Download CellCODE: a robust latent variable approach to differential

CELLCODE: A ROBUST LATENT
VARIABLE APPROACH TO
DIFFERENTIAL EXPRESSION
ANALYSIS FOR HETEROGENEOUS
CELL POPULATIONS
PRESENTATION BY: BRYAN SHAW
MOTIVATION
• Alterations in gene expressions need to be studied
• Problems with pure cell samples
• No method that addresses the heterogeneity of samples without additional proportion
info
INTRODUCTION
• Differential Expression (DE)
• The differences in transcript production in aggregate between normal and tumor cells
• Current methods
• Matrix decomposition
• Iterative
INTRODUCTION
• “A recent R package unifying many of the existent methods lists only two
(DSection and csSAM) that can work as differential expression pipelines, and
both require independent cell proportion measurements as input (Gaujoux
and Seoighe, 2013).”
• Allows the assignment of genes when normal statistical elements fail due to the gene
regulation being altered by disease
• Disease associated expression changes the expected cell type; which is needed for
interpreting results
RESULTS
• “.. Cell-type COmputational Differential Estimation (CellCODE) method is
designed for DE analysis and requires no additional dataset-specific
knowledge.”
• Uses methods for to adjust for mixture variation and improves statistical power
•
Used on already known datasets and proven to be generally applicable
• Uses “… multi-step statistical framework that uses latent variable analysis to analyze [DE]
from mixture samples.
RESULTS
• SPV’s (surrogate proportion variables)
• cross-referencing putative marker genes with correlated data correlation structures
• Needed to estimate the relative difference in cell proportions
• Approach is data dependent due cell type composition varying from dataset
to dataset
RESULTS
Package contains a heatmap
for gene correlation
RESULTS
Compared to the Coulter counter analysis
RESULTS
RESULTS
• CellCODE improves differential expression discovery
• “Although our analysis suggests that interaction models are not well suited for improving
the power to detect differential expression, they are useful for attributing the DE genes to
their cell type of origin.”
RESULTS
• “The CellCODE SPVs explain a large fraction of the global gene expression
changes observed with standard differential analysis by summarizing them as
proportion changes.”
• Vaccine section (skipped)
DISCUSSION
• This method does use or require independent proportion measurements
• Able to distinguish between t-cells and b-cells with the coulter method could
not
METHODS
• P is a matrix of pure cell expression and C is a matrix of mixture proportions
•
N genes M samples K cell types
METHODS
METHODS