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ph525x video stats
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| d <- read.csv("ph525x_vids.csv") | |
| d <- d[-1,] # first 1 minute video | |
| d$week <- factor(d$week) | |
| sum(d$min)/60 # "hours of video") | |
| barplot(tapply(d$min, d$week, sum)/60, main="hours of video / week") | |
| barplot(table(d$week), main="video units / week") | |
| stripchart(min ~ week, data=d, vertical=TRUE, | |
| pch=1, method="jitter", | |
| xlab="week", ylab="min", main="minutes per video unit") | |
| abline(h=10, lwd=2, col="red") | |
| library(ggplot2) | |
| ggplot(data=d, aes(x=week, fill=kind)) + geom_bar(aes(weight=min)) + ylab("min") | |
| with(d, plot(1:nrow(d), min, type="h",xlab="units", | |
| col=kind, lwd=2, main="minutes / unit / kind")) | |
| legend("topleft",lty=1,lwd=2,col=rev(1:3),rev(levels(d$kind)), cex=.65) | |
| titles <- as.character(sapply(split(d$title1, d$week), `[`, 1)) | |
| xs <- sapply(1:8, function(i) which(d$week == i)[1]) | |
| par(mar=c(0,4,2,1),mfrow=c(2,1)) | |
| with(d, plot(1:nrow(d), min, type="h",xlab="", | |
| xlim=c(0,175), ylim=c(0,max(d$min)+1), col=kind, lwd=2, | |
| xaxt="n", bty="n",main="PH525x: Data Analysis for Genomics")) | |
| legend("right",lty=1,lwd=2,col=rev(1:3),rev(levels(d$kind)), cex=.65) | |
| par(mar=c(0,4,0,1)) | |
| plot(xs, seq(from=0, to=.9, length=8), pch=16, cex=.5, | |
| xlab="",ylab="",xaxt="n",yaxt="n", | |
| ylim=c(-.1,1), xlim=c(0,175), bty="n") | |
| segments(xs, seq(from=0, to=.9, length=8),xs, rep(1,8), lty=3, lwd=2) | |
| text(xs, seq(from=0, to=.9, length=8), titles, cex=.75, pos=4) | |
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| week | title1 | subsection | title2 | unit | title3 | min | kind | |
|---|---|---|---|---|---|---|---|---|
| 0 | pre-course | 1 | Introduction to Data Analysis for Genomics | 1 | Introduction to Data Analysis for Genomics | 1 | R lecture | |
| 1 | Introduction | 1 | What we measure and why? | 1 | Introduction | 2 | R lecture | |
| 1 | Introduction | 1 | What we measure and why? | 2 | The molecular basis for phenotypic variation | 3.5 | R lecture | |
| 1 | Introduction | 1 | What we measure and why? | 3 | DNA: chromosomes, replication, SNPs and other variants | 5.5 | R lecture | |
| 1 | Introduction | 1 | What we measure and why? | 4 | Gene expression | 2.5 | R lecture | |
| 1 | Introduction | 1 | What we measure and why? | 5 | Epigenetics | 2 | R lecture | |
| 1 | Introduction | 2 | R Programming Skills | 1 | RStudio | 4.5 | M lab | |
| 1 | Introduction | 2 | R Programming Skills | 2 | R Refresher | 5.5 | M lab | |
| 1 | Introduction | 2 | R Programming Skills | 3 | Installing Bioconductor and finding help | 10 | M lab | |
| 1 | Introduction | 3 | Exploratory data analysis | 1 | EDA Introduction | 2.5 | R lab | |
| 1 | Introduction | 3 | Exploratory data analysis | 2 | Histogram | 4.5 | R lab | |
| 1 | Introduction | 3 | Exploratory data analysis | 3 | qq-plot | 3.5 | R lab | |
| 1 | Introduction | 3 | Exploratory data analysis | 4 | Boxplot | 3 | R lab | |
| 1 | Introduction | 3 | Exploratory data analysis | 5 | Scatterplot | 7.5 | R lab | |
| 1 | Introduction | 3 | Exploratory data analysis | 6 | Median, Mad, and Spearman correlation | 4.5 | R lecture | |
| 1 | Introduction | 3 | Exploratory data analysis | 7 | Log transformation | 3 | R lecture | |
