From ee9081ddb3e76943861147424c7d4829f21f1a7e Mon Sep 17 00:00:00 2001 From: chizhang Date: Thu, 4 Jan 2024 17:24:56 +0100 Subject: [PATCH] add lab notes --- docs/lab/lab_day2_pca.html | 680 ++++++++++++++++++ .../figure-html/pca-nci-keeppc-1.png | Bin 0 -> 119326 bytes .../figure-html/pca-nci-plot2-1.png | Bin 0 -> 120790 bytes .../figure-html/pca-nci-plotpc-1.png | Bin 0 -> 118935 bytes docs/lab/lab_day3_pca.html | 680 ++++++++++++++++++ .../figure-html/pca-nci-keeppc-1.png | Bin 0 -> 119326 bytes .../figure-html/pca-nci-plot2-1.png | Bin 0 -> 120790 bytes .../figure-html/pca-nci-plotpc-1.png | Bin 0 -> 118935 bytes docs/lab/lab_day4_clustering.html | 510 +++++++++++++ .../figure-html/pca-nci-keeppc-1.png | Bin 0 -> 119326 bytes .../figure-html/pca-nci-plot2-1.png | Bin 0 -> 120790 bytes .../figure-html/pca-nci-plotpc-1.png | Bin 0 -> 118935 bytes docs/lab/overview.html | 50 +- docs/search.json | 53 +- lab/.DS_Store | Bin 8196 -> 6148 bytes lab/code/lab_pca.R | 113 +++ lab/lab_day3_pca.qmd | 115 +++ lab/lab_day4_clustering.qmd | 25 + lab/overview.qmd | 9 +- 19 files changed, 2228 insertions(+), 7 deletions(-) create mode 100644 docs/lab/lab_day2_pca.html create mode 100644 docs/lab/lab_day2_pca_files/figure-html/pca-nci-keeppc-1.png create mode 100644 docs/lab/lab_day2_pca_files/figure-html/pca-nci-plot2-1.png create mode 100644 docs/lab/lab_day2_pca_files/figure-html/pca-nci-plotpc-1.png create mode 100644 docs/lab/lab_day3_pca.html create mode 100644 docs/lab/lab_day3_pca_files/figure-html/pca-nci-keeppc-1.png create mode 100644 docs/lab/lab_day3_pca_files/figure-html/pca-nci-plot2-1.png create mode 100644 docs/lab/lab_day3_pca_files/figure-html/pca-nci-plotpc-1.png create mode 100644 docs/lab/lab_day4_clustering.html create mode 100644 docs/lab/lab_day4_clustering_files/figure-html/pca-nci-keeppc-1.png create mode 100644 docs/lab/lab_day4_clustering_files/figure-html/pca-nci-plot2-1.png create mode 100644 docs/lab/lab_day4_clustering_files/figure-html/pca-nci-plotpc-1.png create mode 100644 lab/code/lab_pca.R create mode 100644 lab/lab_day3_pca.qmd create mode 100644 lab/lab_day4_clustering.qmd diff --git a/docs/lab/lab_day2_pca.html b/docs/lab/lab_day2_pca.html new file mode 100644 index 0000000..4cb64b8 --- /dev/null +++ b/docs/lab/lab_day2_pca.html @@ -0,0 +1,680 @@ + + + + + + + + + +MED3007 V24 - R Lab (day 2): PCA + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

R Lab (day 2): PCA

+
+ + + +
+ + + + +
+ + +
+ +

Datasets

+

R script

+
+

NCI60

+
+
library(ISLR)
+nci.labs <- NCI60$labs # Sample labels (tissue type)
+nci.data <- NCI60$data # Gene expression data set
+
+
+
# what if I would like to compute the mean of each gene within each tissue type?
+tissue.means <- apply(nci.data, 2, function(x){tapply(x, nci.labs, mean)})
+dim(tissue.means)
+
+
[1]   14 6830
+
+
table(nci.labs)
+
+
nci.labs
+     BREAST         CNS       COLON K562A-repro K562B-repro    LEUKEMIA 
+          7           5           7           1           1           6 
+MCF7A-repro MCF7D-repro    MELANOMA       NSCLC     OVARIAN    PROSTATE 
+          1           1           8           9           6           2 
+      RENAL     UNKNOWN 
+          9           1 
+
+
+

Compute the PC

+
+
# PCA analysis after scaling the variables to standard deviation one:
+pr.out <- prcomp(nci.data, scale=TRUE)
+
+

Print the summary output

+
+
summary(pr.out)
+
+
+
pr.var <- pr.out$sdev^2
+pve <- pr.var/sum(pr.var)
+pve <- 100*pve
+
+par(mfrow=c(1,2))
+plot(pve,  type="o", ylab="PVE", xlab="Principal Component", col="blue")
+plot(cumsum(pve), type="o", ylab="Cumulative PVE", xlab="Principal Component", col="brown3")
+
+

+
+
+
+

How many PCs to keep?

+
+
mysel80 <- which(cumsum(pve) > 80)[1] # explains 80% of the variability
+mysel70 <- which(cumsum(pve) > 70)[1] # explains 70% of the variability
+
+par(mfrow=c(1,2)) # plot contains two smaller plots next to each other
+plot(pve,  type="o", ylab="PVE", xlab="Principal Component", col="blue")
+abline(v = mysel80)
+abline(v = mysel70, col=3)
+plot(cumsum(pve), type="o", ylab="Cumulative PVE", xlab="Principal Component", col="brown3")
+abline(v = mysel80)
+abline(h = 80)
+abline(v = mysel70, col=3)
+abline(h = 70, col=3)
+
+

+
+
+

If we decide to only keep the principal components that explains 70% of the variance, we end up with 24 components, which we can further analyse to better understand the relationships between the variables. For simplicity we only look at the first few components.

