...

The Hashtag demultiplexing task uses is an algorithm developed by the Satija lab¹ to identify what sample each cell comes from and whether it is a multi-sample doubletimplementation of the algorithm used in Stoeckius et al. 2018¹ for multiplexing cell hashing data. The task adds cell-level attributes "Sample of origin" and "Cells in droplet".  

Prerequisites for running Hashtag demultiplexing

...

If you are processing your data in Partek Flow, be sure to specify a different Data type for your Cell Hashing FASTQ files on import than the FASTQ files for your gene expression and any other antibody data. 

If you want to specify each Sample of origin instead of using the hashtag feature ID, you will need to prepare a Sample ID .csv file with the hashtag feature ID in the first column and the corresponding sample ID in the second column. 

Running Hashtag demultiplexing

• Click the Normalized counts data node for your cell hashing data
• Click Hashtag demultiplexing in the Pre-analysis tools section of the toolbox
• Click Browse to select your Sample ID file (Optional)
• Click Finish to run

The output is a Dumultiplexed counts data node. Two cell-level attribute, Cells in droplet and Sample of origin, are added by this task and are available for use in downstream tasks. 

We recommend using Annotate cells to transfer the new attributes to other sections of your project.