Viktor Petukhov
PhD student at the University of Copenhagen
Baysor: segmentation of spatial transcriptomics data
Viktor Petukhov , Peter Kharchenko
1,2
2,3
- University of Copenhagen, BRIC
- Harvard Medical School, DBMI
-
Harvard Stem Cell Institute
https://bit.ly/2WRTWg5
Problem
How to map expression to space?
Spatial gene expression patterns
pciSeq
MERFISH
1. X. Qian, K.D. Harris, T. Hauling, D. Nicoloutsopoulos, A.M. Manchado, N. Skene, J. Hjerling-Leffler, M. Nilsson, bioRxiv 2018, 431957276097
2. Moffitt, J. R. et al. Molecular, spatial and functional single-cell profiling of the hypothalamic preoptic region. Science 362 (2018)
[1]
[2]
- MERFISH
- ISS
- osmFISH
- smFISH
- BaristaSeq
- Up to 10 cells
- Up to 10 transcripts
- Up to 10000 genes
Spatial protocols based on RNA-FISH or in situ sequencint
- DARTFISH
- ex-FISH
- StarMAP
- seq-FISH
- FISSEQ
Protocols
Data
6
7
Segmentation problems
Molecules
DAPI
*Moffitt, J. R. et al. Molecular, spatial and functional single-cell profiling of the hypothalamic preoptic region. Science 362 (2018)
Segmentation problems
*Moffitt, J. R. et al. Molecular, spatial and functional single-cell profiling of the hypothalamic preoptic region. Science 362 (2018)
Segmentation problems
MERFISH
[1]
osm-FISH
[2]
1. Moffitt, J. R. et al. Molecular, spatial and functional single-cell profiling of the hypothalamic preoptic region. Science 362 (2018)
2. Simone Codeluppi, Lars E. Borm, Amit Zeisel, Gioele La Manno, Josina A. van Lunteren, Camilla I. Svensson & Sten Linnarsson. Nature Methods 15, 932–935 (2018)
Methods
Preparation: local gene expression
Gene coloring
X
Y
Local expression coloring
X
Y
k nearest neighbors
| Gene 1 | ... | Gene K |
|---|---|---|
| N1 | ... | N_K |
Local expression vector LE
Embed to 3d CIELAB colorspace
Baysor: segmentation of spatial transcriptomics data
| X | Y | Gene |
|---|---|---|
| ... | ... | ... |
Expected
cell size
Transcript data
DAPI
Poly-A
staining
__
Optional
Algorithm: toy example
Gene 1: 20%
Gene 2: 80%
Gene 1: 80%
Gene 2: 20%
What's the source?
| X | Y | Gene |
|---|---|---|
| ... | ... | ... |
We know
Baysor model
Cell as a distribution
Non-conjugate, but has good parametrization
(mean and std instead of #degrees of freedom for Inverse Gamma)
Doesn't work yet
Baysor model
Molecules as a random field
X
Y
- Points are molecules
- Point colors are transcript types (i.e. genes)
- Lines are the random field edges
- Background colors are some cell segmentation
Triangulation
w_{u,v} = p(cell(m_u) = cell(m_v) | m_u, m_v) \sim \\
\sim \frac{cor(LE_u, LE_v)}{\sqrt{(x_u - x_v)^2 + (y_u - y_v)^2}}
edge weight
local expression
Baysor: Dirichlet Mixture Models
- Initialize cells from some clustering (K-Shift is used)
- Expect probabilities of molecules to belong to the cells (E-step)
- Scholastically assign molecules to the cells (S-step)
- Maximize parameters of the cell distributions (M-step)
- Optionally: update priors
- Sample new cells from Dirichlet prior (key difference from SEM algorithm)
- Go to 2
Baysor: one step example
f_c(m_t) = \#molecules(c) * \\
N(x_t, y_t | \mu_c, \Sigma_c) * Cat(g_t | G_c)
p(m_t \in c) = \frac{\Sigma_{(c \in adj(t) : cell(v)=c)} w_{v,t} f_c}{\Sigma_{(c \in adj(t))} w_{v,t} f_{cell(u)}}
E-step:
Distribution for S-step:
Baysor: Alorithm demonstration
Results
Problem: validation of segmentation
- Number of cells
- Fraction of molecules, assigned to noise
- Number of doublets based on expression markers
- Contamination level based on segmentation-free cell type assignment
- Detailed comparison with manual segmentation of Allen smFISH
*3 and 4 are probably the same
Segmentation results
Local gene composition
Cell type
Segmentation results
| Protocol | Baysor | Staining |
|---|---|---|
| osm-FISH | 10059 | 4572 |
| Allen sm-FISH | 4435 | 2525 |
| MERFISH (subset) |
9279 | 6119 |
| pciSeq | 2547 | 3413 |
Number of segmented cells
%of assigned molecules
| Protocol | Baysor | Staining |
|---|---|---|
| osm-FISH | 87.4 | 44.1 |
| Allen sm-FISH | 79.6 | 61.6 |
| MERFISH (subset) |
75.5 | 47.4 |
| pciSeq | 25.7 | 25.8 |
Reducing expression contamination
[1]
1. Simone Codeluppi, Lars E. Borm, Amit Zeisel, Gioele La Manno, Josina A. van Lunteren, Camilla I. Svensson & Sten Linnarsson. Nature Methods 15, 932–935 (2018)
What is contamination?