| 1 | Introduction | 3 | Exploratory data analysis | 8 | Symmetry of log ratios | 2 | R lecture | |
| 1 | Introduction | 3 | Exploratory data analysis | 9 | Plots to avoid | 5 | R lecture | |
| 1 | Introduction | 3 | Exploratory data analysis | 10 | Avoid pseudo 3D | 1.5 | R lecture | |
| 1 | Introduction | 3 | Exploratory data analysis | 11 | Examples from genomics | 6 | R lecture | |
| 1 | Introduction | 4 | Probability distributions | 1 | Introduction | 3 | R lecture | |
| 1 | Introduction | 4 | Probability distributions | 2 | Normal distribution | 6.5 | R lecture | |
| 2 | Measurement technology | 1 | Microarray technology | 1 | How hybridization works | 9 | R lecture | |
| 2 | Measurement technology | 1 | Microarray technology | 2 | How microarrays work and two color arrays | 4 | R lecture | |
| 2 | Measurement technology | 1 | Microarray technology | 3 | Applications of microarrays in genomics | 7.5 | R lecture | |
| 2 | Measurement technology | 2 | Next generation sequencing technology | 1 | Brief introduction to the mechanics of NGS | 8.5 | R lecture | |
| 2 | Measurement technology | 2 | Next generation sequencing technology | 2 | Applications of NGS in genomics | 7 | R lecture | |
| 2 | Measurement technology | 3 | Working with data in R | 1 | Basic Bioconductor infrastructure: IRanges | 6.5 | M lab | |
| 2 | Measurement technology | 3 | Working with data in R | 2 | Basic Bioconductor infrastructure: GRanges | 8.5 | M lab | |
| 2 | Measurement technology | 3 | Working with data in R | 3 | Basic Bioconductor infrastructure: ExpressionSet and SummarizedExperiment | 9 | M lab | |
| 2 | Measurement technology | 3 | Working with data in R | 4 | Installing from Github | 2 | M lab | |
| 2 | Measurement technology | 3 | Working with data in R | 5 | Reading Microarray raw data | 13.5 | R lab | |
| 2 | Measurement technology | 3 | Working with data in R | 6 | Microarray EDA | 12 | R lab | |
| 2 | Measurement technology | 3 | Working with data in R | 7 | Mapping algorithms and software | 12 | M lab | |
| 2 | Measurement technology | 3 | Working with data in R | 8 | NGS EDA | 8 | M lab | |
| 3 | Inference | 1 | Inference | 1 | Introduction to statistical inference | 2.5 | R lecture | |
| 3 | Inference | 1 | Inference | 2 | Association tests | 8.5 | R lecture | |
| 3 | Inference | 1 | Inference | 3 | t-tests | 5.5 | R lecture | |
| 3 | Inference | 1 | Inference | 4 | Central limit theorem (CLT) | 6.5 | R lecture | |
| 3 | Inference | 1 | Inference | 5 | t-test in practice | 8 | R lecture | |
| 3 | Inference | 1 | Inference | 6 | Gene expression example | 6.5 | R lecture | |
| 3 | Inference | 1 | Inference | 7 | Inference in practice | 7 | R lecture | |
| 3 | Inference | 1 | Inference | 8 | Mann-Whitney-Wilcoxon | 4.5 | R lecture | |
| 3 | Inference | 1 | Inference | 9 | Monte carlo simulation | 11.5 | R lab | |
| 3 | Inferece | 1 | Inference | 10 | Permutations | 5.5 | R lab | |
| 3 | Inference | 2 | Linear models | 1 | Introduction to linear models | 5.5 | R lecture | |
| 3 | Inference | 2 | Linear models | 2 | Matrix multiplication | 4.5 | R lecture | |
| 3 | Inference | 2 | Linear models | 3 | Fitting linear models and testing | 8.5 | R lecture | |
| 3 | Inference | 2 | Linear models | 3 | Expressing experimental design using R formula | 8 | M lab | |
