STATISTICAL METHODS IN BIOLOGY
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1 STATISTICAL METHODS IN BIOLOGY JOANNA SZYDA MAGDALENA FRĄSZCZAK
2 INTRODUCTION 1. Statistical methods in biology??? 2. The Biostatistic Group current projects 3. Course contents 4. Contact 5. Literature Copyright 2017 Joanna Szyda
3 STATISTICAL METHODS IN BIOLOGY??? science is not data. Data are the raw material of science. It is what you do with the data that is science the interpretation you make, the story you tell. ASHG 2011 Writing Workshop; Albertine 2011 /
4 STATISTICAL METHODS IN BIOLOGY - SNP [Header] BSGT Version Processing Date 11/24/ :14 AM Content BovineSNP50_A.bpm Num SNPs Total SNPs Num Samples 32 Total Samples 2636 [Data] SNP Name Sample ID SNP GC Score Index Allele1 - AB Allele2 - AB Chr Position GT Score ARS-BFGL-BAC _K B B ARS-BFGL-BAC _K B B ARS-BFGL-BAC _K B B ARS-BFGL-BAC _K A B ARS-BFGL-BAC _K B B ARS-BFGL-BAC _K A B ARS-BFGL-BAC _K A B ARS-BFGL-BAC _K B B ARS-BFGL-BAC _K A B ARS-BFGL-BAC _K A B ARS-BFGL-BAC _K B B ARS-BFGL-BAC _K B B ARS-BFGL-BAC _K A B ARS-BFGL-BAC _K A B ARS-BFGL-BAC _K A B ARS-BFGL-BAC _K A A N = Copyright 2017 Joanna Szyda
5 STATISTICAL METHODS IN BIOLOGY - SNP ##FORMAT=<ID=SP,Number=1,Type=Integer,Description="Phred-scaled strand bias P-value"> ##FORMAT=<ID=PL,Number=G,Type=Integer,Description="List of Phred-scaled genotype likelihoods"> ##INFO=<ID=PR,Number=1,Type=Integer,Description="# permutations yielding a smaller PCHI2."> #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT BSWCHEM Chr1 182 C T 30.8 DP=2;VDB= e-02;AF1=1;AC1=2;DP4=0,0,2,0;MQ=34;FQ=-33 GT:PL:GQ 1/1:62,6,0:10 Chr1 300 A G 87 DP=6;VDB= e-02;RPB= e+00;AF1=0.5;AC1=1;DP4 GT:PL:GQ 0/1:117,0,52:5 Chr1 324 A G 34 DP=9;VDB= e-02;RPB= e+00;AF1=0.5;AC1=1;DP4= GT:PL:GQ 0/1:64,0,160:6 Chr1 340 G A 90 DP=14;VDB= e-02;RPB= e-01;AF1=0.5;AC1=1;DP4= GT:PL:GQ 0/1:120,0,209:9 Chr1 353 T A 136 DP=14;VDB= e-01;RPB= e+00;AF1=0.5;AC1=1;DP GT:PL:GQ 0/1:166,0,49:5 Chr1 355 T A 141 DP=14;VDB= e-02;RPB= e+00;AF1=0.5;AC1=1;DP4= GT:PL:GQ 0/1:171,0,50:53 Chr1 380 G T 103 DP=18;VDB= e-01;RPB= e-01;AF1=0.5;AC1=1;DP GT:PL:GQ 0/1:133,0,241:9 Chr1 420 T A 211 DP=19;VDB= e-01;RPB= e-01;AF1=0.5;AC1=1;DP GT:PL:GQ 0/1:241,0,81: polymorphic variants for 1 individual
6 STATISTICAL METHODS IN BIOLOGY - CNV duplication chr1: e e-49 1 deletion chr1: e e+06 1 duplication chr1: e e+09 1 duplication chr1: e e+09 1 duplication chr1: e e-13 1 deletion chr1: e e deletion chr1: e deletion chr1: e deletion chr1: e e deletion chr1: e deletion chr1: e deletion chr1: e e+06 1 deletion chr1: e deletion chr1: e e deletions duplications for 1 individual Copyright 2017 Joanna Szyda
7 STATISTICAL METHODS IN BIOLOGY GENE EXPRESSION Row _ _ _ _ _ _ _ genes 28 individuals 14 comparisons Copyright 2017 Joanna Szyda
8 THE BIOSTATISTIC GROUP PROJECT Copy number variations analysis among diverse cattle breeds Magda Mielczarek Joanna Szyda Magdalena Frąszczak Giulietta Minozzi Ezequiel L. Nicolazzi John Williams Katarzyna Wojdak-Maksymiec
9 THE BIOSTATISTIC GROUP PROJECT CNV in whole genome sequence of 155 bulls Various breeds: Brown Swiss 48 Guernsey 20 Fleckvieh 31 Simmental 16 Norwegian Red 26 Parda de la Montaña 4 Pezzata Rossa Italiana 3 Bruna Italiana 1 Avileña 2 Albera 1 Rubia Gallega 1 Toro de Lidia 1 Pirenaica 1
10 THE BIOSTATISTIC GROUP PROJECT Bioinformatics pipeline
11 THE BIOSTATISTIC GROUP PROJECT # CNV
12 THE BIOSTATISTIC GROUP PROJECT CNV length
13 THE BIOSTATISTIC GROUP PROJECT Genomic distribution of CNV duplications
14 THE BIOSTATISTIC GROUP PROJECT Genomic distribution of CNV deletions
15 THE BIOSTATISTIC GROUP PROJECT # of breed-specific CNVs
16 LECTURE CONTENTS 1. Ability to use biological data of various structures 2. Principles of statistical data analysis 3. Interpretation of results 4. Presence 5. Questions
17 LECTURE CONTENTS Principles of statistical data analysis 1. Introductory lecture 2. Populations and samples 3. Hypotheses testing and parameter estimation 4. Experimental design for biological data 5. Most widely used statistical tests I 6. Most widely used statistical tests II
18 LECTURE CONTENTS Elements of statistical modelling of data 7. Linear regression 8. Nonlinear regression 9. Regression model fit 10. Correlation 11. Elements of statistical data modelling 12. Model comparison 13. Variance analysis 14. Covariance analysis 15. Summary of the material, analysis of examples, discussion
19 LAB CONTENTS 1. Presence 2. Final grade average of particular grades 3. Grading: Written exams - lectures + labs Presentations 4. Computer labs
20 LAB CONTENTS Principles of statistical data analysis 1. Introductory lab 2. Populations and samples 3. Parameter estimation 4. Hypotheses testing I 5. Hypotheses testing II 6. Exam I
21 LAB CONTENTS Elements of statistical modelling of data 7. Correlation 8. Linear regression 9. Nonlinear regression 10. Interpreting results from various models 11. Exam II 12. Model comparison 13. Variance analysis 14. Presentations 15. Presentations
22 CONTACT Statistical mmethods in biology Copyright 2017 Joanna Szyda
23 CONTACT address: Institute of Genetics Kożuchowska 7 consultation: time scheduled individually
24 LITERATURE 1. Lectures 2. Statistical books e.g. Collett, D. (1991) Modelling Binary Data, Chapmann and Hall Draper, N.R., Smith, H. (1998) Applied Regression Analysis, Wiley Hawkins, D. (2005) Biomeasurement. Understanding, analysing, and communicating data in the biosciences. Oxford University Press Ruxton and Colegrave (2003) Experimental design for the life sciences
25 grading STATISTICAL METHODS
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