Beta-Binomial Model for the Detection of Rare Mutations in Pooled Next-Generation Sequencing Experiments

Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data

Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.

Challenges and Opportunities in the Statistical Analysis of Multiplex Immunofluorescence Data

Simple Summary

Immune modulation is considered a hallmark of cancer initiation and progression, and has offered promising opportunities for therapeutic manipulation. Multiplex immunofluorescence (mIF) technology has enabled the tumor immune microenvironment (TIME) to be studied at an increased scale, in terms of both the number of markers and the number of samples. Another benefit of mIF technology is the ability to measure not only the abundance but also the spatial location of multiple cells types within a tissue sample simultaneously, allowing for assessment of the co-localization of different types of immune markers. Thus, the use of mIF technologies have enable researchers to characterize patient, clinical, and tumor characteristics in the hope of identifying patients whom might benefit from immunotherapy treatments. In this review we outline some of the challenges and opportunities in the statistical analyses of mIF data to study the TIME.

Abstract

Immune modulation is considered a hallmark of cancer initiation and progression. The recent development of immunotherapies has ushered in a new era of cancer treatment. These therapeutics have led to revolutionary breakthroughs; however, the efficacy of immunotherapy has been modest and is often restricted to a subset of patients. Hence, identification of which cancer patients will benefit from immunotherapy is essential. Multiplex immunofluorescence (mIF) microscopy allows for the assessment and visualization of the tumor immune microenvironment (TIME). The data output following image and machine learning analyses for cell segmenting and phenotyping consists of the following information for each tumor sample: the number of positive cells for each marker and phenotype(s) of interest, number of total cells, percent of positive cells for each marker, and spatial locations for all measured cells. There are many challenges in the analysis of mIF data, including many tissue samples with zero positive cells or “zero-inflated” data, repeated measurements from multiple TMA cores or tissue slides per subject, and spatial analyses to determine the level of clustering and co-localization between the cell types in the TIME. In this review paper, we will discuss the challenges in the statistical analysis of mIF data and opportunities for further research.

Pooled testing with replication as a mass testing strategy for the COVID-19 pandemics

During the COVID-19 pandemic it is essential to test as many people as possible, in order to detect early outbreaks of the infection. Present testing solutions are based on the extraction of RNA from patients using oropharyngeal and nasopharyngeal swabs, and then testing with real-time PCR for the presence of specific RNA filaments identifying the virus. This approach is limited by the availability of reactants, trained technicians and laboratories. One of the ways to speed up the testing procedures is a group testing, where the swabs of multiple patients are grouped together and tested. In this paper we propose to use the group testing technique in conjunction with an advanced replication scheme in which each patient is allocated in two or more groups to reduce the total numbers of tests and to allow testing of even larger numbers of people. Under mild assumptions, a 13 ×  average reduction of tests can be achieved compared to individual testing without delay in time.

s-dePooler: determination of polymorphism carriers from overlapping DNA pools

Background

Samples pooling is a method widely used in studies to reduce costs and labour. DNA sample pooling combined with massive parallel sequencing is a powerful tool for discovering DNA variants (polymorphisms) in large analysing populations, which is the base of such research fields as Genome-Wide Association Studies, evolutionary and population studies, etc. Usage of overlapping pools where each sample is present in multiple pools can enhance the accuracy of polymorphism detection and allow identifying carriers of rare-variants. Surprisingly there is a lack of tools for result interpretation and carrier identification, i.e. for “depooling”.

Results

Here we present s-dePooler, the application for analysis of pooling experiments data. s-dePooler uses the variants information (VCF-file) and the pooling scheme to produce a list of candidate carriers for each polymorphism. We incorporated s-dePooler into a pipeline (dePoP) for automation of pooling analysis. The performance of the pipeline was tested on a synthetic dataset built using the 1000 Genomes Project data, resulting in the successful identification 97% of carriers of polymorphisms present in fewer than ~ 10% of carriers.

Conclusions

s-dePooler along with dePoP can be used to identify carriers of polymorphisms in overlapping pools, and is compatible with any pooling scheme with equivalent molar ratios of pooled samples. s-dePooler and dePoP with usage instructions and test data are freely available at https://github.com/lab9arriam/depop.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2616-9) contains supplementary material, which is available to authorized users.

Group screening for rare events based on incomplete block designs

Fields such as, diagnostic testing, biotherapeutics, drug development, and toxicology among others, center on the premise of searching through many specimens for a rare event. Scientists in the business of "searching for a needle in a haystack" may greatly benefit from the use of group screening design strategies. Group screening, where specimens are composited into pools with each pool being tested for the presence of the event, can be much more cost-efficient than testing each individual specimen. A number of group screening designs have been proposed in the literature. Incomplete block screening designs are described here and compared with other group screening designs. It is shown under certain conditions, that incomplete block screening designs can provide nearly a 90% cost saving compared to other group screening designs such as when prevalence is 0.001 and screening 3876 specimens with an ICB-sequential design vs. a Dorfman design. In other cases, previous group screening designs are shown to be most efficient. Overall, when prevalence is small (≤0.05) group screening designs are shown to be quite cost effective at screening a large number of specimens and in general there is no one design that is best in all situations. © 2018 American Institute of Chemical Engineers Biotechnol Progress, 35: e2770, 2019.