...

Cell Hashing enables sample multiplexing and super-loading in single cell RNA-Seq isolation by labeling each sample with a sample-specific oligo-tagged antibody against a ubiquitously expressed cell surface protein. 

The Hashtag demultiplexing task is an implementation of the algorithm used in Stoeckius et al. 2018¹ for multiplexing cell hashing data. The task adds cell-level attributes "Sample of origin" and " and Cells in droplet".  

Prerequisites for running Hashtag demultiplexing

To run Hashtag demultiplexing, your data must meet the following criteria:

1) Cell Hashing features must have a different data type than your gene expression or protein expression data

...

• Data node contains number of features less than number of observations
• Data node must be output from normalization task (recommended normalization method for hashtag is CLR)

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

If you are processing your data in Partek FlowFASTQ files using Cell Ranger, be sure to specify a different Data different feature_type for your Cell Hashing FASTQ files on import than the FASTQ files for your gene expression and any other antibody dataantibodies than any other antibodies in the Feature Reference CSV File. 

If you want to specify each Sample of origin sample IDs instead of using the hashtag feature IDIDs as the sample IDs, you will need to prepare a Sample ID .csv file with the hashtag feature ID tab-delimited text file (.txt) with hashtag feature IDs in the first column and the corresponding sample ID IDs in the second column (Figure 1). A header row is required. 

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 (Figure 2).

Two cell-level attributeattributes, Cells in droplet and Sample of origin, are added by this task and are available for use in downstream tasks. You can download the attribute values for each cell by clicking the Demultiplexed counts data node, clicking Download, and choosing to download Attributes only.

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

It is also possible to use the Merge matrices task to combine your data types and attributes. 

References

1. Stoeckius, M., Zheng, S., Houck-Loomis, B., Hao, S., Yeung, B.Z., Mauck, W.M., Smibert, P. and Satija, R., 2018. Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome biology, 19(1), p.224.

...