Introduction

Apart from our interactive web application (http://hrpi.ddnetbio.com/), users who are more adept with the R programming language can download our ready-to-use Seurat objects to further interrogate the rich single-cell data generated in this work. Here, we provide some examples (with code) on how to use these ready-to-use Seurat objects.

Citation: TBC

Getting ready

This ready-to-use Seurat object, containing the scRNA-seq of day 21 reprogramming intermediates, can be downloaded at http://hrpi.ddnetbio.com/. Note that the Seurat object is created using Seurat v3.1.1 and may be incompatible with older or newer versions of Seurat. First, we proceed to load the required libraries and Seurat object.

rm(list=ls())
library(data.table)
library(Seurat)
library(ggplot2)
library(patchwork)
seu = readRDS("readySeu_d21i.rds")

What is in this Seurat object?

This Seurat object, comprising 10518 cells and 12611 genes, is generated from scRNA-seq of day 21 reprogramming intermediates from 3 conditions (D21-fm, D21-nr, D21-tr). The gene expression can be found in the “RNA” assay. Five different dimensional reductions were pre-calculated to uncover different aspects of the reprogramming trajectory.

seu
## An object of class Seurat 
## 12611 features across 10518 samples within 1 assay 
## Active assay: RNA (12611 features)
##  5 dimensional reductions calculated: pca, tsne, umap, diffmap, fdl
names(seu)
## [1] "RNA"     "RNA_nn"  "RNA_snn" "pca"     "tsne"    "umap"    "diffmap"
## [8] "fdl"

Different dimensional reductions

Here, we calculated various dimension dimensional reductions to visualise the reprogramming trajectory. These dimensional reductions include:

  • fdl: Force Directed Layout (FDL)
  • pca: Principal Component Analysis (PCA)
  • tsne: t-Distributed Stochastic Neighbor Embedding (tSNE)
  • umap: Uniform Manifold Approximation and Projection (UMAP)
  • diffmap: diffusion maps
# FDL
ggOut = DimPlot(seu, reduction = "fdl", cols = seu@misc$color$cluster)
ggOut + coord_fixed(ratio = diff(range(ggOut$data$FDL_1)) / 
                      diff(range(ggOut$data$FDL_2))) # square aspect ratio