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Data Mining デデデ デデデデデ 2014/07/14 Unit Statistical Genetics Ryo Yamada デデデデデデデ デデ デ

Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

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Page 1: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Data Miningデータ・マイニング

2014/07/14Unit Statistical Genetics

Ryo Yamada統計遺伝学分野

山田 亮

Page 2: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

A blank sheet for you to answer Qs during the class,

that is collected at the end of class.Put your name and lab on the top.

何も書いていない紙は講義中の質問への回答を書くためのものです。

講義終了時、回収。名前と所属を用紙の一番上に書き

なさい。

Page 3: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Glomerulus 腎糸球体Q “Sketch スケッチせよ”

Page 4: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Model モデル Q2 “Sketch スケッチせよ”

Page 5: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

QDifference between the photo and

the diagram?写真と模式図の違いは?

• Write your opinion. 意見を書け

Page 6: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

The diagramkeeps/stresses

something and

throw awaysomething

in the photo.

模式図は写真にある何かを取り出し、何かを捨

てている

Page 7: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮
Page 8: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

QDifference between PRE-filter and

POST-filter?フィルタリング前後での違いは?• Write your opinion. 意見を書け

Page 9: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮
Page 10: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

QThere are three 2-D images.

What are lost?2次元投影図が3枚ある。

何が失われている?

• Write your opinion. 意見を書け

Page 11: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 2. Correlation between age of females and parturition date.

Barclay RMR (2012) Variable Variation: Annual and Seasonal Changes in Offspring Sex Ratio in a Bat. PLoS ONE 7(5): e36344. doi:10.1371/journal.pone.0036344http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036344

Page 12: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 2. Correlation between age of females and parturition date.

Barclay RMR (2012) Variable Variation: Annual and Seasonal Changes in Offspring Sex Ratio in a Bat. PLoS ONE 7(5): e36344. doi:10.1371/journal.pone.0036344http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036344

QWhat is this about?

What are taken out and what are thrown away?

Page 13: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 2. Correlation between age of females and parturition date.

Barclay RMR (2012) Variable Variation: Annual and Seasonal Changes in Offspring Sex Ratio in a Bat. PLoS ONE 7(5): e36344. doi:10.1371/journal.pone.0036344http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036344

Page 14: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 1. Seasonal variation in offspring sex ratio.

Barclay RMR (2012) Variable Variation: Annual and Seasonal Changes in Offspring Sex Ratio in a Bat. PLoS ONE 7(5): e36344. doi:10.1371/journal.pone.0036344http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036344

Page 15: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 1. Seasonal variation in offspring sex ratio.

Barclay RMR (2012) Variable Variation: Annual and Seasonal Changes in Offspring Sex Ratio in a Bat. PLoS ONE 7(5): e36344. doi:10.1371/journal.pone.0036344http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036344

QWhat is this about?

What are taken out and what are thrown away?

Page 16: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 1. Seasonal variation in offspring sex ratio.

Barclay RMR (2012) Variable Variation: Annual and Seasonal Changes in Offspring Sex Ratio in a Bat. PLoS ONE 7(5): e36344. doi:10.1371/journal.pone.0036344http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036344

Page 17: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

5′- and 3′-end distributions (TSB). 5′ and 3′ ends within the limits of recessing <300 nt and protruding <1,000 nt from their corresponding annotated ORFs were plotted as histograms.

Høvik H et al. J. Bacteriol. 2012;194:100-114

Page 18: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

5′- and 3′-end distributions (TSB). 5′ and 3′ ends within the limits of recessing <300 nt and protruding <1,000 nt from their corresponding annotated ORFs were plotted as histograms.

Høvik H et al. J. Bacteriol. 2012;194:100-114

QWhat is this about?

What are taken out and what are thrown away?

Page 19: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

5′- and 3′-end distributions (TSB). 5′ and 3′ ends within the limits of recessing <300 nt and protruding <1,000 nt from their corresponding annotated ORFs were plotted as histograms.

Høvik H et al. J. Bacteriol. 2012;194:100-114

Page 20: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 4. Scatter plot of age versus biomarker summary score for men and women from the Estonian Biobank cohort.

Fischer K, Kettunen J, Würtz P, Haller T, et al. (2014) Biomarker Profiling by Nuclear Magnetic Resonance Spectroscopy for the Prediction of All-Cause Mortality: An Observational Study of 17,345 Persons. PLoS Med 11(2): e1001606. doi:10.1371/journal.pmed.1001606http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001606

Page 21: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 4. Scatter plot of age versus biomarker summary score for men and women from the Estonian Biobank cohort.

