To begin, you'll review the goals of differential expression analysis, manage gene expression data using R and Bioconductor, and run your first differential expression analysis with limma.

Analisis perbedaan ekspresi

Aplikasi analisis ekspresi diferensial

Data ekspresi diferensial

Buat boxplot

Kelas ExpressionSet

Buat objek ExpressionSet

Buat boxplot dengan objek ExpressionSet

Paket limma

Menentukan model linear untuk membandingkan 2 grup

Uji perbedaan ekspresi antara 2 kelompok

Differential Expression Analysis

In this chapter, you'll learn how to construct linear models to test for differential expression for common experimental designs.

Flexible linear models

Design matrix for group-means model

Contrasts matrix for group-means

Test for differential expression for group-means

Studies with more than two groups

Design matrix for 3 groups

Contrasts matrix for 3 groups

Test for differential expression for 3 groups

Factorial experimental design

Design matrix for 2x2 factorial

Contrasts matrix for 2x2 factorial

Test for differential expression for 2x2 factorial

Flexible Models for Common Study Designs

Now that you've learned how to perform differential expression tests, next you'll learn how to normalize and filter the feature data, check for technical batch effects, and assess the results.

Normalizing and filtering

Normalize

Filter genes

Accounting for technical batch effects

Visualize batch effects

Remove batch effects

Visualizing the results

Histogram of p-values

Volcano plot

Enrichment testing

KEGG pathways

Gene ontology categories

Pre- and post-processing

In this final chapter, you'll use your new skills to perform an end-to-end differential expression analysis of a study that uses a factorial design to assess the impact of the cancer drug doxorubicin on the hearts of mice with different genetic backgrounds.

Pre-process the data

Pre-process features

Boxplot of Top2b

Check sources of variation

Model the data

Design matrix

Contrasts matrix

Test for differential expression

Inspect the results

Pathway enrichment

Conclusion

Case Study: Effect of Doxorubicin Treatment

Doxorubicin dataset

Leukemia dataset

Hypoxia dataset

Teknologi genomik fungsional seperti mikroarray, sekuensing, dan spektrometri massa memungkinkan ilmuwan mengumpulkan pengukuran tingkat ekspresi gen secara tidak bias dalam skala genom. Baik Anda menghasilkan data sendiri maupun ingin mengeksplorasi banyak himpunan data yang tersedia untuk umum, Anda terlebih dahulu perlu mempelajari cara menganalisis jenis eksperimen ini. Dalam kursus ini, Anda akan diajarkan cara menggunakan paket R/Bioconductor yang serbaguna, limma, untuk melakukan analisis ekspresi diferensial pada rancangan eksperimen yang paling umum. Selain itu, Anda akan mempelajari cara praproses data, mengidentifikasi dan mengoreksi efek batch, menilai hasil secara visual, serta melakukan pengujian pengayaan. Setelah menyelesaikan kursus ini, Anda akan memiliki strategi analisis umum untuk memperoleh wawasan dari studi genomik fungsional apa pun.

Introduction to Statistics in R

Pelajari cara menggunakan paket R/Bioconductor limma untuk analisis ekspresi diferensial.

Analisis Ekspresi Diferensial dengan limma di R

Pelajari cara menggunakan paket Bioconductor limma untuk analisis ekspresi gen yang berbeda.

Analisis Data Genomik di R

Aplikasi analisis ekspresi diferensial

Analisis Ekspresi Diferensial dengan limma di R

Latihan interaktif praktis