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Table 1 Questions in plant biology to which single cell profiling could be applied: analytical problems and algorithmic solutions

From: The potential of single-cell profiling in plants

Biological problem or plant-specific question Analytical problems for single-cell data Potential approaches
Distinguish genes that show true biological variation Significant technical noise is present Hypothesis testing based on identification of variation that exceeds empirical estimations of technical noise [11]
What genes vary among physiologically distinct cells of seemingly homogenous tissues? Profiles have no replicates and exhibit zero-biased expression distribution, so traditional statistical methods are inappropriate
Model-driven deconvolution of biological variation using estimations of technical noise [20]
Identify transcriptional signature of rare cell types Linear dimensionality reduction can obscure close relationships and produce misleading clusters Non-linear t-SNE to minimize joint probability distribution distance and draw similar cells together [29]
What is the transcriptional profile of root initials? Clustering methods might miss small sets of cells backSPIN to impose an order and partition data [31, 32]
Find subsets of cells with a unique environmental response Separation of a continuous cell expression space into types is subjective RaceID to identify new cell types by detecting a significant number of biological gene outliers [30]
What is the early response of pathogen-susceptible vs. pathogen-resistant cells of the leaf epidermis?
Assemble dissociated cells into a developmental sequence Missing data-points exist owing to false negatives and misleading false positives De novo trajectory reconstruction to order cells using Monocle [39] or diffusion-like dynamics [40]
What is the ordered profile of specific cell types from initial to differentiated cells? Variation in individual plants can create artificial groupings Seurat to map cells using a priori data and imputation of missing data-points [41]
ICI to map cells to known reference types using many markers [12, 38]
Follow identity transitions during wound repair or in vitro regeneration Detecting transitional and multiple identities must be robust in single-cell data with many false positives and false negatives ICI to classify cells using a priori knowledge of identity markers for detecting mixed or diminished cell identity [12, 38]
Do plant cells follow a course of de- or trans- differentiation during regeneration?
  1. ICI index of cell identity, t-SNE t-distributed stochastic neighbor embedding