Slide 1
Slide 1 text
@lcolladotor.bsky.social
lcolladotor.github.io
lcolladotor.github.io/bioc_team_ds
R Bioconductor-powered Team Data Science
Data Science Team 1, Translational Neuroscience Division
Leonardo Collado Torres, LIBD Investigator + Asst. Prof. Johns Hopkins Biostatistics
January 29th, 2025
Slides available at speakerdeck.com/lcolladotor
Orchestrated By
Louise Huuki-Myers
Slide 2
Slide 2 text
Spatially Resolved
Transcriptomics
with Visium
Presented By
Manisha Barse
Slide 3
Slide 3 text
Visium Technologies
Standard Visium
(Fresh Frozen)
Visium CytAssist
(HD or non HD)
(Fresh Frozen, Fixed Frozen, FFPE)
● Sequencing-based spatial profiling technology developed by 10x Genomics.
● Visualize cells and their location in a tissue sample.
● Measure and map gene activity throughout the tissue.
Slide 4
Slide 4 text
Wet Bench + Preprocessing w/ SpaceRanger
Visium Cytassist
LIBD Repository
and Dissections
Kristen Maynard Stephanie Page Sarah Maguire Ruth Zhang
Ryan Miller
Data Analysis
https://speakerdeck.com/manishabarse/exploring-spaceranger-summary-metrics
https://www.youtube.com/watch?v=cJqsbDh0ZtI
LIBD Data
Science Team
Slide 5
Slide 5 text
Quality Control
Levels of QC in spatial transcriptomics
Artifact Level Spot Level
https://pmc.ncbi.nlm.nih.gov/articles/PMC11185656/
Sample Level
● scran
● SpotSweeper
Slide 6
Slide 6 text
Feature
Selection
Identify Highly variable
genes (HVGs)
Identify Spatially variable
genes (SVGs)
Dimension
Reduction
GLM PCA
UMAP
TSNE
Batch
Correction
Harmony
Combat
ScVI: single-cell
Variational Inference
Slide 7
Slide 7 text
Clustering and Annotating
● Use spatial clustering algorithms like BayesSpace
and Precast.
● Different resolutions of BayesSpace clustering:
1. k = 2: separate gray and white matter
2. k = 9: best reiterated histological layers
https://doi.org/10.1126/science.adh1938
More clusters = More complexity
Slide 8
Slide 8 text
Spatial registration
● Adds anatomical context
● Map SpDs to Maynard et.al manual annotated layers.
● Correlate enrichment t-statistics for top markers for reference.
● Highlight most strongly associated histological layer to add biological context.
https://doi.org/10.1126/science.adh1938
Slide 9
Slide 9 text
SpatialLIBD apps
● http://spatial.libd.org/spatialLIBD/
● https://research.libd.org/spatialDLPFC/#interactive-websites
● https://research.libd.org/Visium_SPG_AD/#interactive-websites
● …
https://bioconductor.org/packages/release/data/experiment/html/spatialLIBD.html
Slide 10
Slide 10 text
Summary:
Visium
● Using both Standard and CytAssist
workflows: analyze diverse tissue
types to spatial transcriptomics
and developmental brain disorder
research.
Slide 11
Slide 11 text
Visium HD data
analysis
Presented By
Nick Eagles
Slide 12
Slide 12 text
Visium HD Overview
- Visium HD is composed of square bins,
not spots, at 3 different resolutions
- 8μm bin size is recommended for
analysis by 10x Genomics
- Compare to 100μm distance between spots!
- ~700k bins, compared to ~5k spots!
- 2μm bin size is subcellular, and can be
combined with segmentation to form
cells
“Bin level”: 8μm bins
“Cell level”: Individual cells from combining
2μm bins
16μm bin border
8μm bin border
2μm bin border cell
Slide 13
Slide 13 text
Visium HD: Goals and Challenges
Goals:
- Explore the current landscape of Visium HD software tools
- What’s useful and feasible to run on our data?