| 3 | Inference | 2 | Linear models | 4 | Basic inference on microarray gene expression I | 10 | M lab | |
| 3 | Inference | 2 | Linear models | 5 | Basic inference on microarray gene expression II | 9.5 | M lab | |
| 4 | Background correction and normalization | 1 | Modeling | 1 | Modeling basics | 5 | R lecture | |
| 4 | Background correction and normalization | 1 | Modeling | 2 | Application of Poisson | 5 | R lecture | |
| 4 | Background correction and normalization | 1 | Modeling | 3 | Maximum likelihood estimate (MLE) | 4.5 | R lecture | |
| 4 | Background correction and normalization | 1 | Modeling | 4 | Modeling standard deviations | 4 | R lecture | |
| 4 | Background correction and normalization | 2 | Background | 1 | Introduction to microarray background | 7 | R lecture | |
| 4 | Background correction and normalization | 2 | Background | 2 | Background model | 4 | R lecture | |
| 4 | Background correction and normalization | 2 | Background | 3 | Different approaches to background | 8 | R lecture | |
| 4 | Background correction and normalization | 3 | Normalization | 1 | The need for normalization | 4 | R lecture | |
| 4 | Background correction and normalization | 3 | Normalization | 2 | Local regression: loess | 4.5 | R lecture | |
| 4 | Background correction and normalization | 3 | Normalization | 3 | Quantile normalization | 5 | R lecture | |
| 4 | Background correction and normalization | 3 | Normalization | 4 | Variance stabilizing normalization | 7 | R lecture | |
| 4 | Background correction and normalization | 3 | Normalization | 5 | When not to normalize | 6.5 | R lecture | |
| 4 | Background correction and normalization | 3 | Normalization | 6 | Subset quantile normalization | 3.5 | R lecture | |
| 4 | Background correction and normalization | 3 | Normalization | 7 | Normalization for RNA-seq | 5 | R lecture | |
| 4 | Background correction and normalization | 3 | Normalization | 8 | Motivating normalization for microarrays with EDA | 10.5 | R lab | |
| 4 | Background correction and normalization | 3 | Normalization | 9 | Loess normalization for microarrays | 8.5 | R lab | |
| 4 | Background correction and normalization | 3 | Normalization | 10 | Quantile normalization for microarrays | 1.5 | R lab | |
| 5 | Distance,clustering and prediction | 1 | Distance and clustering | 1 | Distance | 5.5 | R lecture | |
| 5 | Distance,clustering and prediction | 1 | Distance and clustering | 2 | Multidimensional scaling (MDS) | 2.5 | R lecture | |
| 5 | Distance,clustering and prediction | 1 | Distance and clustering | 3 | Clustering | 6 | R lecture | |
| 5 | Distance,clustering and prediction | 1 | Distance and clustering | 4 | How randomness affects clustering | 3 | R lecture | |
| 5 | Distance,clustering and prediction | 1 | Distance and clustering | 5 | K-means | 4 | R lecture | |
| 5 | Distance,clustering and prediction | 1 | Distance and clustering | 6 | Gene clustering | 6.5 | R lecture | |
| 5 | Distance,clustering and prediction | 1 | Distance and clustering | 7 | Batch effect | 3 | R lecture | |
| 5 | Distance,clustering and prediction | 1 | Distance and clustering | 8 | Distances and clustering I | 5 | M lab | |
| 5 | Distance,clustering and prediction | 1 | Distance and clustering | 9 | Distances and clustering II | 8 | M lab | |
| 5 | Distance,clustering and prediction | 1 | Distance and clustering | 10 | Dimension reduction and heatmaps | 9 | M lab | |
