comp flow lec 041819 - university of pittsburgh · from practical flow cytometry third edition,...
TRANSCRIPT
Lisa BorghesiAssociate Professor of Immunology
Director, Unified Flow Core
FLOWvergnügenthe power and pleasure of flow cytometry!
Intro to Computational Flow Outline
Cytometry technologiesconventional
spectralCyTOF
Power of algorithmic approachesunbiased, high-dimensional
access algorithms at Pittprinciples of algorithmic operations
Conventional flow cytometry“one detector, one color”
PMT 1
PMT 2
PMT 5
PMT 4
DichroicFilters
BandpassFilters
Laser
Flow cell
PMT 3
forwardscatter
Sample
From Practical Flow Cytometry Third Edition, Howard M. Shapiro p.164.
Spectral Overlap
Our high speed analyzers
3 Cytek Auroras= spectral
3 BD LSRFortessa, 2 BD LSRII= conventional
Spectral flow cytometry: Cytek Aurora
collect ALL of the light àincreased sensitivity
discriminate fluors with overlapping spectra à not
possible with conventional flow cytometry
48 channels
FACSAntibodies labelled
with fluorochromes
CyTOFAntibodies labelled with
elemental isotopes
Conventional cytometry, 10-15 colors; beyond that
gets tough
Spectral technologies = game changer, 48
channels
40-50 parameters in a single tube
Ex. gadolinium (Gd), samarium (Sm), terbium
(Tb), thorium (Th), ytterbium (Yb)
Spectral cytometry rivals CyTOF
Advantages of Full Spectrum Flow Cytometry over CyTOF
• SPEED, flow cytometry is high-throughput at >35,000 cell/s while CyTOFprocesses at one-tenth this rate (<1,000 cell/s).
• LEVERAGE EXISTING EXPERTISE, flow reagents and technological expertise that is already in place thereby maximizing existing strengths.
• HIGH ANALYTICAL EFFICIENCY of flow cytometry (90%) versus only 30% recovery rate for CyTOF.
• SORT COMPATIBILITY, smart design of a panel backbone will enable users to move from spectral analyzer to a cell sorter.
• COST, mass cytometers cost 3X, require custom space, specialized reagents, specialized validation.
Power of Algorithmic Analysis
Power of Algorithmic Analysis
High-dimensional analysis – Human brain is not so hot at >3 dimensions. What does 18 dimensional data (18 color) even look like in high dimensional space?
Highlight patterns in the data – Manual analysis with a plethora of iterative bivariate plots is highly inefficient.
Facilitates population discovery – Meaningful populations can overlooked b/c biases and a priori knowledge dictate analysis.
Automated/more unbiased – Manual analysis is subjective and biased.
Fun!
spleen
blood
ILN
MLN
colon
LLN
jejunum
Creating a Human B cell Atlas
BM
CD19CD27CD38CD138IgMIgDIgGCD10CD95
CD45RBCD69CD21CD200CD218aCD370etc.
ERK/MAPKS6AKTmTORstat3zbtb32bach2
30+ donors
Courtesy: Florian Weisel
A day in the life of a FlowJo analyst
Donor 1, Tissue 1
D256 various samples – subsets CD45RB/ CD69
Naive B cellsCD21+ CD27-
atypical B cellsCD21- CD27-
classical MBCCD21+ CD27+
activated MBCCD21- CD27+
CD27
CD21
D256_SP D256_B D256_Col D256_T
160914_B_ST0_ST_new.jo Layout_cells_D256_SP
20.09.2016 11:34 Uhr Page 1 of 1 (FlowJo v9.7.6)
0 102 103 104 105<BV421-A>
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160914_B_ST0_ST_new.jo Layout_cells_D256_B
20.09.2016 11:35 Uhr Page 1 of 1 (FlowJo v9.7.6)
0 102 103 104 105<BV421-A>: CD27
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7.96
90.4
160902_gut_ST0_STnew.jo Layout_cells_D256_Col
20.09.2016 10:09 Uhr Page 1 of 1 (FlowJo v9.7.6)
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5.36
94.3
160713_LS_screen.jo Layout: diff B cells
20.09.2016 11:44 Uhr Page 1 of 1 (FlowJo v9.7.6)
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160914_B_ST0_ST_new.jo Layout: D256_SP_CD45RB_CD69_diffBcells
20.09.2016 11:27 Uhr Page 1 of 1 (FlowJo v9.7.6)
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2.61 1.84
11.284.3
0 102 103 104 105<BV510-A>
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3.05 3.71
7.3885.8
0 102 103 104 105<BV510-A>
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22.6 32.6
2420.7
0 102 103 104 105<BV510-A>
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48.3 13.3
7.131.4
160914_B_ST0_ST_new.jo Layout: D256_B_CD45RB_CD69_diffBcells
20.09.2016 11:26 Uhr Page 1 of 1 (FlowJo v9.7.6)