Low expression / false positive
Contamination
Solution: segmentation-free type assignment
Local gene composition
Cell type
+
Step 1: extract markers
osmFISH paper annotation
>Inhibitory
expressed: Gad2, Pthlh, Crh
not expressed: Tbr1, Rorb, Mfge8, Cpne5
>Excitatory
expressed: Tbr1, Lamp5, Rorb, Syt6
not expressed: Mfge8, Gad2, Mrc1
>Astrocytes
expressed: Aldoc, Gfap, Serpinf1, Mfge8
not expressed: Hexb, Lamp5, Mrc1, Gad2, Sox10, Rorb, Tbr1, Syt6, Plp1
>Oligodendrocytes
expressed: Sox10, Plp1, Pdgfra, Tmem6, Itpr2, Ctps, Bmp4, Anln
not expressed: Hexb, Mrc1, Aldoc, Gfap, Gad2, Tbr1
>Microglia
expressed: Hexb
not expressed: Gad2, Tbr1, Gfap, Mfge8
>Macrophages
expressed: Mrc1
not expressed: Rorb, Lamp5, Syt6, Cpne5, Gfap, Mfge8, Plp1
>Vasculature
expressed: Flt1, Apln, Vtn, Acta2
not expressed: Lamp5, Rorb, Sox10, Gad2, Syt6, Crh
>Ventricle
expressed: Ttr, Foxj1
not expressed: Gad2, Cpne5
>Hippocampus
expressed: Kcnip
not expressed: Gad2, Tbr1, Lamp5, Rorb, Slc32a1
## Inhibitory
>Inh Crhbp
expressed: Crhbp
subtype of: Inhibitory
>Inh Cnr1
expressed: Cnr1
subtype of: Inhibitory
>Inh Kcnip
expressed: Kcnip
subtype of: Inhibitory
>Inh Pthlh
expressed: Pthlh
subtype of: Inhibitory
>Inh Vip
expressed: Vip
subtype of: Inhibitory
>Inh Crh
expressed: Crh
not expressed: Vip
subtype of: Inhibitory
## Vasculature
>Vasc Flt1
expressed: Flt1
subtype of: Vasculature
>Vasc Vtn
expressed: Vtn
subtype of: Vasculature
>Vasc Apln
expressed: Apln
subtype of: Vasculature
>Vasc Acta2
expressed: Acta2
subtype of: Vasculature
## Excitatory
>Ex Rorb
expressed: Rorb
subtype of: Excitatory
>Ex Syt6
expressed: Syt6
subtype of: Excitatory
>Ex Tbr1
expressed: Tbr1
not expressed: Syt6, Rorb
subtype of: Excitatory
>Ex Lamp5
expressed: Lamp5
not expressed: Syt6, Rorb
subtype of: Excitatory
## Oligodendrocytes
>Oligo Cop
expressed: Bmp4
subtype of: Oligodendrocytes
>Oligo MF
expressed: Ctps
subtype of: Oligodendrocytes
>Oligo NF
expressed: Itpr2
subtype of: Oligodendrocytes
>Oligo Precursors
expressed: Pdgfra
subtype of: Oligodendrocytes
>Oligo Mature
expressed: Plp1, Anln
not expressed: Itpr2, Ctps, Bmp4
subtype of: Oligodendrocytes
## Ventricle
>Ependymal
expressed: Foxj1
subtype of: Ventricle
>C. Plexus
expressed: Ttr
subtype of: Ventricle
## Astrocytes
>Astro Mfge8
expressed: Mfge8
subtype of: Astrocytes
>Astro Gfap
expressed: Gfap
not expressed: Mfge8
subtype of: Astrocytes
Extracted markers
Step 1: extract markers
osmFISH paper annotation
Pagoda embedding, same annotation
Step 1: extract markers
New annotation, level 1
New annotation, level 2
Step 1: extract markers
New annotation, level 1
New annotation, level 2
Step 2: extract local vectors
Problems:
-
1976659 pseudo-cells -
Expression is very sparse (10 reads per cell)
Result:
-
No graph - No embeddings
Step 2: extract local vectors
New annotation, level 1
New annotation, level 2
Step 3: estimate fraction of the most represented type per cell
Validation of the approach
Paper
Baysor
Validation of the approach
Cell type
Max. fraction
Validation: zoom in
Validation: add polyT
Paper
Baysor
Validation: summary
We want to improve this plot
Next steps
Cell type expression prior
Idea:
Aggregate expression over similar cells
Problems:
- NNs depends on distance. No way to find good one a-priori
- Contamination has its own patterns and similar cells are simply contaminated in the same manner
Example cell
Nearest
neighbors
Cell sampling prior
- "Contamination" regions are too dense to be noise, and probability to form a new cell is too small
Cell sampling prior
- "Contamination" regions are too dense to be noise, and probability to form a new cell is too small
Cell sampling prior
- Initialize cells from some clustering
- Expect probabilities of molecules to belong to the cells (E-step)
- Scholastically assign molecules to the cells (S-step)
- Maximize parameters of the cell distributions (M-step)
- Optionally: update priors
- Sample new cells from Dirichlet prior
- Go to 2
Split-merge algorithm
Chinese restaurant processes
Segmentation-free DAPI processing
w_{u,v} = p(cell(m_u) = cell(m_v) | m_u, m_v) \sim \\
\sim \frac{cor(LE_u, LE_v)}{\sqrt{(x_u - x_v)^2 + (y_u - y_v)^2}}
w_{u,v} = p(cell(m_u) = cell(m_v) | m_u, m_v) \sim \\
\sim \frac{cor(LE_u, LE_v)}{\sqrt{(x_u - x_v)^2 + (y_u - y_v)^2}} * F(brightness)
Transcript info
Staining info
Segmentation of "bulk" data
Slide-Seq: 10μm beads
500μm
HDST: 2μm wells
- Rodriques S.G., et. al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
- Vickovic S., et. al. High-definition spatial transcriptomics for in situ tissue profiling
Baysor, PM 06 Nov 2019
By Viktor Petukhov
Baysor, PM 06 Nov 2019
PKLab progress meeting presentation