- Develop novel code where there are holes
- Gain interesting insights from LIBD Visium HD datasets (e.g. habenula,
DLPFC, HPC samples)
- What questions can we answer with spatially resolved cells?
Challenges:
- Big data: 1 HD sample is larger than 100 standard samples
- Uncharted territory; limited existing solutions
Slide 14
Slide 14 text
Top SVGs
(bin level)
1. Lower
resolution
with SEraster
2. Compute
SVGs with
nnSVG
1
5
9
Slide 15
Slide 15 text
FICTURE: habenula
* Note: mirrored and rotated data
Slide 16
Slide 16 text
Banksy cell-level clusters
k = 2 k = 4 k = 9
Slide 17
Slide 17 text
Transferring cell-type annotation
Slide 18
Slide 18 text
Software Exploration
Software
tool
Runs on our
data
Run time
(per sample)
Required
memory (per
sample, GB)
Integrates
well with
SpatialExper
iment
Operates on
multiple
samples
simultaneously
Uses
GPU
bin2cell Yes 30 min < 32 Yes No No
FICTURE Yes 3 hours < 64 No No No
SEraster Yes 20 min < 64 Yes No No
MERINGUE Sort of 1 day > 400 Yes No No
Giotto Yes Varied Varied Yes Yes No
Banksy Yes 1 hour < 16 Yes Yes No
HERGAST No Yes
ENACT No No No
CellNEST No No Yes
Slide 19
Slide 19 text
Visium HD Summary
- Promising existing software includes FICTURE, bin2cell, Banksy, and
SERaster
- Novel code requires careful implementation to control memory and runtime
- bin2cell produces spatially resolved individual cells with many
downstream possibilities
Slide 20
Slide 20 text
sc/snRNA-seq data
analysis
Presented By
Melissa Mayén Quiroz
Slide 21
Slide 21 text
Droplet Microfluidics: Single cells
encapsulated with barcoded gel beads.
Barcoding: Unique barcodes assign reads
to individual cells.
Unique Molecular Identifiers (UMIs):
Distinguish real transcripts from PCR
artifacts.
Library Preparation: cDNA synthesis and
amplification.
NGS Sequencing: Captures transcript data
for each cell.
Data Analysis
Single-Cell RNA-seq - 10x genomics
Slide 22
Slide 22 text
Obtaining samples and preprocessing
Ryan Miller
Staff Scientist
3. Cell ranger
Analysis software suite provided by 10x
Genomics for processing and analyzing raw
sequencing data from their single-cell RNA
sequencing (scRNA-seq) platform
● Demultiplexing
● Read Alignment
● Barcode and UMI Processing
● Gene Quantification
1. Sample
Preparation
2. Sequencing
Slide 23
Slide 23 text
Quality Control
● Empty Droplets
● Doublet detection
● Quality Control
➔ Low counts
➔ Low detected genes
➔ High mitochondrial
percentage
Slide 24
Slide 24 text
Dimensionality reduction and batch correction
GLM-PCA
GLM-PCA incorporates a generalized linear model (GLM)
framework to model the distribution of the data
More appropriate for count data.
● Accounts for Data
Distribution
● Captures Variance
Harmony
It is designed for batch correction and works on data already
reduced to a lower-dimensional space, such as PCA
embeddings.
● Prevent Overcorrection
● Iterative Refinement
Slide 25
Slide 25 text
Clustering
Shared Nearest Neighbors (SNN) Graph:
● Build an SNN graph by identifying the k-nearest neighbors for each cell in a reduced dimensional space (Harmony-corrected
PCA)
● Nodes represent cells, and edges connect cells with overlapping nearest neighbors.
Walktrap Community Detection:
● Apply the Walktrap algorithm to the SNN graph.
● This algorithm identifies clusters by simulating random walks on the graph, grouping nodes that are frequently visited together.