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4.08 0.0227
0.22795.7
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105<F
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A>: C
D45R
B
2.98 0
1.9995.3
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: CD4
5RB
82.9 0.286
0.15916.6
0 102 103 104 105<BV510-A>: CD69
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: CD4
5RB
40.1 0.361
0.36158.5
160902_gut_ST0_STnew.jo Layout: CD45RB_CD69_diffBcells_D256_Col
20.09.2016 10:04 Uhr Page 1 of 1 (FlowJo v9.7.6)
0 102 103 104 105<BV510-A>: CD69
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5.45 2.89
9.2282.4
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5RB
14.8 22.7
18.244.3
0 102 103 104 105<BV510-A>: CD69
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5RB
11.2 73.7
11.63.55
0 102 103 104 105<BV510-A>: CD69
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: CD4
5RB
16.4 57.2
15.410.9
160713_LS_screen.jo Layout: D256_T_CD45RB_CD69
20.09.2016 11:40 Uhr Page 1 of 1 (FlowJo v9.7.6)
0 102 103 104 105<BV510-A>
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6.13 3.22
12.678.1
0 102 103 104 105<BV510-A>
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8.57 5.99
9.0876.5
0 102 103 104 105<BV510-A>
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30.1 39
18.112.8
0 102 103 104 105<BV510-A>
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36.1 41.7
11.310.9
CD69
CD45
RB
Courtesy:Nadine Weisel
D256 various samples – IgG/ IgM distribution in B cell populations160914_B_ST0_ST_new.jo Layout_cells_D256_SP
20.09.2016 11:34 Uhr Page 1 of 1 (FlowJo v9.7.6)
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8.34
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160914_B_ST0_ST_new.jo Layout_cells_D256_B
20.09.2016 11:35 Uhr Page 1 of 1 (FlowJo v9.7.6)
0 102 103 104 105<BV421-A>: CD27
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2.93
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0 102 103 104 105<BV421-A>: CD27
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D21
7.96
90.4
160902_gut_ST0_STnew.jo Layout_cells_D256_Col
20.09.2016 10:09 Uhr Page 1 of 1 (FlowJo v9.7.6)
0 102 103 104 105<BV421-A>: CD27
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3.4
96.3
0 102 103 104 105<BV421-A>: CD27
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<Per
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D21
5.36
94.3
160713_LS_screen.jo Layout: diff B cells
20.09.2016 11:44 Uhr Page 1 of 1 (FlowJo v9.7.6)
0 102 103 104 105<BV421-A>
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7.35
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160914_B_ST0_ST_new.jo Layout: D256_SP_IgG_IgM_diffBcells
20.09.2016 11:25 Uhr Page 1 of 1 (FlowJo v9.7.6)
0 102 103 104 105<PE-Cy5-A>
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5.48 1.38
71.821.2
0 102 103 104 105<PE-Cy5-A>
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<BUV
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A>
2.06 2.82
74.220.9
0 102 103 104 105<PE-Cy5-A>
0102
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<BUV
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A>
43.4 0.684
26.829.1
0 102 103 104 105<PE-Cy5-A>
0102
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<BUV
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A>
40.8 3.79
34.321.1
160914_B_ST0_ST_new.jo Layout: D256_B_IgG_IgM_diffBcells
20.09.2016 11:23 Uhr Page 1 of 1 (FlowJo v9.7.6)
0 102 103 104 105<PE-Cy5-A>: IgM
0102
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<BUV
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A>: I
gG
0.128 1.31
95.82.75
0 102 103 104 105<PE-Cy5-A>: IgM
0102
103
104
105
<BUV
737-
A>: I
gG
1.24 7.44
81.69.93
0 102 103 104 105<PE-Cy5-A>: IgM
0102
103
104
105
<BUV
737-
A>: I
gG
8.01 1.4
68.522.1
0 102 103 104 105<PE-Cy5-A>: IgM
0102
103
104
105
<BUV
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A>: I
gG
20.6 14.1
32.132.5
160902_gut_ST0_STnew.jo Layout: IgG_IgM_diffBcells_D256_Col
20.09.2016 10:06 Uhr Page 1 of 1 (FlowJo v9.7.6)
0 102 103 104 105<PE-Cy5-A>: IgM
0102
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<BUV
