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MATH-474
11 March - 17 March
Lecture 4
Lecture 4
Click
lec04.pdf
link to view the file.
◄ Video Lecture 4b
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Announcements
CRAN: The Comprehensive R Archive Network
R Studio downloads page
Quick-R home page
R Markdown: The Definitive Guide
R Markdown lessons from R Studio
knitr with R Markdown - Karl Broman's website
BioConductor Case Studies
Video Lecture 1a
Video Lecture 1b
Lecture 1
TP 0
TP 1
Q+A lecture/tp 1
Video Lecture 2a
Video Lecture 2b
Lecture 2
Irizarry et al. RMA paper (optional)
limma method paper (optional)
B-stat paper
Ben Bolstad's PhD dissertation (optional)
TP 2
Q+A lecture/tp 2
Video Lecture 3a
Video Lecture 3b
Lecture 3
TP 3
DAFL cel files
Paper comparing quality measures
Q+A lecture/tp 3
Video Lecture 4a
Video Lecture 4b
Linear Models in R
Stop Ignoring Experimental Design ...
Linear Statistical Models
TP 4
PT cel files
Q+A lecture/tp 4
Video Lecture 5a
Video Lecture 5b
Lecture 5
What's a p-value?
R/S graphics
latex page layout
TP 5
Q+A lecture/tp 5
Video Lecture 6a
Video Lecture 6b
Lecture 6
TP 6
Hierarchichal clustering in R
Heatmaps in R
Heatmaps in R - short tutorial
R graph gallery - heatmaps
Help on adding legend to heatmap
Q+A lecture/tp 6
TP 7 - Practice Exam
(Practice) Exam evaluation criteria
Exam Guidance
Additional tips for reports
Q+A tp 7
Lecture 7 (optional)
Using rpart to make classification and regression trees
Machine learning in R
Another example: ClassifyR package (optional)
Lecture 8 (optional)
Lecture 9 (optional)
Questions (instead of office hours this week)
TP 7 (Practice Exam)
TP 7 (late)
Lecture 10 (optional)
Exam evaluation criteria
Exam Guidance
Loading public microarray data in R/BioConductor
Video Lecture 11a
Video Lecture 11b
Video Lecture 11c
Lecture 11 (optional)
Final exam
Linear Models in R ►
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