Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
なぜ人はRNA-Seqのリードカウントを負の二項分布に従うと考えるのか
Search
cakkby
April 26, 2022
Science
0
1.4k
なぜ人はRNA-Seqのリードカウントを負の二項分布に従うと考えるのか
なぜ人はRNA-Seqのリードカウントを負の二項分布に従うと考えるのかについて解説してみました
cakkby
April 26, 2022
Tweet
Share
Other Decks in Science
See All in Science
07_浮世満理子_アイディア高等学院学院長_一般社団法人全国心理業連合会代表理事_紹介資料.pdf
sip3ristex
0
600
データベース08: 実体関連モデルとは?
trycycle
PRO
0
930
データマイニング - ノードの中心性
trycycle
PRO
0
270
Celebrate UTIG: Staff and Student Awards 2025
utig
0
150
Cross-Media Technologies, Information Science and Human-Information Interaction
signer
PRO
3
31k
研究って何だっけ / What is Research?
ks91
PRO
1
120
03_草原和博_広島大学大学院人間社会科学研究科教授_デジタル_シティズンシップシティで_新たな_学び__をつくる.pdf
sip3ristex
0
600
データベース06: SQL (3/3) 副問い合わせ
trycycle
PRO
1
620
02_西村訓弘_プログラムディレクター_人口減少を機にひらく未来社会.pdf
sip3ristex
0
610
CV_3_Keypoints
hachama
0
200
データベース04: SQL (1/3) 単純質問 & 集約演算
trycycle
PRO
0
990
サイゼミ用因果推論
lw
1
7.5k
Featured
See All Featured
[Rails World 2023 - Day 1 Closing Keynote] - The Magic of Rails
eileencodes
36
2.5k
Agile that works and the tools we love
rasmusluckow
330
21k
The MySQL Ecosystem @ GitHub 2015
samlambert
251
13k
Statistics for Hackers
jakevdp
799
220k
Mobile First: as difficult as doing things right
swwweet
224
9.9k
Unsuck your backbone
ammeep
671
58k
Visualization
eitanlees
148
16k
Reflections from 52 weeks, 52 projects
jeffersonlam
