7. Pseudotime

During development, in response to stimuli, and throughout life, cells transition from one functional “state” to another. Cells in different states express different sets of genes, producing a dynamic repetoire of proteins and metabolites that carry out their work. As cells move between states, they undergo a process of transcriptional re-configuration, with some genes being silenced and others newly activated. These transient states are often hard to characterize because purifying cells in between more stable endpoint states can be difficult or impossible. Single-cell RNA-Seq can enable you to see these states without the need for purification. However, to do so, we must determine where each cell is in the range of possible states.

We can learned a principal graph that fits the scRNAseq data, each cell is projected onto the graph. Then, from given starting points in the graph, we can measure the distance from these start points to each cell, traveling along the graph as it does so. A cell’s pseudo-time is simply the distance from each cell to the closest starting point on the graph.

The Monocle 3 workflow

Read the monocle 3 vignette to learn the basis of trajectory inference for scRNASeq data and pseudo-time.

TRAjectory Differential Expression analysis for SEQuencing data

Instead of following the differential analysis step from the trajectory analysis tools we are going to follow the tradeSeq vignette.

Fit negative binomial model

How do you choose the evaluateK to what correspond this number ?

What is the sce object ?

Discovering progenitor marker genes

Try to plot the 1-10 most significant gene.

Between-lineage comparisons

What is the difference between the Within-lineage and Between-lineage tests ?

Differential expression in large datasets

Explain the problem solved by the l2fc=log2(2) parameter

Better than pseudo-time: velocity

A 2018 study from La Manno et al., brought forth another interesting aspect of RNASeq data. Particularly interesting in the case of scRNASeq data. RNA abundance is a powerful indicator of the state of individual cells. scRNA can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena such as embryogenesis or tissue regeneration. In their paper, they show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours.

scVelo is a python package for the analysis of scRNASeq data which enables the recovery of directed dynamic information by leveraging splicing kinetics La Manno et al..

Read the scVelo description to better understand the model behind it.

This vignette show you how to use scVelo from R using conda and reticulate

We will comeback on DEA after the next section about pseudo-time in scRNASeq analysis.

Data integration can also be used to integrate scRNAseq data with spatial scRNASeq data