Fischer K, Kettunen J, Würtz P, Haller T, et al. (2014) Biomarker Profiling by Nuclear Magnetic Resonance Spectroscopy for the Prediction of All-Cause Mortality: An Observational Study of 17,345 Persons. PLoS Med 11(2): e1001606. doi:10.1371/journal.pmed.1001606http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001606

QWhat is this about?

What are taken out and what are thrown away?

Page 22: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 4. Scatter plot of age versus biomarker summary score for men and women from the Estonian Biobank cohort.

Fischer K, Kettunen J, Würtz P, Haller T, et al. (2014) Biomarker Profiling by Nuclear Magnetic Resonance Spectroscopy for the Prediction of All-Cause Mortality: An Observational Study of 17,345 Persons. PLoS Med 11(2): e1001606. doi:10.1371/journal.pmed.1001606http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001606

Page 23: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 5. Cumulative probability of death in the Estonian Biobank cohort by percentiles of the biomarker summary score.

Fischer K, Kettunen J, Würtz P, Haller T, et al. (2014) Biomarker Profiling by Nuclear Magnetic Resonance Spectroscopy for the Prediction of All-Cause Mortality: An Observational Study of 17,345 Persons. PLoS Med 11(2): e1001606. doi:10.1371/journal.pmed.1001606http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001606

Page 24: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 6. Discrimination curves for 5-y mortality in FINRISK cohort.

Fischer K, Kettunen J, Würtz P, Haller T, et al. (2014) Biomarker Profiling by Nuclear Magnetic Resonance Spectroscopy for the Prediction of All-Cause Mortality: An Observational Study of 17,345 Persons. PLoS Med 11(2): e1001606. doi:10.1371/journal.pmed.1001606http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001606

Page 25: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 6. Discrimination curves for 5-y mortality in FINRISK cohort.

Fischer K, Kettunen J, Würtz P, Haller T, et al. (2014) Biomarker Profiling by Nuclear Magnetic Resonance Spectroscopy for the Prediction of All-Cause Mortality: An Observational Study of 17,345 Persons. PLoS Med 11(2): e1001606. doi:10.1371/journal.pmed.1001606http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001606

QWhat is this about?

What are taken out and what are thrown away?

Page 26: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 6. Discrimination curves for 5-y mortality in FINRISK cohort.

Fischer K, Kettunen J, Würtz P, Haller T, et al. (2014) Biomarker Profiling by Nuclear Magnetic Resonance Spectroscopy for the Prediction of All-Cause Mortality: An Observational Study of 17,345 Persons. PLoS Med 11(2): e1001606. doi:10.1371/journal.pmed.1001606http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001606

Page 27: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 1. Odds ratio of death for transfusion compared to no transfusion by risk category.

Perel P, Clayton T, Altman DG, Croft P, et al. (2014) Red Blood Cell Transfusion and Mortality in Trauma Patients: Risk-Stratified Analysis of an Observational Study. PLoS Med 11(6): e1001664. doi:10.1371/journal.pmed.1001664http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001664

Page 28: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 1. Odds ratio of death for transfusion compared to no transfusion by risk category.

Perel P, Clayton T, Altman DG, Croft P, et al. (2014) Red Blood Cell Transfusion and Mortality in Trauma Patients: Risk-Stratified Analysis of an Observational Study. PLoS Med 11(6): e1001664. doi:10.1371/journal.pmed.1001664http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001664

QWhat is this about?

What are taken out and what are thrown away?

Page 29: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 1. Odds ratio of death for transfusion compared to no transfusion by risk category.

Perel P, Clayton T, Altman DG, Croft P, et al. (2014) Red Blood Cell Transfusion and Mortality in Trauma Patients: Risk-Stratified Analysis of an Observational Study. PLoS Med 11(6): e1001664. doi:10.1371/journal.pmed.1001664http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001664

Page 30: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 3. Time courses of RT-related activation in representative gray matter ROIs.

Yarkoni T, Barch DM, Gray JR, Conturo TE, et al. (2009) BOLD Correlates of Trial-by-Trial Reaction Time Variability in Gray and White Matter: A Multi-Study fMRI Analysis. PLoS ONE 4(1): e4257. doi:10.1371/journal.pone.0004257http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004257

Page 31: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 3. Time courses of RT-related activation in representative gray matter ROIs.