| 5 | Distance,clustering and prediction | 2 | Prediction | 1 | Conditional expectation | 3 | R lecture | |
| 5 | Distance,clustering and prediction | 2 | Prediction | 2 | 2 variable example: linear regression | 5 | R lecture | |
| 5 | Distance,clustering and prediction | 2 | Prediction | 3 | k-nearest neighbors | 7.5 | R lecture | |
| 5 | Distance,clustering and prediction | 2 | Prediction | 4 | Cross-validation | 11.5 | M lab | |
| 6 | Batch effects | 1 | Statistical solutions to batch effects | 1 | Confounding | 7 | R lecture | |
| 6 | Batch effects | 1 | Statistical solutions to batch effects | 2 | Confounding in genomics | 3.5 | R lecture | |
| 6 | Batch effects | 1 | Statistical solutions to batch effects | 3 | Modeling batch effects | 6.5 | R lecture | |
| 6 | Batch effects | 1 | Statistical solutions to batch effects | 4 | ComBat | 3 | R lecture | |
| 6 | Batch effects | 1 | Statistical solutions to batch effects | 5 | Factor analysis | 2.5 | R lecture | |
| 6 | Batch effects | 1 | Statistical solutions to batch effects | 6 | SVD and PCA | 7.5 | R lecture | |
| 6 | Batch effects | 1 | Statistical solutions to batch effects | 7 | PCA example | 5.5 | R lecture | |
| 6 | Batch effects | 1 | Statistical solutions to batch effects | 8 | Microarray example | 4.5 | R lecture | |
| 6 | Batch effects | 1 | Statistical solutions to batch effects | 9 | Surrogate variable analysis (SVA) | 6 | R lecture | |
| 6 | Batch effects | 2 | Applying batch effects solutions | 1 | Introduction to exploring batch effects in R | 7 | R lab | |
| 6 | Batch effects | 2 | Applying batch effects solutions | 2 | ComBat in R | 5 | R lab | |
| 6 | Batch effects | 2 | Applying batch effects solutions | 3 | SVA in R | 5 | R lab | |
| 6 | Batch effects | 2 | Applying batch effects solutions | 4 | PCA and SVD | 11.5 | M lab | |
| 7 | Advanced differential expression | 1 | Hierarchical modeling | 1 | Introduction to hierarchical models | 4 | R lecture | |
| 7 | Advanced differential expression | 1 | Hierarchical modeling | 2 | Hierarchical models: baseball example | 7.5 | R lecture | |
| 7 | Advanced differential expression | 1 | Hierarchical modeling | 3 | Hierarchical models for genes | 5 | R lecture | |
| 7 | Advanced differential expression | 1 | Hierarchical modeling | 4 | Hierarchical models for genes in practice | 2 | R lecture | |
| 7 | Advanced differential expression | 1 | Hierarchical modeling | 5 | Using the limma package | 13 | M lab | |
| 7 | Advanced differential expression | 2 | Multiple comparisons | 1 | Introduction to multiple testing | 6 | R lecture | |
| 7 | Advanced differential expression | 2 | Multiple comparisons | 2 | Combining null and non-null tests | 2 | R lecture | |
| 7 | Advanced differential expression | 2 | Multiple comparisons | 3 | Multiple hypothesis testing | 9 | R lecture | |
| 7 | Advanced differential expression | 2 | Multiple comparisons | 4 | False discovery rate | 6 | R lecture | |
| 7 | Advanced differential expression | 3 | Gene set testing | 1 | Gene sets | 5 | R lecture | |
| 7 | Advanced differential expression | 3 | Gene set testing | 2 | Summary statistics for gene sets | 4 | R lecture | |
| 7 | Advanced differential expression | 3 | Gene set testing | 3 | Hypothesis testing | 5.5 | R lecture | |
| 7 | Advanced differential expression | 3 | Gene set testing | 4 | Permutations | 4.5 | R lecture | |
