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STATISTICAL METHODS FOR MOLECULAR BIOLOGY

BMI/STAT 877
과목 설명

Statistical and computational methods in statistical genomics for human and experimental populations. Review methods for quality control, experimental design, clustering, network analysis, and other downstream analysis of next-generation sequencing studies along with methods for genome wide association studies.

선수과목

Graduate/professional standing

충족 요건

This course does not satisfy any prerequisites.

학점

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개설 시기

미보고

평점
3.47

-7.16% 과거 데이터 대비

수료율
88.24%

-8.27% 과거 데이터 대비

A 비율
82.35%

-4.98% 과거 데이터 대비

학급 규모
17

29.52% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

INTRODUCTION TO BIOSTATISTICS

BMI/STAT 541

Course designed for the biomedical researcher. Topics include: descriptive statistics, hypothesis testing, estimation, confidence intervals, t-tests, chi-squared tests, analysis of variance, linear regression, correlation, nonparametric tests, survival analysis and odds ratio. Biomedical applications used for each topic.

STATISTICS IN HUMAN GENETICS

BMI/STAT 620

Provides a comprehensive survey of statistical methods in human genetics research. Covered topics include linkage analysis, genome-wide association study, rare variant association analysis, meta-analysis, genome and variant annotation, heritability estimation, multi-trait modeling techniques, multi-omic data integration, and genetic risk prediction.

STATISTICAL METHODS FOR EPIDEMIOLOGY

BMI/STAT 642

Methods for analysis of case-control, cross sectional, and cohort studies. Covers epidemiologic study design, measures of association, rates, classical contingency table methods, and logistic and Poisson regression.

STATISTICAL METHODS FOR CLINICAL TRIALS

BMI/STAT 641

Statistical issues in the design of clinical trials, basic survival analysis, data collection and sequential monitoring.

QUANTITATIVE RESEARCH AND RIGOR IN GENETICS

GENETICS 807

Combines review of key statistical concepts needed for genetics research with discussions of contemporary debates and common pitfalls in the application of quantitative methods specific to genetics research. Use statistical software to explore and apply statistics to genetic inference, with an emphasis on computationally intensive methods, including group coding exercises and case studies of genetics problems. Engage with contemporary ideas around study design, transparency, rigor and reproducibility in genetics research.