Slide 26
Slide 26 text
Downstream
analysis
● Obtain Marker genes
● Annotation of clusters
● Differential expression
analysis (pseudo bulk)
Slide 27
Slide 27 text
Summary:
sc/snRNAseq ● Heterogeneity: Distinguishes
individual cell types and states
● Allow to explore differences
between Hb-VTA projection
neurons and other brain cells
Slide 28
Slide 28 text
Multi-ome data
analysis
(snATAC-seq + snRNA-seq)
Presented By
Cynthia Soto Cardinault
Slide 29
Slide 29 text
Multiome scRNAseq / scATACseq strategy
It analyzes Chromium Single Cell Multiome data,
connecting gene expression and chromatin accessibility
for enhanced genomic understanding
Cell Ranger ARC
Slide 30
Slide 30 text
Data preprocessing
Raw data
Ryan M
Kristen M Sarah M
Kelsey M Leonardo C.
Customized
approach
Cellranger
count M
GEX ATAC
Cellranger
atac count
Lisa K. Johnson
Abby Primack
Cell Ranger ARC
Image modified from 10x Genomics
Slide 31
Slide 31 text
Downstream analysis
(Hao Y, et al, 2021; Stuart T et al, 2021)
Single-cell chromatin analysis workflow with Signac
Slide 32
Slide 32 text
Additional controls: quality on GEX and ATAC
Standard Quality Controls for GEX Specific Quality Controls for ATAC
Normalize and scale
data and perform
dimensionality
reduction
Batch correction
method
Clustering
Slide 33
Slide 33 text
Clustering and
Data exploration
● Clustering evaluation and
optimization
● Identify and uncover cell types and
regulatory elements.
SNN (RNA)
LSI (ATAC)
Slide 34
Slide 34 text
Integration with Weighted Nearest Neighbor (WNN)
WNN = GEX+ATAC
Multimodal clustering on pca/harmonize
reduction for RNA and lsi reduction for
ATAC
∫
Visualize chromatin tracks to identify accessible regions and
boundaries over genes or regulatory elements
Slide 35
Slide 35 text
Summary:
Multiome
● Multiome approach links gene
expression to regulation,
revealing disease mechanisms.
● Habenula project explores
regulatory pathways, and
unique gene expression
patterns in habenula
subdomains.
Slide 36
Slide 36 text
Bulk RNA-seq
deconvolution
Presented By
Louise Huuki-Myers
Slide 37
Slide 37 text
Studying Gene Expression
in the Human Brain
Bulk RNA-seq
● Mixture of cell types
Single nucleus
RNA-seq
● Profile cell type
populations
37
Slide 38
Slide 38 text
What is Deconvolution?
Computational method that...
● Infers the composition of
different cell types in a bulk
RNA-seq data
● Utilizes single cell data to
obtain cell type gene
expression profiles
38
Slide 39
Slide 39 text
Mean Ratio Gene Selection
DeconvoBuddies::get_mean_ratio2()
Slide 40
Slide 40 text
Deconvolution Benchmark
● Paired dataset of bulk, snRNA-seq, and
RNAScope
○ Used RNAScope/IF to build orthogonal measure of cell
type proportions in DLPFC
● Compared proportion estimates from six
deconvolution methods
○ DWLS, Bisque, MuSiC, BayesPrism, hspe, and
CIBERSORTx
● hspe & Bisque are top performing methods
Slide 41
Slide 41 text
● Quality Control
○ Ex. Remove samples with high proportion of neighboring region
● Control for cell type in downstream analysis
○ Differential Expression
○ eQTL cell type interactions
How to use
deconvolution?
