputs page following your successful pipeline run, as shown below.
When reporting the methods used for downstream analysis and visualization in the Insights module of Trailmaker, it is important to specify the data processing settings including filtering thresholds, integration method, and embedding and clustering settings. These values can be accessed in the Data Processing tab or by downloading the data processing settings (.txt file) from the Insights module Project Details page:
An example methods paragraph for Insights module analysis is provided below for guidance. Note that ‘X’ should be replaced by the relevant data processing settings for your project:
The single cell RNA-seq dataset was processed, explored and visualized using TrailmakerTM (https://app.trailmaker.parsebiosciences.com/; Parse Biosciences, analysis completed on DATE). Unfiltered count matrices were uploaded to Trailmaker, and background was removed by setting a minimum transcripts per cell threshold on a per sample basis (threshold range: X to X). Dead or dying cells were removed by filtering barcodes with high mitochondrial content (X% cut-off). Outliers in the distribution of number of genes vs number of transcripts were removed by fitting a linear or spline [delete as appropriate] regression model (p-values between X and X). Cells with a high probability of being doublets were filtered out using the scDblFinder method (threshold range: X to X). Overall filtering rates after processing are in the range of X to X% of cells. Data normalization, principal-component analysis (PCA) and data integration using Harmony were performed on data from high-quality cells. Clusters were identified using the Leiden OR Louvain method, and a Uniform Manifold Approximation and Projection (UMAP) embedding was calculated to visualize the results. Cluster-specific marker genes were identified by comparing cells of each cluster to all other cells using the presto package implementation of the Wilcoxon rank-sum test.
Citing Trailmaker in the body of the text and references section
For in-text citation, it is recommended that you reference Trailmaker as follows:
"TrailmakerTM (Parse Biosciences) was used to complete our single cell RNA-sequencing data analysis."
For the full citation in the references section of your manuscript, you can state the following, where DATE should reflect the date that you completed your analysis in Trailmaker:
"TrailmakerTM, Parse Biosciences, Seattle, U.S.A.; available at http://app.trailmaker.parsebiosciences.com; analysis completed on DATE”
Using figures generated in Trailmaker
The Plots and Tables tab within Trailmaker’s Insights module facilitates the plotting and full customization of scRNA-seq figures for inclusion in research publications, enabling full user control over the plot data (e.g., clusters and genes) and plot features such as dimensions, axis ranges and titles, legends, colors, titles and labels. We recommend that you perform the figure customization within Trailmaker, then use the option to save the plot as a high resolution SVG image file for inclusion in your paper. The example below shows how to save an SVG image file from a customized dot plot:
Note that, if needed, SVG images can be converted to more common image formats such as JPEG or PNG using tools like Inkscape or Adobe Illustrator®.
Citing tutorial articles from the Parse Biosciences support suite
Data analysis tutorials that are available on the Parse Biosciences support suite, including the Workflow for Processing Long Read Data and RNA Velocity Analysis via scVelo, can be cited following the format below:
Parse Evercode and droplet based data was integrated into a single analysis following the tutorial article provided by Parse Biosciences (https://support.parsebiosciences.com/hc/en-us/articles/22499300829204-How-to-integrate-Parse-Evercode-and-droplet-based-data-for-analysis-in-Trailmaker, Parse Biosciences, accessed DATE).
Promoting your publication
With your permission, we would be delighted to promote your publication through our social media pages. Please reach out to us at support@parsebiosciences.com or through your local FAS team member.