Produce Searchable Output Files

At a Glance

HISE supports a variety of analysis pipelines that generate searchable output files. These files contain analysis results plus one or more reports. This document discusses the specific files each analysis pipeline produces. If you have questions, contact Support.

	Abbreviations Key
ADT	antibody-derived tag			PB	probability binning
ARC	ATAC + RNA chromium			PBMCs	peripheral blood mononuclear cells
archR	analysis of regulatory chromatin in R			scATAC-seq	single-cell assay transposase-accessible chromatin sequencing
BMMCs	bone marrow mononuclear cells			scRNA-seq	single-cell RNA sequencing
CITE-seq	cellular indexing of transcriptomes and epitopes by sequencing			SLIMS	simplified laboratory information management system
CSV	comma-separated value			TSS	transcription start site
ELN	electronic laboratory notebook			tsv.gz	tab-separated values, GNU zip
FQ	FASTQ			UMI	unique molecular identifier
GS	gating set			V(D)J	variable, (diversity), and joining [gene segments]
HTO	hashtag oligo

Methods

• TEA-seq combinations
• scATAC-seq
• scRNA-seq
• Fixed RNA
• CITE-seq
• V(D)J-seq
• Flow Cytometry
• CyAnno
• Olink Proteomics
• Xenium

TEA-seq combinations

TEA-seq is a trimodal single-cell assay that simultaneously measures transcriptomics, protein epitopes, and chromatin accessibility. This assay identifies cell type–specific gene regulation and expression grounded in phenotypically defined cell types. 

In the TEA-seq  pipeline, we couple Cell Ranger ARC with a rigorous QC process to ensure that cells from the TEA-seq pipeline are high quality, to reduce the number of doublets, and to make cells available in a variety of formats for downstream analysis.

In addition to TEA-seq, HISE supports the analysis of data generated by hashed TEA-seq and by CITE-seq + scRNA-seq. These methods integrate multiple layers of biological information at the single-cell level to yield a comprehensive view of cellular functioning.

These techniques produce the same searchable output files as scRNA-seq and ATAC-seq (see above), as well as the files listed in Table 3.

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scATAC-seq

In the scATAC-seq pipeline, we implement Cell Ranger alignment, followed by a rigorous quality control process to ensure that cells from the scATAC-seq pipeline are high quality, to reduce the number of doublets, and to make the cells available for downstream analysis in a variety of formats (.arrow, fragments.tsv.gz, and H5-formatted count matrices).

The pipeline results files are not immediately available for further analysis, but require review and approval by a dedicated team of scientists. Once approved, the data is put through a labeling process using archR to provide an initial cell-type label for each cell. 

The specific searchable output files for the scATAC-seq pipeline are summarized in the following table.

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scRNA-seq

In a simple scRNA-seq pipeline, the core scientific analysis method is CellRanger alignment. This analysis produces a report and an output H5 file.

In the cell hashing pipeline, where a number of samples have been barcoded and then mixed, CellRanger alignment is needed. In addition, a barcode recognition and counting process is used to identify the origin of each cell so that the results can be rearranged to produce an output H5 file for each sample.  Both pipelines end with a labeling process, using a Seurat-based normalization and labeling method. 

The specific searchable output file types for the scRNA-seq pipeline are summarized in the following table.

Fixed RNA

The Fixed RNA pipeline takes FASTQ files from short-read sequencers and applies a number of steps to generate a decorated H5 file. The initial processing is done by the 10x Genomics Cell Ranger tool, which produces a cell-by-gene matrix and performs demultiplexing if necessary. The pipeline then adds extra metadata to these H5 files and generates a QC report. This QC report determines if any samples, wells, or pools should be excluded from downstream processing.

Once the samples are approved, the pipeline merges the multiplexed samples into a single H5 file per sample. This consolidation is followed by cell-type labeling, using one of the currently available references (PBMCs or BMMCs). This final step in the pipeline produces a decorated H5 file with additional metadata and cell-type labels.

The searchable output files are summarized in the following table.

CITE-seq

CITE-seq integrates both ADT and transcriptomic (RNA) workflows, generating results that include file groups from each pipeline. This dual approach profiles surface proteins and gene expression simultaneously, so the output files represent a comprehensive combination of both data types. The Tag Counts CSV (tag-counts-csv) file for CITE-seq data contains ADT counts for each cell barcode, letting researchers quantify relative surface protein abundance at single-cell resolution. The file is structured as a matrix, with cell barcodes as rows and antibody/epitope tags as columns. Each entry shows the UMI (or read) count for a given antibody in a given cell. For other files, see the scRNA section.

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V(D)J-seq

In the V(D)J-seq pipeline, the T cell receptor (TCR)/B cell receptor (BCR) contig information also comes with scRNA-seq data. The core scientific analysis method is also Cell Ranger alignment. This is a multimodal pipeline. Cell Ranger multi aligns the scRNA and contig sequence and improves cell calling. The Cell Ranger alignment produces an H5 output file for scRNA, and CSV files for both TCR and BCR contig information.  

In the cell hashing pipeline, scRNA-seq data are processed the same as in the simple scRNA-seq pipeline. The contig CSV file of TCR/BCR is demultiplexed by the HTO barcodes and merged with each sample in the pool.  

After the scRNA and contig file are dehashed and merged, the V(D)J pipeline adds the contig information for TCR/BCR, arranged by cell, into the metadata of an H5 file. The V(D)J pipeline also produces the TCR/BCR CSV files arranged by contig. The labeling pipeline is the same as for scRNA-seq, and the labels are based on scRNA-seq data. 

The specific searchable output files are shown in the following table.

Flow Cytometry

Flow Cytometry searchable output files provide structured, standardized representations of experimental data and analysis results from flow cytometry assays in order to facilitate rapid data interpretation and downstream analysis. The files are typically formatted for compatibility with widely used platforms like FlowJo and other cytometry analysis tools.

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CyAnno

The CyAnno pipeline is a machine learning framework that uses various models for each panel to label the cell types from a dataset. It requires no intermediate QC step. 

The specific searchable output files for this pipeline are shown in the following table.

Olink Proteomics

In the pipeline for Olink Proteomics, Olink provides a raw results file and a PDF report on data that's missing because of an analysis problem. The appropriate samples are associated with either the results file or the missing data report.

The specific searchable output files are shown in the following table. 

Xenium

The Xenium pipeline enables spatial transcriptomics analysis within HISE, converting raw Xenium data into formats that can be analyzed, explored, and visualized in HISE. For a list of Xenium output files, see Table 6 in the Xenium documentation. 

Related Resources

Understand Automated Pipelines

Configure a Pipeline (Tutorial)