Back

A comparative analysis of quantitative metrics of root architectural phenotypes

Rangarajan, H.; Lynch, J.

2020-12-02 plant biology
10.1101/2020.12.01.406827 bioRxiv
Show abstract

High throughput phenotyping is important to bridge the gap between genotype and phenotype. The methods used to describe the phenotype therefore should be robust to measurement errors, relatively stable over time, and most importantly, provide a reliable estimate of elementary phenotypic components. In this study, we use functional-structural modeling to evaluate quantitative phenotypic metrics used to describe root architecture to determine how they fit these criteria. Our results show that phenes such as root number, root diameter, lateral root branching density are stable, reliable measures and are not affected by imaging method or plane. Metrics aggregating multiple phenes such as total length, total volume, convexhull volume, bushiness index etc. estimate different subsets of the constituent phenes, they however do not provide any information regarding the underlying phene states. Estimates of phene aggregates are not unique representations of underlying constituent phenes: multiple phenotypes having phenes in different states could have similar aggregate metrics. Root growth angle is an important phene which is susceptible to measurement errors when 2D projection methods are used. Metrics that aggregate phenes which are complex functions of root growth angle and other phenes are also subject to measurement errors when 2D projection methods are used. These results support the hypothesis that estimates of phenes are more useful than metrics aggregating multiple phenes for phenotyping root architecture. We propose that these concepts are broadly applicable in phenotyping and phenomics.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Frontiers in Plant Science
256 papers in training set
Top 0.1%
18.2%
2
Plant Direct
95 papers in training set
Top 0.1%
14.9%
3
Journal of Experimental Botany
219 papers in training set
Top 0.6%
9.6%
4
in silico Plants
27 papers in training set
Top 0.1%
7.8%
50% of probability mass above
5
Plant, Cell & Environment
78 papers in training set
Top 0.5%
4.8%
6
AoB PLANTS
13 papers in training set
Top 0.1%
4.0%
7
Plant Physiology
238 papers in training set
Top 2%
3.4%
8
PLOS ONE
5266 papers in training set
Top 38%
3.2%
9
Crop Science
18 papers in training set
Top 0.2%
2.4%
10
New Phytologist
346 papers in training set
Top 3%
2.1%
11
Plant and Soil
18 papers in training set
Top 0.2%
2.1%
12
Plant Phenomics
18 papers in training set
Top 0.1%
2.1%
13
Scientific Reports
3612 papers in training set
Top 51%
1.9%
14
Agronomy
18 papers in training set
Top 0.4%
1.9%
15
Plant Methods
42 papers in training set
Top 0.4%
1.7%
16
Planta
18 papers in training set
Top 0.5%
1.3%
17
Physiologia Plantarum
39 papers in training set
Top 1.0%
1.1%
18
The Plant Journal
215 papers in training set
Top 3%
1.1%
19
G3 Genes|Genomes|Genetics
351 papers in training set
Top 3%
1.1%
20
Theoretical and Applied Genetics
49 papers in training set
Top 0.6%
1.0%
21
Frontiers in Genetics
230 papers in training set
Top 6%
0.8%
22
American Journal of Botany
47 papers in training set
Top 0.8%
0.8%
23
Quantitative Plant Biology
15 papers in training set
Top 0.3%
0.8%
24
PLANTS, PEOPLE, PLANET
27 papers in training set
Top 0.7%
0.8%
25
BMC Plant Biology
57 papers in training set
Top 1%
0.8%
26
The Plant Genome
57 papers in training set
Top 1%
0.6%
27
PeerJ
308 papers in training set
Top 13%
0.6%
28
The Plant Phenome Journal
14 papers in training set
Top 0.2%
0.6%
29
Tree Physiology
24 papers in training set
Top 0.5%
0.6%