B. Tech Biosciences and Bioengineering

BT 307        Biological Data Analysis        2-0-2-6

Syllabus: Data, descriptive statistics, and visualization: Introduction to different types of data in biology; Descriptive statistics like mean, median, mode, quartiles, standard deviation, standard error; Different types of plots like scatter plot, bar graph, line graph, pie chart, box plot, frequency histogram; Understanding error bars. Probability and probability distributions: basic concepts of probability, conditional probability, Bayes theorem; binomial, multinomial, Poisson, exponential, and Gaussian distribution; Sampling distribution and central limit theorem. Hypothesis testing: Student's t-test, Z-test, Chi-squared test, ANOVA. Correlation, regression and estimation: Pearson correlation; Regression: linear, non-linear, single and multivariate; concept of likelihood and method of maximum likelihood. Tools for data of high throughput experiments: principle component analysis; Clustering of data: K-means algorithm, hierarchical clustering; Visualization tools: heat map, volcano plot. Laboratory component: R and MS Excel based exercises on graphical visualization of data, different tests of hypothesis, estimation of correlation, regression, PCA, clustering.

Texts:

1. S. Ross, A First Course in Probability, 9^th Edition, Pearson Education India, 2014.

2. R. C. Elston and W. D. Johnson, Basic Biostatistics for Geneticists and Epidemiologists: A Practical
Approach, 1^st Edition, Wiley, 2008.

3. G. Hartvigsen, A Primer in Biological Data Analysis and Visualization Using R, 1^st Edition, Columbia
University Press, 2014.

References:

1.M. C. Whitlock, and D. Schluter, The Analysis of Biological Data, 2^nd Edition, W. H. Freeman &
Company, 2014.

2.G. P. Quinn, and M. J. Keough, Experimental Design and Data Analysis for Biologists, 1^st Edition,
Cambridge University Press, 2002.

3. M. D. Ugarte, A. F. Militino, and A. T. Arnholt, Probability and Statistics with R, 2^nd Edition, CRC
Press, 2016.