| 7 | Advanced differential expression | 3 | Gene set testing | 5 | Gene set testing in R part I | 8.5 | M lab | |
| 7 | Advanced differential expression | 3 | Gene set testing | 6 | Gene set testing in R part II | 9 | M lab | |
| 7 | Advanced differential expression | 4 | Gene and technology annotations | 1 | Annotating features | 13.5 | M lab | |
| 8 | Advanced topics | 1 | Manipulating NGS data using Bioconductor | 1 | Visualizing NGS data part 1 | 6.5 | M lab | |
| 8 | Advanced topics | 1 | Manipulating NGS data using Bioconductor | 2 | Visualizing NGS data part 2 | 10 | M lab | |
| 8 | Advanced topics | 1 | Manipulating NGS data using Bioconductor | 3 | Visualizing NGS data part 3 | 7 | M lab | |
| 8 | Advanced topics | 1 | Manipulating NGS data using Bioconductor | 4 | Making an NGS read count table | 15.5 | M lab | |
| 8 | Advanced topics | 2 | DNA Methylation | 1 | Introduction to DNA methylation | 3.5 | R lecture | |
| 8 | Advanced topics | 2 | DNA Methylation | 2 | CpG islands | 4 | R lecture | |
| 8 | Advanced topics | 2 | DNA Methylation | 3 | Bisulfite treatment | 1.5 | R lecture | |
| 8 | Advanced topics | 2 | DNA Methylation | 4 | Measuring methylation from array | 2 | R lecture | |
| 8 | Advanced topics | 2 | DNA Methylation | 5 | Measuring methylation from sequencing | 2 | R lecture | |
| 8 | Advanced topics | 2 | DNA Methylation | 6 | DNA Methylation part 1 | 11.5 | R lab | |
| 8 | Advanced topics | 2 | DNA Methylation | 7 | DNA Methylation part 2 | 13 | R lab | |
| 8 | Advanced topics | 2 | DNA Methylation | 8 | Reading 450K files with minfi | 8.5 | R lab | |
| 8 | Advanced topics | 3 | ChIP Sequencing | 1 | Introduction to ChIP-seq | 3.5 | R lecture | |
| 8 | Advanced topics | 3 | ChIP Sequencing | 2 | Computational analysis of ChIP-seq | 3 | R lecture | |
| 8 | Advanced topics | 3 | ChIP Sequencing | 3 | Statistical analysis of ChIP seq | 3.5 | R lecture | |
| 8 | Advanced topics | 3 | ChIP Sequencing | 4 | ChIP-seq part 1 | 13 | M lab | |
| 8 | Advanced topics | 3 | ChIP Sequencing | 5 | ChIP-seq part 2 | 15 | M lab | |
| 8 | Advanced topics | 4 | RNA Sequencing | 1 | Introduction to RNA-seq | 5.5 | R lecture | |
| 8 | Advanced topics | 4 | RNA Sequencing | 2 | Data generation and simple counts | 5 | R lecture | |
| 8 | Advanced topics | 4 | RNA Sequencing | 3 | Model for quantification | 4.5 | R lecture | |
| 8 | Advanced topics | 4 | RNA Sequencing | 4 | Transcript quantification | 7 | R lecture | |
| 8 | Advanced topics | 4 | RNA Sequencing | 5 | Unstable quantification | 5 | R lecture | |
| 8 | Advanced topics | 4 | RNA Sequencing | 6 | RNA-seq part 1 | 11 | M lab | |
| 8 | Advanced topics | 4 | RNA Sequencing | 7 | RNA-seq part 2 | 12.5 | M lab | |
| 8 | Advanced topics | 4 | RNA Sequencing | 8 | RNA-seq part 3 | 11.5 | M lab | |
| 8 | Advanced topics | 5 | Genome Variation | 1 | SNPs and SNVs | 6 | R lecture | |
| 8 | Advanced topics | 5 | Genome Variation | 2 | Copy number variants from array | 4 | R lecture | |
| 8 | Advanced topics | 5 | Genome Variation | 3 | Copy number variants from sequencing | 4.5 | R lecture | |
| 8 | Advanced topics | 5 | Genome Variation | 4 | Variant calling part 1 | 12 | M lab | |
| 8 | Advanced topics | 5 | Genome Variation | 5 | Variant calling part 2 | 6.5 | M lab |
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