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A>: I
gG
1.04 0.12
79.219.7
0 102 103 104 105<PE-Cy5-A>: IgM
0102
103
104
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<BUV
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A>: I
gG
9.09 0
31.859.1
0 102 103 104 105<PE-Cy5-A>: IgM
0102
103
104
105
<BUV
737-
A>: I
gG
9.47 0.0999
29.660.8
0 102 103 104 105<PE-Cy5-A>: IgM
0102
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104
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<BUV
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A>: I
gG
15 0
11.773.2
160713_LS_screen.jo Layout: D256_T_IgG_IgM
20.09.2016 11:39 Uhr Page 1 of 1 (FlowJo v9.7.6)
0 102 103 104 105<PE-Cy5-A>
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2.5 2.39
64.530.6
0 102 103 104 105<PE-Cy5-A>
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8.46 1.7
61.128.9
0 102 103 104 105<PE-Cy5-A>
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34.9 1.89
30.432.8
0 102 103 104 105<PE-Cy5-A>
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<BUV
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44.5 0.655
17.237.6
D256_SP D256_B D256_Col D256_T
Naive B cellsCD21+ CD27-
atypical B cellsCD21- CD27-
classical MBCCD21+ CD27+
activated MBCCD21- CD27+
CD27
CD21
IgM
IgG
Courtesy:Nadine Weisel
Problem: how do you visualize millions of cells marked by a rainbow of colors in order to establish the underlying structure or principles of organization?
Solution: take high-D spaces and embed this information into low-D spaces that our brains can understand
à algorithms identify local patterns and global patterns that our brains may miss when performing manual analysis via iterative pairwise plots
D182 D205 D243D256 D269
Blood
D181D182 D205 D243D256
SPL
BM
merge
merge
merge
manual gateoverlaysindividual donors
D193 D260D182 D205 D215 D246D256
D164
2K10K 2K 2K 2K 2K
2K12K 2K 2K 2K 2K
2K14K 2K 2K 2K 2K 2K 2K
2K
Donor distance:
D181
D182
D205
D243
D256
D18
1
D18
2
D20
5
D24
3
D25
6
D164D182D205D243D256D269
D16
4D
182
D20
5D
243
D25
6D
269
D182D193D205D215D246D256D260
D18
2D
193
D20
5D
215
D24
6D
256
D26
0
BMBloodSPL
Naive BASCMBC IgM+MBC IgG+MBC swAg-exper. IgG+Ag-exper. sw
Unbiased analysis of across-donor heterogeneity
-40 -30 -20 -10 0 10 20 30 40bh-SNE1
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Blood BM SPL T LLN Col
D260
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D256
merge
Naive BASCMBC IgM+IgD-MBC IgD+MBC swAg-exper. sw
manual gating
Tissue-specific B cell signatures
t-SNE FlowSOMSPADE
Algorithm Overview
CD19CD27IgDIgMCD38CD21
CD45RBCD69CD200CD218aCD370
star chart
CITRUS
PhenoGraph
Single cell resolution Tree of relationships
Automated population identification
Tree of relationships
Computational algorithms
Kimball 2017 JI
Choosing an Algorithm
Algorithms on site, actually free or basically free
1) FlowJo v.10+: t-SNE, SPADE, FlowMeans ($200/yr site license)
2) Cyt package: viSNE, PhenoGraph, Wishbone, Wanderlust, PCA, K-means, other statistical tools (free for faculty and students and $100/yr for everyone else)
3) SPADE http://pengqiu.gatech.edu/software/SPADE/ (free)
4) ExCYT https://github.com/sidhomj/ExCYT (free)
5) High performance cluster: viSNE, SPADE (free)
see supplemental slides at end of ppt for instructions on #2-5
Commercial Options
Cytobank: viSNE, SPADE, CITRUS, Sunburst; FlowSOM beta ($1,500/yr/user) www.cytobank.org
FCS Express: t-SNE, SPADE, K-means, PCAalso handles raw image files from the ImageStream($234-500/yr/license) www.denovosoftware.com
t-SNE – dimensionality reduction algorithm
Goal: find a low dimensional visualization that best reflects population structure in high dimensional space
à colloquially, get a feel for how objects are arranged in data space
tSNE_X
tSN
E_Y
Laurens van der Maaten explains t-SNE (UCSD seminar) – fun and informative!!
https://www.youtube.com/watch?v=EMD106bB2vY
PCA Swiss roll problem
• Principal Components Analysis (PCA) preserves large pairwise distances• Euclidean distance between two points on the Swiss roll does not
accurately reflect local structure
Why not just use PCA?
t-SNE preserves local distances and global distances
Amir 2013 Nature Biotech, Suppl.
