TotalSeq™ Data Analysis

 

Single-cell multiomics experiments generate a large amount of data. Choosing the optimal data analysis tool during each step of the data analysis process enables you to uncover unique biological insights from these data. Explore our resources to learn how to process and analyze data generated from experiments using TotalSeq™ antibodies.

barcode lookup page

Multiomics Analysis Software (MAS) is our free cloud-based program that allows you to quickly and easily explore CITE-seq data without extensive bioinformatics knowledge.

 

Discover MAS

Overview of the Analysis Pipeline

 

Sequencing

RNA, HTO, and ADT libraries are sequenced.
Output: BCL files (raw sequencing data)
 

Primary Data Processing

Tools like bcl2fastq or Cell Ranger demultiplex data using the i7 and/or i5 indices used to label your libraries.
Output: FASTQ files (one for each library)

 

Read more

Primary data processing (BCL to matrix file)

 

The single-cell libraries (GEX/ADT/HTO/VDJ) are sequenced on Illumina instruments, which generate raw base call files (BCL) as primary output. The per-cycle BCL files need to be translated to a per-read FASTQ file before proceeding with most analysis pipelines. The two most common conversion methods include Cell Ranger mkfastq and bcl2fastq.

 

Sequencing library multiplexing and multi-flow cell sequencing

 

In addition, multiplexed libraries (more than one library sequenced in a single flow cell) or libraries sequenced across multiple flow cells, can be demultiplexed during the FASTQ translation to yield the appropriate FASTQ files with the respective sample libraries. Note that combining or demultiplexing the samples within the libraries using TotalSeq™ hashtags happens during the subsequent multi/count pipeline steps (see below).

 

 

Quantification

Convert FASTQ files to expression matrices using Cell Ranger, CITE-seq-count, Kallisto, STAR, or other community-developed tools.
Output: Expression matrix in MTX file format

 

Read more

FASTQ to count matrices and web summary

 

The FASTQ files are then typically run through the Cell Ranger count pipeline or similar, in which the reads are aligned and filtered, and the barcode and UMI sequences are counted. This pipeline produces a variety of file types such as BAM, matrix files, and summary files. If there are multiple samples within the libraries that have been multiplexed using TotalSeq™ hashtags, after FASTQ conversion the samples can be run through the Cell Ranger count pipeline or multi pipeline*. 

 

 

*Note: Cell Ranger multi does not currently support HTO + VDJ data, please refer to the BioLegend notebook for more information.

 

The Cell Ranger pipeline produces a Web summary.html file that contains metrics and automated secondary analysis results that can be useful for assessing both library and sample quality.

 

Run Summary CSV

 

BAM/BAM Index

 

Raw and filtered feature-barcode matrices – Unfiltered (raw) and filtered feature-barcode matrices are output in both the Market Exchange format (MEX) and Hierarchical Data Format (HDF5). The HDF5 format matrices are the most commonly used and are typically the primary input for sequential analysis pipelines such as MAS, R (Seurat), and Python software. The unfiltered matrix contains every barcode from the fixed list of known barcode sequences that have at least one associated read. This includes background and cell-associated barcodes. The filtered matrix only contains cell-associated barcode sequences and is the primary input to the MAS analysis pipeline.

 

For more information on the file types that are not discussed here, visit the 10x Cell Ranger output review.

 

Downstream Analyses

The cloud-based Multiomics Analysis Software (MAS) software from BioLegend can be used to perform downstream analyses including normalization, cell detection, multiplet removal, and differential protein and gene expression removal.