Slide 42
Slide 42 text
Suggested Deconvolution Pipeline
1. Select single nucleus reference dataset
a. Same brain region, multiple donors (4+)
b. Determine cell type resolution of interest
2. Select Marker Genes
a. Mean Ratio marker genes DeconvoBuddies::get_mean_ratio()
b. Observe marker gene selection with heatmaps and violin plots to gauge quality
3. Run Deconvolution
a. Subset to marker gene set
b. Run Bisque or hspe
4. Check estimated proportions and apply to downstream analysis
Slide 43
Slide 43 text
DeconvoBuddies
Find Marker Genes
● Implements Mean Ratio marker gene selection
○ get_mean_ratio()
● Implements 1 vs. All marker gene selection
○ findMarkers_1vALL() wrapper function for
scran::findMarkers()
Plotting tools
● Quickly plot gene expression over cell types (or
other category)
○ plot_gene_express()
● Plot top marker genes with annotated statistics
○ Plot_marker_express
● Plot Composition bar plots of deconvolution
outputs
○ plot_comoposition_bar()
Access Data
● Access paired data from
consecutive slices of human
DLPFC, used in deconvolution
benchmark
○ RNA-scope
○ snRNA-seq
○ bulk RNA-seq
Slide 44
Slide 44 text
Summary:
Deconvolution
● Deconvolution predicts cell
type composition in bulk
RNA-seq data
● Our deconvolution benchmark
determined Bisque & hspe as
top methods for human brain
data
○ Now in preprint
https://doi.org/10.1101/2024.02.09.579665
Slide 45
Slide 45 text
Bulk RNA-seq data
analysis
Slide 46
Slide 46 text
eQTL Analysis
● Expression quantitative trait loci
● Test if snp (loci) explains variation in gene expression
● We use tensorQTL
Presented By
Louise Huuki-Myers
Image credit: 10.1038/s41588-021-00913-z
Slide 47
Slide 47 text
- Do eQTLs and PGC3 SCZD GWAS risk SNPs share common
causal variants?
- Used coloc::coloc.abf(), one eQTL gene at a time,
considering all SNPs in a neighborhood of the gene
Colocalization Analysis
Presented By
Nick Eagles
16 colocalized genes 2 sig. colocalized eQTLs
123 colocalized PGC3 risk SNPs
Slide 48
Slide 48 text
qsvaR
Presented By
Nick Eagles
Slide 49
Slide 49 text
qsvaR Background
- Post-mortem studies involve a post-mortem interval where tissue degrades
before being frozen
- Different transcripts degrade at different rates
- Degradation confounds biological signal in differential expression analysis
and can produce false positives
Slide 50
Slide 50 text
qsvaR Overview
- R package to reduce degradation signal in DEG analysis
- Degradational signal is summarized from degradation-associated transcripts
and included as covariates in gene-level DE model
- Expansion of Jaffe et al approach, now using transcripts instead of expressed
regions
Slide 51
Slide 51 text
qsvaR: Results
qsvaR effectively removes
degradation signal in DE
Degradation and case-control
signals correlated for other
models, even when including
RIN!
Slide 52
Slide 52 text
Best Number of
Transcripts?
Assumption: correctly adjusting
for degradation should improve
concordance of DE results
across datasets (replication)
Replication: prop. of sig genes in
discovery that are sig at p = 0.05
in target and match direction
Slide 53
Slide 53 text
Summary:
qsvaR
● qsvaR is an R package
reducing the confounding
effect of degradation on DE
● Requires transcript-level data
instead of expressed regions
● Upcoming manuscript
Slide 54
Slide 54 text
CHESS-Brain
Presented By
Geo Pertea
Slide 55
Slide 55 text
Center for Computational Biology - JHU
● CHESS : building a comprehensive catalog of RNA transcripts expressed in RNAseq data
● based on transcript assembly of nearly 10,000 GTEx RNAseq samples with StringTie
Comprehensive Human Expression Sequences
(CHESS)
exon2 exon3
exon1 exon4
StringTie uses splicing graphs to rebuild
transcript structures from read alignments
Slide 56
Slide 56 text
Collaboration work with Dr. Mihaela
Pertea and Ida Shinder at CCB JHU
● Original CHESS used GTEx PolyA-selection samples
● LIBD and CMC RNAseq prep uses rRNA-depletion (RiboZero)
● Stringtie 3 is aware of co-transcriptional splicing as captured by RiboZero data
Comprehensive Human Expression Sequences
— Brain edition
exon2 exon3
exon1 exon4 splicing graph takes into account
unprocessed introns
https://github.com/gpertea/stringtie
Slide 57
Slide 57 text
CHESS-Brain : polyA vs. RiboZero transcriptomics
A. BrainSeq (Phase 1 and 2): includes SCZD
cases for differential expression analysis
B. Cell Fractionation: nuclear, cytoplasmic,
and total RNA fractions compared
C. Degradation experiment: time course
showing degradation effects on RNA
capture
3 DLPFC datasets sequenced with
both PolyA and RiboZero
Slide 58
Slide 58 text
CHESS-Brain : polyA vs. RiboZero transcriptomics
Transcript assembly performance for the degradation
samples across 4 time points.