PCA tSNE
High-D data space. Draw Gaussian bell (circle) around data point. Measure density of all other points relative to that Gaussian bell, and establish probability distribution that represents their similarity. Computes local densities to get a distribution of pairs of points. à Pij
Low-D 2D map. Repeat above. à Qij
Mathematically minimize P||Q difference. Zero would be if two points were the same.
t-SNE operation
Barnes-Hut Modification of t-SNE (bh-SNE)
t-SNE
Advantages
• single cell information
• non-linear assumptions (as opposed to PCA)
• preserves local and global structure
Limitations• computationally expensive; obligate downsampling means data are discarded
• plot axes are arbitrary and have no intrinsic meaning
• no population identification; follow up approaches required to assign identity
to clusters and cells
• distance between clusters is not meaningful; no hierarchy
tSNE_X
tSN
E_Y
SPADE: Hierarchical clustering algorithm
Goal: organize cells into a hierarchy using unsupervised approaches
à colloquially, generate a tree of relationships
spanning tree progression of density normalized events
Output minimal spanning tree (MST) highlights the relationships between most closely related cell type clusters
SPADE
SPADE views data as a cloud of points (cells) where the dimensions = # markers
Density-dependent downsamplingto equalize density in different parts of cloud, ensures rare cells not lost
Agglomerative clustering based on marker intensity
Connect clusters in minimal spanning tree that best reflects geometry of the original cloud
Upsampling, map each cell in the original data set to the clusters
SPADE
Advantages
• rare pops preserved through density-dependent downsampling
• enables visualization of continuity of phenotypes
• can combine data sets that share common markers, and then co-map
any markers unique to each data set (see orig. paper)
Limitations
• loss of single cell information
• user chooses cluster number
• MSTs are non-cyclic and paths can be artificially split
SPADE
t-SNE
PhenoGraph
Goal: automated partitioning of high-dimensional single-cell data into subpopulations
à colloquially, map nearest neighbors
PhenoGraph
First order relationship – find the k nearest neighbors for each cell using Euclidean distance
Second order relationships – cells with shared neighbors should be placed near one another
Third, identify communities – Louvain method that measures the density of edges inside communities to edges outside communities
PhenoGraph: Number of neighbors
Neighbors = 5 Neighbors = 30 Neighbors = 200
Manual gate overlays PhenoGraph
Naive BASCMBC (total)Ag-exper.
PhenoGraph – population discovery
PhenoGraph
Advantages
• opportunity for population discovery
• can resolve subpopulations as rare as 1 in 2000 cells
• robust to cluster shape (e.g., need not be spherical)
Limitations• user specifies number of neighbors
• ideal cluster number, or biologically relevant cluster number, is largely
unconstrained
Harinder Singh, PhD
• 2 Cytek Aurora instruments + computational flow toolset
• algorithms installed on HPC
• Center for Systems Immunology (CSI)
• harness newly emerging experimental and computational approaches
• programmatic analysis pipelines
Looking Forward
Spring 2019 Computational Flow Schedule
Introduction to Computational Flow:Friday, April 19, 10am-11am, BST E1095
Friday, May 17, 11am-12pm, BST E1095
Algorithm Installation and Assistance:Friday, April 26, 2pm-3pm, BST E1048
Tuesday, May 28, 2pm-3pm, BST E1048
Acknowledgements
Dewayne FalknerDirector of Operations
Aarika MacIntyreSenior Technologist
Ailing LiuApplications Specialist
Unified Core, BoardMark ShlomchikFadi LakkisMark GladwinLarry MorelandJohn McDyer
Center for Research ComputingKim Wong
Nan ShengCore Technician
Heidi GunzelmanSenior Flow Cytometry Specialist
ResourcesUseful starting places - reviews1. Kimball AK, Oko LM, Bullock BL, Nemenoff RA, van Dyk LF, Clambey ET. A Beginner's Guide to Analyzing and
Visualizing Mass Cytometry Data. J Immunol. 2018 Jan 1;200(1):3-22.2. Saeys Y, Gassen SV, Lambrecht BN. Computational flow cytometry: helping to make sense of high-dimensional
immunology data. Nat Rev Immunol. 2016 Jul;16(7):449-62.3. Mair F, Hartmann FJ, Mrdjen D, Tosevski V, Krieg C, Becher B. The end of gating? An introduction to automated
analysis of high dimensional cytometry data. Eur J Immunol. 2016 Jan;46(1):34-43.4. Chester C & Maecker HT. J Immunol. 2015 Aug 1;195(3):773-9. doi: 10.4049/jimmunol.1500633. Algorithmic Tools
for Mining High-Dimensional Cytometry Data. J Immunol. 2015 Aug 1;195(3):773-9.