 

Read more

Secondary Analysis

 

Dimensionality Reduction

 

Typical CITE-seq data sets are highly dimensional and contain information for thousands of genes, ADT and/or HTO reads, which are denoted as columns/features. They can also contain anywhere between 1,000 to 50,000+ cells (rows/observations), depending on the experimental design. Dimensionality reduction techniques help reduce the data complexity and aid in the visualization of this high-dimensional data.  The most common dimensionality reduction methods used in CITE-seq include t-SNE or UMAP methods, both of which are non-linear methods that project data in the high-dimensional space into two (or more) dimensional space to enable visualization. Broadly speaking, these methods attempt to preserve local neighborhoods observed in the high-dimensions when projecting into the lower dimensions; i.e. cells that are close to each other in the high-dimensional space are typically close to each other in the lower dimensional space. As a result, clusters of cells are easier to visualize and probe them for co-expression patterns.

 

Normalization and identifying RNA with sufficient variance 

 

Data normalization is a crucial part to the analysis of CITE-seq datasets data set since it helps reduce sequencing noise and bias that is present due to the inherent nature of this assay (i.e. gene length, GC content, sequencing depth, etc.). There are many methods available but most focus on correcting for the difference in RNA abundance related to the size of the cells. Once normalized the read counts more accurately reflect the differences in biology of the samples/cells rather than cell volume.

 

Clustering

 

Clustering of cells aids in understanding the cellular heterogeneity within datasets. Clustering involves the grouping of cells based on their “similarities”, found within the gene expression and/or ADT expression profiles of those cells.

 

 

Visualization

After performing downsteam data analysis, visualize your data and create publication quality images using tools like MAS, Loupe Browser, Seurat, and Scanpy.
 
 

Tech Insights: TotalSeq™ Data Analysis

 

Single-cell multiomics experiments generate a large amount of sequencing data that can be overwhelming. Get technical insights from one of our experts, Roman Magallon, to learn more about the multiomics analysis workflow and how you can transform sequencing data into novel biological insights.

Tools for Multiomics Analysis

 

BCL files
Fastq files
Raw counts
Cell Detection
Sample Demultiplexing
Clean, demultiplexed UMI Counts
 
Normalization/Harmonization
Cluster analysis
1 vs. rest DE analysis
Visualization

BCL2fastq

Cell Ranger

Kallisto/
KITE

CITEseq-count

STAR

Seurat

Scrublet

scvi-tools

MAS

DemuxEM

Scanpy

VISION

Loupe Browser

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Explore these community-developed and commercially available data analysis resources to learn more about multiomics data analysis tools.

 

 

Analysis Guides

 

 

Our bioinformaticists developed detailed, step-by-step examples of common analysis pipelines for the use of TotalSeq antibodies. Please note that while we can provide general guidance on using community-developed tools for data analysis, we do not guarantee the performance of these tools.

 

Demultiplexing with Hashtags Notebook

  • This notebook provides instruction on how to demultiplex datasets that have been pooled using our hashtag reagents.
  • By running the data through this pipeline, you can read the Cell Ranger output and properly demultiplex the dataset. It also provides a variety of plots to indicate the performance of your hashtag reagents.
  • The notebook works with all formats of our TotalSeq hashtag reagents, as long as you use the HTO_XXXXX format for hashtag IDs, which is provided in the example Cell Ranger sheet.
Download the notebook
 

VDJ Notebook

  • The VDJ Notebook provides instruction regarding how to demultiplex datasets with hashtags and incorporate corresponding VDJ results as metadata columns.
  • The samples are demultiplexed based on cellranger count results. Next, the number of chains detected, the VDJ gene expression, and the assigned clonotype are extracted from the Cell Ranger VDJ results.
  • The output of the notebook can be visualized in cloupe or cellxgene for further analysis.

Download the notebook

 

BEN-seq Notebook

  • This notebook provides instruction on how to analyze bulk epitope and nucleic acid sequencing (BEN-seq) data generated using our TotalSeq-A antibodies.

Download the notebook

 

Download BEN-seq TotalSeq Analysis Pipeline User Guide

 

Example Datasets

 

 

Fill out this form to access our downloadable example datasets to explore data analysis pipelines without running an experiment.

 

 

 

 

ProductsHere

Login / Register
Remember me
Forgot your password? Reset password?
Create an Account