ImmunoCluster provides a computational framework for the non-specialist to profile high- dimensional cytometry data
Abstract
High dimensional cytometry is an innovative tool for immune monitoring in health and disease, it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of large multiparametric datasets usually requires specialist computational knowledge. Here we describe ImmunoCluster (https://github.com/kordastilab/ImmunoCluster) an R package for immune profiling cellular heterogeneity in high dimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a non-specialist. The analysis framework implemented within ImmunoCluster is readily scalable to millions of cells and provides a variety of visualization and analytical approaches, as well as a rich array of plotting tools that can be tailored to users' needs. The protocol consists of three core computational stages: 1, data import and quality control; 2, dimensionality reduction and unsupervised clustering; and 3, annotation and differential testing, all contained within an R-based open-source framework.
Data availability
The liquid mass cytometry dataset is avaiable from FlowRepository (http://flowrepository.org/id/FR-FCM-Z244).Imaging and flow cytometry datasets have been deposited to Dryad under the following DOIs: 10.5061/dryad.gf1vhhmpr, 10.5061/dryad.4b8gthtcf, 10.5061/dryad.3n5tb2rhd
-
Imaging mass cytometry data: Head and neck squamous cell carcinoma tissue sectionDryad Digital Repository, 10.5061/dryad.gf1vhhmpr.
-
Flow cytometry data: healthy donor bone marrow taken during hip surgeryDryad Digital Repository, 10.5061/dryad.4b8gthtcf.
-
Imaging mass cytometry data: Diffuse large B-cell lymphoma lymph node sectionDryad Digital Repository, 10.5061/dryad.3n5tb2rhd.
Article and author information
Author details
Funding
Cancer Research UK (A29283)
- Jessica A Timms
- Shahram Kordasti
Aplastic Anemia and MDS International Foundation
- Jessica A Timms
- Shahram Kordasti
Blood Cancer UK
- Jessica A Timms
- Shahram Kordasti
H2020 European Research Council (335326)
- James W Opzoomer
- James N Arnold
Medical Research Council (MR/N013700/1)
- James W Opzoomer
Medical Research Council (Doctoral Training Partnership in Biomedical Sciences)
- James W Opzoomer
Rosetrees Trust (M117-F2)
- Sedigeh Kareemaghay
- Mahvash Tavassoli
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Formalin-fixed paraffin-embedded (FFPE) DLBCL tumor tissue was obtained from King's College Hospital, in accordance with the Declaration of Helsinki and approved by the UK National Research Ethics Committee (reference 13/NW/0040).Head and neck squamous cell carcinoma (HNSCC) tissue was obtained from King's College Hospital, consent was attained by the King Guy's & St Thomas' Research Biobank, within King's Health Partners Integrated Cancer Centre.The non-interventional study which collected bone marrow samples from elderly healthy donors was approved by the ethical committee of Cochin-Port Royal Hospital (Paris, France) (CLEP Decision N{degree sign}: AAA-2020-08039).
Copyright
© 2021, Opzoomer et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 4,063
- views
-
- 422
- downloads
-
- 14
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Computational and Systems Biology
- Neuroscience
Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions - the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS) - while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal's choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally-inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.
-
- Computational and Systems Biology
- Ecology
Environmental DNA (eDNA) is becoming an increasingly important tool in diverse scientific fields from ecological biomonitoring to wastewater surveillance of viruses. The fundamental challenge in eDNA analyses has been the bioinformatical assignment of reads to taxonomic groups. It has long been known that full probabilistic methods for phylogenetic assignment are preferable, but unfortunately, such methods are computationally intensive and are typically inapplicable to modern Next-Generation Sequencing data. We here present a fast approximate likelihood method for phylogenetic assignment of DNA sequences. Applying the new method to several mock communities and simulated datasets, we show that it identifies more reads at both high and low taxonomic levels more accurately than other leading methods. The advantage of the method is particularly apparent in the presence of polymorphisms and/or sequencing errors and when the true species is not represented in the reference database.