This is the landing page for COMP683 Course Project.
# unless you are starting an R session
# from within this directory, source
# the `.Rprofile` from the project root.
source(".Rprofile")
# If running for the first time use the code below to setup SLICER by installing dependencies
# ## SLICER and some other dependencies are no longer on CRAN, thus they must be installed manually
box::use(mods/setup_SLICER[...])
# If you have run the above, future work may be called by doing the above again or the below:
library(SLICER) # traditional
box::use(SLICER[...]) # using `box::use()`
SLICER is a method to select features (genes) to build a trajectory of cells. In Single Cell RNAseq (scRNAseq), this method may be helpful in the context of cell differentiation analyses. The goal of our project is to investigate Differential Expression (DE) approaches to the features selected by SLICER.
While SLICER automatically selects genes that are important for defining a trajectory among the data, it does not associate which features are most important to defined cell types.
Directly related works are SLICER
(Welch, Hartemink, and Prins
2016)
DESeq2
(Love, Huber, and Anders 2014)
The overall goal of our work is to identify if performing DE based on branches assignments by SLICER will lead to biologically significant results.
We will be using data sets from Single-cell datasets for temporal gene expression integration, specifically utilizing a few Hematopoiesis differentiation dataset (as there are 2).
(Nestorowa et al. 2016)
(Olsson et al. 2016); (Olsson et al. 2019)
As a validation, we will perform Differential Expression analysis on input data prior to SLICER and then compare the results of the same DE pipeline, except only on features selected by SLICER. In practicality, this requires a dataset with some experimental labels which will be used for differential expression comparisons.
Validation Results:
Experiments:
DE on SLICER Features, comparing against original experimental labels
DE on SLICER Features, comparing against SLICER branch labels
DE on ALL Features, comparing against SLICER branch labels
In experiments where SLICER Features are used, we intend to do a set comparison between DE genes in validation versus the experiment.
In experiments where SLICER branch labels are used, we NMI to assess if branches correspond to experimental labels (they may not!)
Immediate challenges will be the disparity between softwares. Data is
stored in a h5ad
format that can be read into memory via
scanpy.read_h5ad(...)
. However SLICER
is implemented in R and will
need to be locally installed as it was removed from
CRAN in
2022
.
Furthermore, branch assignments seem to be based on the Dimensionality
Reduction of LLE
, but the actual trajectories through the graph may
bounce between branch assignments (see Preliminary Results).
Additionally, there is no guarantee to the size of branch assignments
given by SLICER. Assuming one branch is sufficiently small, this may
lead to under powered DE results. DE results may not be comparable to
SLICER results due to the nature of SLICER feature selection (selecting
genes with low neighborhood variance versus global variance).
Since SLICER
is implemented in R, we will be implementing our DE in R
as well. We will ideally provide an R function that takes a cell by gene
matrix and returns SLICER results along with supplemental data relating
to DE analysis. Our code will be posted on
GitHub.
Our preliminary results at the moment just involve running SLICER
’s
workflow on their own toy dataset. Please enjoy the following gif
revealing cells along SLICER
’s defined trajectory.
Love, Michael I., Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2” 15: 550. https://doi.org/10.1186/s13059-014-0550-8.
Nestorowa, Sonia, Fiona K. Hamey, Blanca Pijuan Sala, Evangelia Diamanti, Mairi Shepherd, Elisa Laurenti, Nicola K. Wilson, David G. Kent, and Berthold Göttgens. 2016. “A Single-Cell Resolution Map of Mouse Hematopoietic Stem and Progenitor Cell Differentiation.” Blood 128 (8): e20–31. https://doi.org/10.1182/blood-2016-05-716480.
Olsson, Andre, Meenakshi Venkatasubramanian, Viren K. Chaudhri, Bruce J. Aronow, Nathan Salomonis, Harinder Singh, and H. Leighton Grimes. 2016. “Single-Cell Analysis of Mixed-Lineage States Leading to a Binary Cell Fate Choice.” Nature 537 (7622): 698–702. https://doi.org/10.1038/nature19348.
Olsson, Andre, Meenakshi Venkatasubramanian, Virendra K. Chaudhri, Bruce J. Aronow, Nathan Salomonis, Harinder Singh, and H. Leighton Grimes.
Welch, Joshua D., Alexander J. Hartemink, and Jan F. Prins. 2016. “SLICER: Inferring Branched, Nonlinear Cellular Trajectories from Single Cell RNA-Seq Data.” Genome Biology 17 (1). https://doi.org/10.1186/s13059-016-0975-3.
As a validation - Perform DE on data set against a known assigned labels (data set must be an RNAseq dataset as we do not know how to do DE with Cytoph – Natalie??)
select_genes()
select_k()
)Alternatives: compare junction points to each other SLICER Branch
Potentional Problems: