Advanced biometrical and quantitative genetics akshayAkshay Deshmukh
Additive and Multiplicative Model
Shifted Multiplicative Model
Analysis and Selection of Genotype
Methods and steps to select the best model
Bioplot and mapping genotype
Advanced biometrical and quantitative genetics akshayAkshay Deshmukh
Additive and Multiplicative Model
Shifted Multiplicative Model
Analysis and Selection of Genotype
Methods and steps to select the best model
Bioplot and mapping genotype
Multiple Linear Regression II and ANOVA IJames Neill
Explains advanced use of multiple linear regression, including residuals, interactions and analysis of change, then introduces the principles of ANOVA starting with explanation of t-tests.
this activity is designed for you to explore the continuum of an a.docxhowardh5
this activity is designed for you to explore the continuum of an addictive behavior of your choice.
Addictive behavior appears in stages. The earliest stage is non-use, which finally leads up to out-of-control dependence. The stages in between are important to identify, as it is much easier to correct an early-stage issue as opposed to a late-stage problem.
After reviewing the module readings and tasks, use the module notes as a reference and alcohol or substance abuse addiction as an example to identify the various levels of addiction.
You may choose to develop a time line identifying the stages or develop a written essay (no more than 500 words in Word format) to describe the escalation of addictive behaviors.
You are to include at least two references from academic sources that you have researched on this topic in the Excelsior College Library and use appropriate citations in American Psychological Association (APA) style.
You cannot just do a Google search for the topic! Academic sources are required. You may use Google Scholar or other libraries.
Chapter 13
Qualitative Data Analysis
1
Process of Qualitative Data Analysis
Preparing the Qualitative Data
Transform the data into readable text
Check for and resolve transcription errors
Manage the data
Organize by attribute coding
Two Separate Processes
5
Coding: Involves labeling and breaking down the data to find:
Patterns
Themes
Interpretation: Giving meaning to the identified patterns and themes
Coding
Starts with identifying the unit of analysis
Coding categories may reflect realms of meaning or different activities.
Coding categories can be theoretically-based or inductively created emerging from the data.
Use of Analytical Memos
7
Analytical memos help researchers w/ process of breaking down the data
Personal reflections on the research experience, methodological issues, or patterns in the data
Comes in 3 varieties:
Code notes
Operational notes
Theoretical notes
Data Displays
Taxonomy: system of ordered classification
Data matrix: individuals or other units represent columns and coding categories represent rows
Typologies: representation of findings based on the interrelationship between two or more ideas, concepts, or variables
Flow charts: diagrams that display processes
Taxonomy of Survival Strategies
Data Matrix: Homeless Individuals by Dimensions
Drawing and Evaluating Conclusions
Conclusions may result in:
Rich descriptions
Identification of themes
Inferences about patterns and concepts
Theoretical propositions
Evaluation of the data can occur by:
Comparing notes among observers
Using multiple sources of data
Examining exceptions to the data patterns
Member checking
Variations in Qualitative Data Analysis: Grounded Theory
Objective is to develop theory from data
Emphasizes people’s actions and voices as the main sources of d.
In this webinar Dr. Lani discusses key points in successfully completing your quantitative analysis. You will learn how to conduct common statistical analyses, how to examine assumptions, how to easily generate APA 6th edition tables and figures, how to use Intellectus Statistics(TM) Software, how to identify and interpret the appropriate statistics, and how to present and summarize your findings.
SSP is now Intellectus Statistics Software. Intellectus Statistics™ software primarily serves the academic and research communities as a powerful statistical package that can be purchased via four distinct cloud based subscriptions. Learn more here: http://www.statisticssolutions.com/buy-intellectus/
v When to Choose a Statistical Tests OR When NOT to Choose? v Parametric vs. Non-Parametric Tests (Comparison)
v Parameters to check when Choosing a Statistical Test:
- Distribution of Data
- Type of data/Variable
- Types of Analysis (What’s the hypothesis)
- No of groups or data-sets
- Data Group Design
v Snapshot of all statistical test and “How” to Choose using above parameters v Explanation using Examples:
- Mann Whitney U Test
- Wilcoxon Sign Rank Test
- Spearman’s co-relation
- Chi-Square Test
v Conclusion
Market Research using SPSS _ Edu4Sure Sept 2023.pptEdu4Sure
SPSS Training Related Content. There is practical training on the tool. The PPT is for reference purpose.
For any training need, kindly connect us at partner@edu4sure.com or call us at +91-9555115533.
For more courses at our LMS, you can also refer www.testformula.com
#Edu4Sure #SPSS #Training #Certificate
Qualitative Analysis of a Discrete SIR Epidemic Modelijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Science is a cumulative process. Therefore, it is not surprising that one can often find multiple studies addressing the same basic question. Researches trying to aggregate and synthesize the literature on a particular topic are increasingly conducting meta-analyses. Broadly speaking, a meta-analysis can be defined as a systematic literature review supported by statistical methods where the goal is to aggregate and contrast the findings from several related studies.
Multiple Linear Regression II and ANOVA IJames Neill
Explains advanced use of multiple linear regression, including residuals, interactions and analysis of change, then introduces the principles of ANOVA starting with explanation of t-tests.
this activity is designed for you to explore the continuum of an a.docxhowardh5
this activity is designed for you to explore the continuum of an addictive behavior of your choice.
Addictive behavior appears in stages. The earliest stage is non-use, which finally leads up to out-of-control dependence. The stages in between are important to identify, as it is much easier to correct an early-stage issue as opposed to a late-stage problem.
After reviewing the module readings and tasks, use the module notes as a reference and alcohol or substance abuse addiction as an example to identify the various levels of addiction.
You may choose to develop a time line identifying the stages or develop a written essay (no more than 500 words in Word format) to describe the escalation of addictive behaviors.
You are to include at least two references from academic sources that you have researched on this topic in the Excelsior College Library and use appropriate citations in American Psychological Association (APA) style.
You cannot just do a Google search for the topic! Academic sources are required. You may use Google Scholar or other libraries.
Chapter 13
Qualitative Data Analysis
1
Process of Qualitative Data Analysis
Preparing the Qualitative Data
Transform the data into readable text
Check for and resolve transcription errors
Manage the data
Organize by attribute coding
Two Separate Processes
5
Coding: Involves labeling and breaking down the data to find:
Patterns
Themes
Interpretation: Giving meaning to the identified patterns and themes
Coding
Starts with identifying the unit of analysis
Coding categories may reflect realms of meaning or different activities.
Coding categories can be theoretically-based or inductively created emerging from the data.
Use of Analytical Memos
7
Analytical memos help researchers w/ process of breaking down the data
Personal reflections on the research experience, methodological issues, or patterns in the data
Comes in 3 varieties:
Code notes
Operational notes
Theoretical notes
Data Displays
Taxonomy: system of ordered classification
Data matrix: individuals or other units represent columns and coding categories represent rows
Typologies: representation of findings based on the interrelationship between two or more ideas, concepts, or variables
Flow charts: diagrams that display processes
Taxonomy of Survival Strategies
Data Matrix: Homeless Individuals by Dimensions
Drawing and Evaluating Conclusions
Conclusions may result in:
Rich descriptions
Identification of themes
Inferences about patterns and concepts
Theoretical propositions
Evaluation of the data can occur by:
Comparing notes among observers
Using multiple sources of data
Examining exceptions to the data patterns
Member checking
Variations in Qualitative Data Analysis: Grounded Theory
Objective is to develop theory from data
Emphasizes people’s actions and voices as the main sources of d.
In this webinar Dr. Lani discusses key points in successfully completing your quantitative analysis. You will learn how to conduct common statistical analyses, how to examine assumptions, how to easily generate APA 6th edition tables and figures, how to use Intellectus Statistics(TM) Software, how to identify and interpret the appropriate statistics, and how to present and summarize your findings.
