Last updated: 2021-07-05

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Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/

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Welcome! This is a research website detailing analyses analyses performed on scRNA-seq data from human embryoid bodies, aggregates of spontaneously differentiating cells generated from induced pluripotent stem cells.

Preprint is now available on BioRxiv: https://www.biorxiv.org/content/10.1101/2021.06.16.448714v1

The overarching goals of this work are to:

  1. characterize tthe diversity of cell types resulting from this particular differentiation protocol
  2. Understand the contribution of biological and technical variation present in all levels of this this type of data i)What is the relative contribution of individual and replicate to variation in cell composition?
  1. What is the relative contribution of individual and replicate to variation in gene expression?
  2. What is the relative contribution of individual and replicate to variation in gene expression dynamics across distinct developmental trajectories?
  1. Evaluate the future prospects of this model system (EBs + single cell) for identification of dynamic QTLs

Study Design:

Embryoid bodies were formed from 3 human iPSCs lines in 3 replicates. Three weeks after formation, EBs were dissociated and scRNA-seq data was collected using the 10x platform. After quality control and filtering, this dataset consists of 42,488 cells.

Alignment and Preprocessing

See full alignment and preprocessing pipeline here: https://github.com/kennethabarr/HumanChimp

Run EmptyDrops, Add Metadata to Seurat object from each 10x lane

See code directory: https://github.com/KLRhodes/Embryoid_Body_Pilot_Workflowr/blob/master/code/EB.getHumanMetadata.Rmd

Merging and integration, seurat clustering

Reference Integration

Here, we integrate our EB data with 1) scRNA-seq data from human fetal tissues (Cao et al. 2020) and with Embryoid body cells and human embryonic stem cells from Human Cell Landscape (Han et al. 2020)

We then checked that the integration and annotation procedure was robust by subsetting our EB data to only cells for a particular type and re-integrated to check that most cells were annotated the same as in the full integration.

Topic Modelling with FastTopics

Topic modelling Scripts can be found in the code directory.

Variance Partition (to determine the relative contribution of Cluster, Batch, and Individual to variation in gene expression)

Down Sampling and Power Analysis

Trajectory Inference with Scanpy/PAGA and Identification/Exploration of Dynamic Gene Module with Split-GPM

See Josh Popp's GitHub: https://github.com/jmp448/ebpilot

Scripts for generating clean paper figures

Additional Processing and Analyses that were tested but ultimately not included in the paper:

Other integrations w/ and w/out harmony:

Cell type identification using scHCL