Abstract
Interpreting tissue architecture plays an important role in gaining a better understanding of healthy tissue development and disease. Novel molecular detection and imaging techniques make it possible to locate many different types of objects, such as cells and/or mRNAs, and map their location across the tissue space. In this review, we present several methods that provide quantification and statistical verification of observed patterns in the tissue architecture. We categorize these methods into three main groups: Spatial statistics on a single type of object, two types of objects, and multiple types of objects. We discuss the methods in relation to four hypotheses regarding the methods' capability to distinguish random and non-random distributions of objects across a tissue sample, and present a number of openly available tools where these methods are provided. We also discuss other spatial statistics methods compatible with other types of input data.
Highlights
A range of new imaging-based methods make it possible to explore the architecture of tissue samples both at the transcriptomics and proteomics level
The second step in interpretation is to be able to quantify relationships and patterns in an unbiased and reproducible way, and provide confidence measures for observed patterns as compared to a more randomized organization. This is often referred to as spatial statistics. In this mini-review, we focus on spatial statistics applicable to tissue data independent of image resolution
We focused on methods where the input data can be described as points in 2D tissue space representing the presence of different object types
Summary
A range of new imaging-based methods make it possible to explore the architecture of tissue samples both at the transcriptomics and proteomics level. We consider two types of objects, and their potential interaction or repulsion This is illustrated in Figure 1H3, and the hypothesis is H3: Objects of type A and B are non-randomly distributed in relation to one another within the distribution of other objects in the same tissue sample. In this case, we pose hypothesis H4: There are groups of object types (‘niches’) that are non-randomly distributed within the distribution of other objects in the tissue sample. We group different spatial statistics methods according to what types of tissue patterns they investigate, and summarize and discuss their theoretical ability to answer the four hypotheses we pose above
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