Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
A Markov Random Fields Approach to the Gating o...
Search
Kevin Brosnan
June 15, 2016
Research
150
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
A Markov Random Fields Approach to the Gating of Flow Cytometry Data
Reasearch Students Conference Dublin 2016. Best talk prize awarded.
Kevin Brosnan
June 15, 2016
More Decks by Kevin Brosnan
See All by Kevin Brosnan
Automated Gating for Flow Cytometry
significantstats
0
220
False Starts in Athletics: Are they truly fair?
significantstats
0
110
False Starts in Athletics: Are they truly fair?
significantstats
0
110
False Start Detection in Elite Athletics
significantstats
0
140
A Markov Random Fields Approach to the Gating of Flow Cytometry Data
significantstats
0
160
Challenges for tertiary level mathematics tutors
significantstats
0
100
Elite Athletics: Is the false start disqualification rule appropriate?
significantstats
0
130
Quantile Regression
significantstats
0
170
Forward Modelling of UK Gas Prices
significantstats
0
63
Other Decks in Research
See All in Research
MM-OVSeg: Multimodal Optical–SAR Fusion for Open-Vocabulary Segmentation in Remote Sensing
satai
3
130
SAKURAONE:An Open Ethernet-based AI HPC System And Its Observed Workload Dynamicsin a Single-Tenant LLM Development Environment
yuukit
1
600
HAKARI-Bench - 実運用視点での情報検索モデル評価ベンチマーク
hotchpotch
1
740
Cross-Media Human-Information Interaction
signer
PRO
0
240
マーケットストリート 社会実験2024 in 秋葉原ジャンク通り 調査報告書
izumiyama_lab
1
140
「AIとWhyを深堀る」をAIと深堀る
iflection
0
620
Source Code Diff Revolution
tsantalis
0
150
HackSick vol.7 LT資料【LLMアーキテクチャ入門・事前学習時の躓き所解説】 スパースなAttention・状態空間モデル
rikkabotan7
0
180
Spatial Active Noise Control Based onSound Field Interpolation Incorporating Physical Constraints
skoyamalab
0
190
最先端NLP勉強会2026 論文紹介:Reasoning with Sampling: Your Base Model is Smarter Than You Think (ICLR 2026 paper)
kogoro
4
610
某助成金プロジェクト採択に向けて企業研究所のアウトリーチ専任者がやったこと
afroscript
0
180
RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent
satai
3
550
Featured
See All Featured
The Impact of AI in SEO - AI Overviews June 2024 Edition
aleyda
6
1.2k
A designer walks into a library…
pauljervisheath
211
25k
Java REST API Framework Comparison - PWX 2021
mraible
34
9.7k
Impact Scores and Hybrid Strategies: The future of link building
tamaranovitovic
0
440
Bootstrapping a Software Product
garrettdimon
PRO
306
120k
Efficient Content Optimization with Google Search Console & Apps Script
katarinadahlin
PRO
1
850
No one is an island. Learnings from fostering a developers community.
thoeni
21
3.8k
GraphQLとの向き合い方2022年版
quramy
50
15k
The AI Search Optimization Roadmap by Aleyda Solis
aleyda
1
6.2k
Hiding What from Whom? A Critical Review of the History of Programming languages for Music
tomoyanonymous
3
1.2k
How to Talk to Developers About Accessibility
jct
2
550
Exploring the relationship between traditional SERPs and Gen AI search
raygrieselhuber
PRO
2
4.3k
Transcript
A M A R KO V R A N D
O M F I E L D S A P P ROA C H T O T H E GAT I N G O F F LO W C Y T O M E T R Y DATA K e v i n B r o s n a n , U n i v e r s i t y O f L i m e r i c k
P R E S E N TAT I O N
OU T L I N E Flow Cytometry Existing methods Markov Random Fields Identifying Clusters Results Further Work
F LO W C Y T O M E T
R Y …
W h at i s f lo w c y
to m et r y ?
W h at i s f lo w c y
to m et r y ? Flow
W h at i s f lo w c y
to m et r y ? Flow Cyto
W h at i s f lo w c y
to m et r y ? Flow Cyto metry
F lo w c y to m et r y
d at a … Mean St. Dev. Min Max FSC.H 287.08 178.19 59 1, 023 SSC.H 251.83 186.65 11 1, 023 FL1.H 349.16 234.35 0 974 FL2.H 126.40 90.84 0 705 FL3.H 258.34 192.26 1 1, 023 FL1.A 73.46 195.15 0 1, 023 FL1.W 17.60 56.39 0 444
F lo w c y to m et r y
d at a …
S p ars i t y … Statistic N Mean
St. Dev. Min Max FSC.H 1, 545 71.40 44.53 14 255 SSC.H 1, 545 62.57 46.65 2 255 FL1.H 1, 545 86.93 58.58 0 243 FL2.H 1, 545 31.27 22.66 0 176 FL3.H 1, 545 64.20 48.03 0 255 FL1.A 1, 545 18.25 48.65 0 255 FL1.W 1, 545 4.33 14.01 0 111 time 1, 545 294.04 177.55 2 598
S p ars i t y …
E X I S T I N G M E
T H O D S …
I n d u s t r y S t
a n d ar d …
I n d u s t r y S t
a n d ar d … 6= Expert 2 Expert 1
M o d e l B a s e d
C lu s t er i ng …
t- d i s t r i b u t
i o n M i x t u re s …
t- d i s t r i b u t
i o n M i x t u re s …
M A R KO V R A N D O
M F I E L D S …
X0,0 X1,0 X2,0 X3,0 X0,1 X1,1 X2,1 X3,1 X0,2 X1,2
X2,2 X3,2 X0,3 X1,3 X2,3 X3,3
X0,0 X1,0 X2,0 X3,0 X0,1 X1,1 X2,1 X3,1 X0,2 X1,2
X2,2 X3,2 X0,3 X1,3 X2,3 X3,3
X0,0 X1,0 X2,0 X3,0 X0,1 X1,1 X2,1 X3,1 X0,2 X1,2
X2,2 X3,2 X0,3 X1,3 X2,3 X3,3
X0,0 X1,0 X2,0 X3,0 X0,1 X1,1 X2,1 X3,1 X0,2 X1,2
X2,2 X3,2 X0,3 X1,3 X2,3 X3,3
Wo r k i ng E xam p le …
Wo r k i ng E xam p le …
I D E N T I F Y I N
G C LU S T E RS …
C o n n e ct e d - C
o m p o n e nt s …
C o n n e ct e d - C
o m p o n e nt s …
R E S U LT S …
6 -b i t co n f i g u
r at i o n …
6 -b i t co n f i g u
r at i o n …
6 -b i t co n f i g u
r at i o n …
F U RT H E R WO R K …
None
None
None
None
T H A N K S F O R L
I S T E N I N G ! A N Y Q U E S T I O N S ?