SSP is now Intellectus Statistics Software. Intellectus Statistics™ software primarily serves the academic and research communities as a powerful statistical package that can be purchased via four distinct cloud based subscriptions. Learn more here: http://www.statisticssolutions.com/buy-intellectus/
v When to Choose a Statistical Tests OR When NOT to Choose? v Parametric vs. Non-Parametric Tests (Comparison)
v Parameters to check when Choosing a Statistical Test:
- Distribution of Data
- Type of data/Variable
- Types of Analysis (What’s the hypothesis)
- No of groups or data-sets
- Data Group Design
v Snapshot of all statistical test and “How” to Choose using above parameters v Explanation using Examples:
- Mann Whitney U Test
- Wilcoxon Sign Rank Test
- Spearman’s co-relation
- Chi-Square Test
v Conclusion
Market Research using SPSS _ Edu4Sure Sept 2023.pptEdu4Sure
SPSS Training Related Content. There is practical training on the tool. The PPT is for reference purpose.
For any training need, kindly connect us at partner@edu4sure.com or call us at +91-9555115533.
For more courses at our LMS, you can also refer www.testformula.com
#Edu4Sure #SPSS #Training #Certificate
Qualitative Analysis of a Discrete SIR Epidemic Modelijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Science is a cumulative process. Therefore, it is not surprising that one can often find multiple studies addressing the same basic question. Researches trying to aggregate and synthesize the literature on a particular topic are increasingly conducting meta-analyses. Broadly speaking, a meta-analysis can be defined as a systematic literature review supported by statistical methods where the goal is to aggregate and contrast the findings from several related studies.
1. Developing Metrics to Discern
Apparent Study Power in
Protein Mutation Distributions
Anna Blendermann
Mentor: Arlin Stoltzfus
2. Deep Mutational Scanning
Deep Mutational Scanning: technique that uses high
throughput DNA sequencing to analyze protein mutations
Last month, an article appeared in Genetics with results
on 2000 mutants of the BRCA1 gene, which is linked to
breast cancer (http
://www.genetics.org/content/200/2/413)
3. The Inadequacy of Recent Studies
Understanding the effects of mutations is a major
challenge in genomics, evolution, and medicine
Recent studies show…
An unprecedented amount of data on the
effects of mutations in proteins
Unexplained differences in the power of studies
to discern effects in mutations
For example, Lind’s analysis of ribosomal protein mutations shows
little difference between missense and synonymous mutations. (
http://www.sciencemag.org/content/330/6005/825.abstract)
UNDISCERNABLE
GRAPH?!
Lind Study
4. GCC = alanine
GUC = valine
Different amino acid
Missense Mutations
Missense mutations change amino acids
Largest frequency among the three effect types
Expected to cause a wide variety of effects
5. CAG = glutamine
UAG = stop condon
Truncated protein
CAA = valine
CAT = valine
Different codon, same
amino acid
Nonsense & Synonymous
Mutations
Synonymous mutations…
- Change codons but not amino acids
- Have very small, beneficial effects
Nonsense mutations…
- Truncate proteins
- Have strong, deleterious effects
6. Learning and Implementing R
My project required learning R and writing code for the
development of analytical metrics
7. Using Rstudio for Data Analysis
Rstudio was used to compile distribution graphs of missense, nonsense,
and synonymous mutations, in stacked histogram form
Firnberg Study
Stacked
Histogram
Distributions
8. Visualizing DMS Data with Fitness
Fitness Distribution graphs are based on
Y-axis: frequency of protein mutations
X-axis: fitness level of the resulting cell
Frequency – number of mutations
Fitness – how fast the cell grows
Nonsense
Mutations
Missense
Mutations
Synonymous
Mutations
9. Visualizing DMS Data with Quantiles
Quantile Distribution graphs are based on
Y-axis: frequency of mutations relative
to the total number of protein