352
21k
Practical Orchestrator
shlominoach
190
11k
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
51
5.6k
CoffeeScript is Beautiful & I Never Want to Write Plain JavaScript Again
sstephenson
162
15k
Art, The Web, and Tiny UX
lynnandtonic
303
21k
Transcript
None
• • • •
None
None
None
𝑖 𝑙𝑖 𝑒𝑖 𝑁 𝑖 𝑁𝑖 𝑁𝑖 𝑁 = 𝑙𝑖
𝑒𝑖 σ 𝑗 𝑙𝑗 𝑒𝑗 𝑒𝑖 = 1 𝑒𝑖 𝑞𝑖 = 𝑙𝑖 𝑒𝑖 𝑞𝑖
𝑛 𝑔 = σ𝑗 𝑞𝑗 𝑖 𝑞𝑖 𝑁 𝑝 =
𝑞𝑖 /𝑔 𝑖 Pr 𝑁 = 𝑘 = 𝐵𝑖𝑛𝑜𝑚 𝑘|𝑛, 𝑝 = 𝑛 𝑘 𝑝𝑘 1 − 𝑝 𝑛−𝑘
𝑛 𝑔 𝜆 = 𝑛𝑝 = 𝑛𝑞𝑖 𝑔 𝑛, 𝑔
lim 𝜆=𝑛𝑝: fix 𝑛,𝑔→∞ 𝑛 𝑘 𝑝𝑘 1 − 𝑝 𝑛−𝑘 = 𝜆𝑘𝑒−𝜆 𝑘! = 𝑃𝑜𝑖𝑠𝑠𝑜𝑛 𝑘|𝜆 𝜆 = 𝑛𝑝 𝑖 𝜆 𝜆 = 1 𝜆 = 2 𝜆 = 3
𝜆 𝜆 𝜆1 𝜆2
𝜆 𝑖 𝑗 𝜆𝑖𝑗 𝜑 𝜃 𝜃 𝑃 𝑥|𝜃 𝜃
𝑃 𝑥 = 1 𝑀 𝑗=1 𝑀 𝑃 𝑥|𝜃𝑗 ≃ න𝜑 𝜃 𝑃 𝑥|𝜃 𝑑𝜃
𝐺𝑎𝑚𝑚𝑎 𝜆|𝜇, 𝜙 𝑃𝑜𝑖𝑠𝑠𝑜𝑛 𝑥|𝜆 න 0 ∞ 𝐺𝑎𝑚𝑚𝑎 𝜆|𝜙,
𝜇 𝜙 𝑃𝑜𝑖𝑠𝑠𝑜𝑛 𝑥|𝜆 𝑑𝜆 = Γ 𝑥 + 𝜙 Γ 𝑥 + 1 Γ 𝜙 𝜙 𝜇 + 𝜙 𝜙 𝜇 𝜇 + 𝜙 𝑥 = 𝑁𝑒𝑔𝐵𝑖𝑛𝑜𝑚 𝑥|𝜇, 𝜙 𝜇, 𝜙 𝑉 𝑥 = 𝜇 + 𝜇2 𝜙 > 𝜇 𝜙 = ∞
None
𝜙 𝜎2 = 𝜇 𝜎2 = 𝜇 + 𝜇2 𝜙
𝑒𝑖 𝑞𝑖 𝑥𝑖 ′ = 𝑥𝑖 𝑙𝑖 𝐸 𝑥′ =
𝐸 𝑥 𝑙𝑖 = 𝜇 𝑙𝑖 = 𝜇, 𝑉 𝑥′ = 𝑉 𝑥 𝑙𝑖 2 = 𝜇 𝑙𝑖 2 + 𝜇2 𝑙𝑖 2𝜙 = 𝜇 𝑙𝑖 + 𝜇2 𝜙 ≠ 𝜇 + 𝜇2 𝜙
log2 𝑦 = 𝑋𝑓𝑢𝑙𝑙 𝛽 log2 𝑦 = 𝑋𝑟𝑒𝑑𝑢𝑐𝑒𝑑 𝛽
𝑃 𝑌|𝑋𝑓𝑢𝑙𝑙 𝑃 𝑌|𝑋𝑟𝑒𝑑𝑢𝑐𝑒𝑑
None
𝑃 𝑘 = 𝑛 𝑛 − 1 ⋯ 𝑛 −
𝑘 𝑘! 𝑝𝑘 1 − 𝑝 𝑛−𝑘 𝜆 = 𝑛𝑝 𝑛 → ∞ 𝑝 → 0 𝑃 𝑘 ≈ 𝑛𝑘 𝑘! 𝑝𝑘 1 − 𝑝 𝑛 = 𝜆𝑘 𝑘! 1 − 𝜆 𝑛 𝑛 𝑒 1 − 𝜆 𝑛 𝑛 → 𝑒−𝜆 𝑃 𝑘 ≈ 𝜆𝑘 𝑘! 𝑒−𝜆
න 0 ∞ 𝐺𝑎𝑚𝑚𝑎 𝜆|𝜙, 𝜇 𝜙 𝑃𝑜𝑖𝑠𝑠𝑜𝑛 𝑥|𝜆 𝑑𝜆
= න 0 ∞ 𝜆𝜙−1𝑒− 𝜆𝜇 𝜙 𝜙 𝜇 𝜙 Γ 𝜙 𝜆𝑥 𝑥! 𝑒−𝜆𝑑𝜆 = 𝜇 𝜙 𝜙 Γ 𝑥 + 1 Γ 𝜙 න 0 ∞ 𝜆𝜙+𝑥−1𝑒−𝜆 𝜇+𝜙 𝜙 𝑑𝜆 = 𝜙 𝜇 + 𝜙 −𝜙 𝜇 𝜇 + 𝜙 𝜙 Γ 𝑥 + 1 Γ 𝜙 𝜙 𝜇 + 𝜙 𝜙+𝑥 Γ 𝜙 + 𝑥 = Γ 𝜙 + 𝑥 Γ 𝑥 + 1 Γ 𝜙 𝜙 𝜇 + 𝜙 𝑥 𝜇 𝜇 + 𝜙 𝜙 = Γ 𝑥 + 𝜙 Γ 𝑥 + 1 Γ 𝜙 𝜙 𝜇 + 𝜙 𝜙 𝜇 𝜇 + 𝜙 𝑥
• • • • • •