Yarkoni T, Barch DM, Gray JR, Conturo TE, et al. (2009) BOLD Correlates of Trial-by-Trial Reaction Time Variability in Gray and White Matter: A Multi-Study fMRI Analysis. PLoS ONE 4(1): e4257. doi:10.1371/journal.pone.0004257http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004257

Q7What is this about?

What are taken out and what are thrown away?

Page 32: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 3. Time courses of RT-related activation in representative gray matter ROIs.

Yarkoni T, Barch DM, Gray JR, Conturo TE, et al. (2009) BOLD Correlates of Trial-by-Trial Reaction Time Variability in Gray and White Matter: A Multi-Study fMRI Analysis. PLoS ONE 4(1): e4257. doi:10.1371/journal.pone.0004257http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004257

Page 33: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 1. Network Illustrating Structural Parameters.

Christakis NA, Fowler JH (2010) Social Network Sensors for Early Detection of Contagious Outbreaks. PLoS ONE 5(9): e12948. doi:10.1371/journal.pone.0012948http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012948

Page 34: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 1. Network Illustrating Structural Parameters.

Christakis NA, Fowler JH (2010) Social Network Sensors for Early Detection of Contagious Outbreaks. PLoS ONE 5(9): e12948. doi:10.1371/journal.pone.0012948http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012948

Q8What is this about?

What are taken out and what are thrown away?

Page 35: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 1. Network Illustrating Structural Parameters.

Christakis NA, Fowler JH (2010) Social Network Sensors for Early Detection of Contagious Outbreaks. PLoS ONE 5(9): e12948. doi:10.1371/journal.pone.0012948http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012948

Page 36: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 3. Empirical differences in flu contagion between “friend” group and randomly chosen individuals.

Christakis NA, Fowler JH (2010) Social Network Sensors for Early Detection of Contagious Outbreaks. PLoS ONE 5(9): e12948. doi:10.1371/journal.pone.0012948http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012948

Page 37: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 3. Empirical differences in flu contagion between “friend” group and randomly chosen individuals.

Christakis NA, Fowler JH (2010) Social Network Sensors for Early Detection of Contagious Outbreaks. PLoS ONE 5(9): e12948. doi:10.1371/journal.pone.0012948http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012948

Q9What is this about?

What are taken out and what are thrown away?

Page 38: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 3. Empirical differences in flu contagion between “friend” group and randomly chosen individuals.

Christakis NA, Fowler JH (2010) Social Network Sensors for Early Detection of Contagious Outbreaks. PLoS ONE 5(9): e12948. doi:10.1371/journal.pone.0012948http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012948

Page 39: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Circular transcriptome map showing the normalized RNAseq transcription signals derived from the MIN-cultured cells.

Høvik H et al. J. Bacteriol. 2012;194:100-114

Page 40: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Circular transcriptome map showing the normalized RNAseq transcription signals derived from the MIN-cultured cells.

Høvik H et al. J. Bacteriol. 2012;194:100-114

QWhat is this about?

How do you summarize these?

Page 41: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Circular transcriptome map showing the normalized RNAseq transcription signals derived from the MIN-cultured cells.

Høvik H et al. J. Bacteriol. 2012;194:100-114

Page 42: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 4. Progression of flu contagion in the friendship network over time.

Christakis NA, Fowler JH (2010) Social Network Sensors for Early Detection of Contagious Outbreaks. PLoS ONE 5(9): e12948. doi:10.1371/journal.pone.0012948http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012948

Page 43: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 4. Progression of flu contagion in the friendship network over time.

Christakis NA, Fowler JH (2010) Social Network Sensors for Early Detection of Contagious Outbreaks. PLoS ONE 5(9): e12948. doi:10.1371/journal.pone.0012948http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012948

QWhat is this about?

How do you summarize these?

Page 44: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

Figure 4. Progression of flu contagion in the friendship network over time.

Christakis NA, Fowler JH (2010) Social Network Sensors for Early Detection of Contagious Outbreaks. PLoS ONE 5(9): e12948. doi:10.1371/journal.pone.0012948http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012948

Page 45: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮
Page 46: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

QWhat is this about?

What are taken out and what are thrown away?

Page 47: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮
Page 48: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮
Page 49: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮

QWhat is this about?

How do you summarize these?

Page 50: Data Mining データ・マイニング 2014/07/14 Unit Statistical Genetics Ryo Yamada 統計遺伝学分野 山田 亮