Original application of algorithms1. Qiu P, Simonds EF, Bendall SC, Gibbs KD Jr, Bruggner RV, Linderman MD, Sachs K, Nolan GP, Plevritis SK.
Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol. 2011 Oct 2;29(10):886-91. SPADE
2. Amir el-AD, Davis KL, Tadmor MD, Simonds EF, Levine JH, Bendall SC, Shenfeld DK, Krishnaswamy S, Nolan GP, Pe'er D. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Amir el-AD, Davis KL, 3. Tadmor MD, Simonds EF, Levine JH, Bendall SC, Shenfeld DK, Krishnaswamy S, Nolan GP, Pe'er D. Nat Biotechnol. 2013 Jun;31(6):545-52. viSNE
3. Levine JH, Simonds EF, Bendall SC, Davis KL, Amir el-AD, Tadmor MD, Litvin O, Fienberg HG, Jager A, ZunderER, Finck R, Gedman AL, Radtke I, Downing JR, Pe'er D, Nolan GP. Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis. Cell. 2015 Jul 2;162(1):184-97. PhenoGraph
4. Bruggner RV, Bodenmiller B, Dill DL, Tibshirani RJ, Nolan GP. Automated identification of stratifying signatures in cellular subpopulations. Proc Natl Acad Sci U S A. 2014 Jul 1;111(26):E2770-7. CITRUS
5. Setty M, Tadmor MD, Reich-Zeliger S, Angel O, Salame TM, Kathail P, Choi K, Bendall S, Friedman N, Pe'er D. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat Biotechnol. 2016 Jun;34(6):637-45. Wishbone
6. Bendall SC, Davis KL, Amir el-AD, Tadmor MD, Simonds EF, Chen TJ, Shenfeld DK, Nolan GP, Pe'er D. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell. 2014 Apr 24;157(3):714-25. Wanderlust
Install Cyt3 on Your Personal ComputerviSNE, PhenoGraph, (Wanderlust, Wishbone, and more)
1. Install Matlab from my.pitt.edu software downloads• Free for students and teaching faculty• Everyone else, $100/yr license
2. Install Cyt3, obtain installer from Unified Flow CoreMac People – Cyt3 installer for MacWindows People – Master Cyt3 installer, bh_tsne_win64 file
• Then, in the Cyt3 à src à 3rdparty à bh_tsne folder remove bh_tsne_mac64 and replace with bh_tsne_win64
3. Follow CyTutorial ppt instructions provided by installer to perform analysis• CyTutorial instructions lean toward the skeletal• implementation can be tricky• no longer supported by developer Dana Pe’er
viSNE
Amir et al. Nat Biotechnol. 2013 Jun;31(6):545-52
Install SPADE 3.0 on Your Personal Computer
Easy to install and use!
1. For Mac or Windows2. Stand-alone (or via Matlab)3. Great instructions provided at URL4. Supported by developer Peng Qiu
http://pengqiu.gatech.edu/software/SPADE/
Qiu et al. Nat Biotechnol. 2011 Oct 2;29(10):886-91
ExCYT
• interactive gating• gate directly on tSNE plots• tSNE and then re-tSNE on gated subsets• novel high-dimensional flow plots
Manuscript & video:https://www.jove.com/video/57473/excyt-graphical-user-interface-for-streamlining-analysis-high?status=a59479k&fbclid=IwAR2i0i-M51YhAkrEermEwtpinvPLPccc_n88pXmrfZPwhhu9tZLbZTzvQuA
Software:https://github.com/sidhomj/ExCYT
ExCYT
Pitt’s High Performance ClusterviSNE and SPADE
Unified Core website