mutations
X-axis: fitness level of effect types
relative to the overall fitness of the cell
Frequency – number of mutations
Fitness – how fast the cell grows
Nonsense
Mutations
Synonymous
Mutations
Missense Mutations
10. Standard deviation of synonymous mutations
Difference of missense & nonsense averages
Difference of synonymous & missense averages
Difference of synonymous & nonsense averages
Difference of nonsense average and min fitness
Developing Metrics for
Quality Analysis Five Metrics were developed to assess the
quality of fitness and quantile distributions
11. #1
• Compute metric values
#2
• Get R^2 values from cross validation
#3
• Plot metrics vs. R^2 values
#4
• Graph linear regressions (best fit lines)
#5
• Calculate P-values for each plot
Completing Metric Analysis
with Five Steps
Metric Analysis – determining the
ability of each metric to evaluate
apparent study power
Apparent Study Power – how well
a distribution graph displays data
Our Five Steps
12. Computing Metrics for Nine
Mutation Studies
We had 25 studies, but only 9 studies contained the effect
types needed to calculate the metrics
Study Stan.dev Mis.non Syn.mis Syn.non Non.fitness
Acevedo 0.250016 0.229262 0.234982 0.464244 0.080622
Carrasco 0.235649 0.275932 0.192387 0.46832 0
Dc_phi NA 0.294643 NA NA 0.101563
Firnberg 0.147203 0.159689 0.252223 0.411911 0.317862
Hietpas 0.051814 0.223672 0.419424 0.643095 0.268372
Peris 0.146945 0.306863 0.321471 0.628333 0
Roscoe 0.078261 0.379291 0.203929 0.58322 0.114418
Sanjuan 0.245534 0.263043 0.273641 0.536685 0
Wu_v1 0.199661 0.243483 0.279801 0.523284 0.17869
13. Getting R^2 Values from the
Cross Validation
R^2.values
0.14926564
0.15930005
0.58369074
0.18015482
0.22684849
0.17337046
0.21835122
0.18749149
0.14267328
R^2 values – mean quantile (exchangeability) values from
each study that measure power on 0-1 scale
VS
Study Stan.dev Mis.non Syn.mis Syn.non Non.fitness
Acevedo 0.250016 0.229262 0.234982 0.464244 0.080622
Carrasco 0.235649 0.275932 0.192387 0.46832 0
Dc_phi NA 0.294643 NA NA 0.101563
Firnberg 0.147203 0.159689 0.252223 0.411911 0.317862
Hietpas 0.051814 0.223672 0.419424 0.643095 0.268372
Peris 0.146945 0.306863 0.321471 0.628333 0
Roscoe 0.078261 0.379291 0.203929 0.58322 0.114418
Sanjuan 0.245534 0.263043 0.273641 0.536685 0
Wu_v1 0.199661 0.243483 0.279801 0.523284 0.17869
14. Plot - Standard Deviation of
Synonymous Mutations
X-Axis Values: r^2 values
Y-Axis Values: sd(synonymous)
Linear Regression Slope: negative
18. Plot - Difference of Average
Nonsense and Minimum Fitness
X-Axis Values: r^2 values
Y-Axis Values: avg(non) – min(fitness)
Linear Regression Slope: zero
19. Correlating Metrics with Apparent
Study Power
P-Values: values calculated from linear regression that measure
the significance of correlation, lower values are better!
Metric P-Value
Standard Deviation of
Synonymous Mutations 0.014051
Difference of Missense &
Nonsense Averages 0.53634
Difference of Synonymous
& Nonsense Averages 0.304701
Difference of Synonymous
& Nonsense Averages 0.128621
Difference of Nonsense
Average and Min Fitness 0.975549
Column1
Mis.non
Syn.mis
Syn.non
Non.fitness
00.511.5
0.01
0.54
0.3
0.13
0.981
2 3 4 5
1
2 3 4 5
P-Value of Metrics
P-Values P-Values P-Values
20. 1. We developed one metric ideal for the quality analysis of mutation
distributions: Standard Deviation of Synonymous Mutations
2. There were not enough studies with available data to create linear
regressions that accurately evaluated the usability of each metric
3. We only tested five metrics, so there was already a 15%-20% chance
that at least one P-value < 0.05
Future Work: develop MORE METRICS from the mutational data from
MORE STUDIES, to help researchers accurately assess the quality of their
studies and thus, better discern the effects of mutations in proteins
Our Conclusions Based on
the Metric Analysis
21. Thank You
Dr. Arlin Stoltzfus, Mentor
Dr. Mary Satterfield, MML Chief of Staff
Dr. Brandi Tolliver, NIST SURF Director
The SURF Program