time points (minutes)
polyA vs RiboZero DEGs by gene biotype
Slide 59
Slide 59 text
SCZD DGE for PolyA and RiboZero on the
BrainSeq datasets before and after
● Volcano plots (diagonal): differential expression
and significant DEG counts
CHESS-Brain : polyA vs. RiboZero transcriptomics
● Correlation plots (lower triangle): t-statistic
concordance between methods, significant DEGs
highlighted
● CAT plots (upper triangle): cumulative
Concordance-At-the-Top agreement of top 3000 DEGs
qSVA correction
Slide 60
Slide 60 text
Summary:
CHESS-Brain
● Data-driven expansion of the
catalog of RNA transcripts
expressed in brain tissue
● Improved transcript-level
analyses of LIBD RNAseq
RiboZero data
● Upcoming manuscripts
Slide 61
Slide 61 text
Open source
software
development
Presented By
Nick Eagles
Slide 62
Slide 62 text
SPEAQeasy and BiocMAP
SPEAQeasy: Bulk RNA-seq preprocessing
pipeline
BiocMAP: WGBS preprocessing pipeline
Documentation sites:
https://research.libd.org/SPEAQeasy/
https://research.libd.org/BiocMAP/
SPEAQeasy updates:
- Minor bug fixes
- Quantify transcripts for rat
Slide 63
Slide 63 text
visiumStitched
- R package for spatially integrating multiple Visium
samples
- Large brain regions can be accurately analyzed in one
piece
- Successfully applied in NAc, hippocampus
Agreement at overlaps
PRECAST clustering (k = 4)
Manuscript
Slide 64
Slide 64 text
slurmjobs
- R package from working with SLURM (e.g.
JHPCE) from R
- Simplifies repetitive tasks
- Enables creation of parallel workflows for
efficient data processing
- Enables monitoring memory usage
Job submission Monitoring
job_report() example output
Slide 65
Slide 65 text
Packages Maintained by the Team
● SpatialLIBD
● DeconvoBuddies
● qsvaR
● SPEAQeasy
● visiumStitched
● slurmjobs
Slide 66
Slide 66 text
LIBD JHPCE
Presented By
Nick Eagles
Slide 67
Slide 67 text
Modules
- Modules: units of software we maintain for LIBD and
collaborators at JHU
- Users can immediately load and use popular software
- Behavior of software is identical across users
- Installation for each user not necessary
- Result: reproducible, straightforward data analysis at
JHPCE
bin2cell/0.3.0
cellpose/2.0
cellranger/8.0.1
ficture/0.0.3.1
hergast/0.0.1
leafcutter
nda-tools/0.3.0
plink2
qctool/2.2.5
spaceranger/3.1.2
spatula
visium_hd/1.0
Recent modules
module load bin2cell
Slide 68
Slide 68 text
LIBD rstats club +
Journal club videos
Slide 69
Slide 69 text
R Stats Club Overview
- Topics include genomics software, computational methods, R packages, and
tips for working as data scientists at JHPCE
- Promotes sharing of knowledge and building skills outside of explicit project
requirements
Slide 70
Slide 70 text
A Growing Resource for LIBD and Collaborators
- Topics are recorded, freely
accessible, and searchable by
keyword
- Complements our efforts to
share knowledge through
DSGSs
Slide 71
Slide 71 text
lcolladotor.github.io
@lcolladotor.bsky.social
Slides: