6.3 Steven Braun - Improving figure clarity
May Institute: Computation and Statistics for MS
6.3 Steven Braun - Improving figure clarity
1:35:57
6.6 Nils Gehlenborg-Data, Visualization, and Discovery
May Institute: Computation and Statistics for MS
6.6 Nils Gehlenborg-Data, Visualization, and Discovery
1:16:03
6.4 Ting Huang-Improving Figure Clarity
May Institute: Computation and Statistics for MS
6.4 Ting Huang-Improving Figure Clarity
1:23:55
6.1 Jan Vitek-Introduction / Overview of R
May Institute: Computation and Statistics for MS
6.1 Jan Vitek-Introduction / Overview of R
1:33:47
6.2 Steven Brau-Visualization Foundations Composition and Layout
May Institute: Computation and Statistics for MS
6.2 Steven Brau-Visualization Foundations Composition and Layout
1:33:10
6.5 Steven Braun-Data Exploration and Linked Views
May Institute: Computation and Statistics for MS
6.5 Steven Braun-Data Exploration and Linked Views
33:49
7.3 Lev Litichevskiy-Drug Characterization Clustering and Connectivity
May Institute: Computation and Statistics for MS
7.3 Lev Litichevskiy-Drug Characterization Clustering and Connectivity
56:29
5.6 Naomi Altman- Multiple testing
May Institute: Computation and Statistics for MS
5.6 Naomi Altman- Multiple testing
1:56:10
7.2 Jacob D. Jaffe-Case Studies in Quantitative Proteomics
May Institute: Computation and Statistics for MS
7.2 Jacob D. Jaffe-Case Studies in Quantitative Proteomics
1:17:01
7.1 Ruedi Aebersold-Data Generation
May Institute: Computation and Statistics for MS
7.1 Ruedi Aebersold-Data Generation
1:31:08
5.5 Olga Vitek/ Meena Choi - Challenge in high dimensional studies
May Institute: Computation and Statistics for MS
5.5 Olga Vitek/ Meena Choi - Challenge in high dimensional studies
1:25:38
5.3 Olga Vitek - Methods for high-throughput biology
May Institute: Computation and Statistics for MS
5.3 Olga Vitek - Methods for high-throughput biology
1:34:05
5.4 Rafael Irizarry-Bias, Unwanted Variability in High Throughput Technologies
May Institute: Computation and Statistics for MS
5.4 Rafael Irizarry-Bias, Unwanted Variability in High Throughput Technologies
1:40:02
5.1 Olga Vitek/ Meena Choi - MSstats
May Institute: Computation and Statistics for MS
5.1 Olga Vitek/ Meena Choi - MSstats
1:31:43
5.2 Olga Vitek - MSstats Normalization and Run Summarization
May Institute: Computation and Statistics for MS
5.2 Olga Vitek - MSstats Normalization and Run Summarization
1:34:57
4.6 Jan Vitek-Funcion and Functional Programming
May Institute: Computation and Statistics for MS
4.6 Jan Vitek-Funcion and Functional Programming
1:30:44
4.5 Tsung-Heng Tsai - Tidyverse Iteration Models
May Institute: Computation and Statistics for MS
4.5 Tsung-Heng Tsai - Tidyverse Iteration Models
1:26:19
4.2 Jan Vitek-Introduction : Overview of R
May Institute: Computation and Statistics for MS
4.2 Jan Vitek-Introduction : Overview of R
1:29:33
4.8 Kylie Bemis-Performance, Profiling, and Debugging in R
May Institute: Computation and Statistics for MS
4.8 Kylie Bemis-Performance, Profiling, and Debugging in R
1:20:21
4.7 Kylie Bemis-Object-oriented Programming in R
May Institute: Computation and Statistics for MS
4.7 Kylie Bemis-Object-oriented Programming in R
1:19:06
4.9 Kylie Bemis - Scalability in R
May Institute: Computation and Statistics for MS
4.9 Kylie Bemis - Scalability in R
1:14:35
4.3 Sarah Taheri - Notes on how to use R Markdown
May Institute: Computation and Statistics for MS
4.3 Sarah Taheri - Notes on how to use R Markdown
53:24
4.4 Tsung-Heng Tsai - Tidyverse-data wrangling
May Institute: Computation and Statistics for MS
4.4 Tsung-Heng Tsai - Tidyverse-data wrangling
31:22
4.1 Tsung-Heng Tsai - Case study and typical operations with base R
May Institute: Computation and Statistics for MS
4.1 Tsung-Heng Tsai - Case study and typical operations with base R
55:08
1.1 Susan Abbatiello - System Suitability for LC MS
May Institute: Computation and Statistics for MS
1.1 Susan Abbatiello - System Suitability for LC MS
58:15
1.9 Cyril Galitzine - Nonlinear LOB and LOD estimation
May Institute: Computation and Statistics for MS
1.9 Cyril Galitzine - Nonlinear LOB and LOD estimation
19:58
1.8 D.R.Mani-Making Sense of LOB, LOD, LOQ in Targeted Proteomics
May Institute: Computation and Statistics for MS
1.8 D.R.Mani-Making Sense of LOB, LOD, LOQ in Targeted Proteomics
33:41
1.7 Sue Abbatiello-Calibration Curves and Standards
May Institute: Computation and Statistics for MS
1.7 Sue Abbatiello-Calibration Curves and Standards
1:28:59
1.4 Susan Abbatiello - CPTAC
May Institute: Computation and Statistics for MS
1.4 Susan Abbatiello - CPTAC
38:11
1.6 Lindsay Pino - Reproducibility, Stability Testing, Repeatibility
May Institute: Computation and Statistics for MS
1.6 Lindsay Pino - Reproducibility, Stability Testing, Repeatibility
1:23:47
1.5 Lindsay Pino - Absolute Quantification and Method Validation
May Institute: Computation and Statistics for MS
1.5 Lindsay Pino - Absolute Quantification and Method Validation
43:50
1.3 Lindsay Pino - Retention-time
May Institute: Computation and Statistics for MS
1.3 Lindsay Pino - Retention-time
29:16
1.2 Sue Abbatiello-Refining transitions and optimizing collision energy
May Institute: Computation and Statistics for MS
1.2 Sue Abbatiello-Refining transitions and optimizing collision energy
1:24:10
3.7 Laurent Gatto - Linear model and correlation
May Institute: Computation and Statistics for MS
3.7 Laurent Gatto - Linear model and correlation
1:27:16
3.6 Meena Choi - Sample size calculation, analysis of categorical data
May Institute: Computation and Statistics for MS
3.6 Meena Choi - Sample size calculation, analysis of categorical data
1:41:08
3.5 Meena Choi - Basic statistics - randomization, error bars
May Institute: Computation and Statistics for MS
3.5 Meena Choi - Basic statistics - randomization, error bars
1:25:48
3.4 Laurent Gatto - Beginner’s statistics in R (day 2 part2)
May Institute: Computation and Statistics for MS
3.4 Laurent Gatto - Beginner’s statistics in R (day 2 part2)
1:41:58
3.1 Olga Vitek- Statistics for (label-free) proteomics
May Institute: Computation and Statistics for MS
3.1 Olga Vitek- Statistics for (label-free) proteomics
1:13:37
3.3 Laurent Gatto - Beginner’s statistics in R (day2)
May Institute: Computation and Statistics for MS
3.3 Laurent Gatto - Beginner’s statistics in R (day2)
1:27:02
3.2 Laurent Gatto - Beginner’s statistics in R (day1)
May Institute: Computation and Statistics for MS
3.2 Laurent Gatto - Beginner’s statistics in R (day1)
2:21:27
2.1 Oliver Kohlbacher- Qualitative proteomics - Database search, PSMs
May Institute: Computation and Statistics for MS
2.1 Oliver Kohlbacher- Qualitative proteomics - Database search, PSMs
37:29
2.3 Oliver Kohlbacher- Introduction to non-targeted metabolomics
May Institute: Computation and Statistics for MS
2.3 Oliver Kohlbacher- Introduction to non-targeted metabolomics
52:27
2.2 Oliver Kohlbacher - label-free quantitative proteomics
May Institute: Computation and Statistics for MS
2.2 Oliver Kohlbacher - label-free quantitative proteomics
1:08:09