+

We plot the first few principal component score vectors, to visualize the results. The observations (cell lines) corresponding to a given cancer type will be plotted in the same colour.

+
+
Cols=function(vec){
+  cols=rainbow(length(unique(vec)))
+  return(cols[as.numeric(as.factor(vec))])
+}
+
+# Plot the first vs second and first vs third principal component score vectors,
+# with colors associated to labels (using the Cols() helper function)
+par(mfrow=c(1,2))
+plot(pr.out$x[,1:2], col=Cols(nci.labs), pch=19,xlab="PC 1",ylab=" PC 2")
+plot(pr.out$x[,c(1,3)], col=Cols(nci.labs), pch=19,xlab="PC 1",ylab=" PC 3")
+legend('topleft', col=rainbow(length(unique(nci.labs))), legend=unique(nci.labs), bty='n', lwd=2, cex=.6)
+
+

+
+
+ + + + +
+
+ +
+ +
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b/docs/lab/lab_day3_pca.html new file mode 100644 index 0000000..96b26f1 --- /dev/null +++ b/docs/lab/lab_day3_pca.html @@ -0,0 +1,680 @@ + + + + + + + + + +MED3007 V24 - R Lab (day 3): Principal Component Analysis + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ + + + +
+ +
+
+

R Lab (day 3): Principal Component Analysis

+
+ + + +
+ + + + +
+ + +
+ +

Datasets

+

R script

+
+

NCI60

+
+
library(ISLR)
+nci.labs <- NCI60$labs # Sample labels (tissue type)
+nci.data <- NCI60$data # Gene expression data set
+
+
+
# what if I would like to compute the mean of each gene within each tissue type?
+tissue.means <- apply(nci.data, 2, function(x){tapply(x, nci.labs, mean)})
+dim(tissue.means)
+
+
[1]   14 6830
+
+
table(nci.labs)
+
+
nci.labs
+     BREAST         CNS       COLON K562A-repro K562B-repro    LEUKEMIA 
+          7           5           7           1           1           6 
+MCF7A-repro MCF7D-repro    MELANOMA       NSCLC     OVARIAN    PROSTATE 
+          1           1           8           9           6           2 
+      RENAL     UNKNOWN 
+          9           1 
+
+
+

Compute the PC

+
+
# PCA analysis after scaling the variables to standard deviation one:
+pr.out <- prcomp(nci.data, scale=TRUE)
+
+

Print the summary output

+
+
summary(pr.out)
+
+
+
pr.var <- pr.out$sdev^2
+pve <- pr.var/sum(pr.var)
+pve <- 100*pve
+
+par(mfrow=c(1,2))
+plot(pve,  type="o", ylab="PVE", xlab="Principal Component", col="blue")
+plot(cumsum(pve), type="o", ylab="Cumulative PVE", xlab="Principal Component", col="brown3")
+
+

+
+
+
+

How many PCs to keep?

+
+
mysel80 <- which(cumsum(pve) > 80)[1] # explains 80% of the variability
+mysel70 <- which(cumsum(pve) > 70)[1] # explains 70% of the variability
+
+par(mfrow=c(1,2)) # plot contains two smaller plots next to each other
+plot(pve,  type="o", ylab="PVE", xlab="Principal Component", col="blue")
+abline(v = mysel80)
+abline(v = mysel70, col=3)
+plot(cumsum(pve), type="o", ylab="Cumulative PVE", xlab="Principal Component", col="brown3")
+abline(v = mysel80)
+abline(h = 80)
+abline(v = mysel70, col=3)
+abline(h = 70, col=3)
+
+

+
+
+

If we decide to only keep the principal components that explains 70% of the variance, we end up with 24 components, which we can further analyse to better understand the relationships between the variables. For simplicity we only look at the first few components.

+

We plot the first few principal component score vectors, to visualize the results. The observations (cell lines) corresponding to a given cancer type will be plotted in the same colour.

+
+
Cols=function(vec){
+  cols=rainbow(length(unique(vec)))
+  return(cols[as.numeric(as.factor(vec))])
+}
+
+# Plot the first vs second and first vs third principal component score vectors,
+# with colors associated to labels (using the Cols() helper function)
+par(mfrow=c(1,2))
+plot(pr.out$x[,1:2], col=Cols(nci.labs), pch=19,xlab="PC 1",ylab=" PC 2")
+plot(pr.out$x[,c(1,3)], col=Cols(nci.labs), pch=19,xlab="PC 1",ylab=" PC 3")
+legend('topleft', col=rainbow(length(unique(nci.labs))), legend=unique(nci.labs), bty='n', lwd=2, cex=.6)
+
+

+
+
+ + + + +
+
+ +
+ +
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b/docs/lab/lab_day4_clustering.html new file mode 100644 index 0000000..306d235 --- /dev/null +++ b/docs/lab/lab_day4_clustering.html @@ -0,0 +1,510 @@ + + + + + + + + + +MED3007 V24 - R Lab (day 4): Clustering + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ + + + +
+ +
+
+

R Lab (day 4): Clustering

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+ + + +
+ + + + +
+ + +
+ +

Datasets

+

R script

+
+

NCI60

+
+
library(ISLR)
+nci.labs <- NCI60$labs # Sample labels (tissue type)
+nci.data <- NCI60$data # Gene expression data set
+
+ + + + +
+ +
+ +
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b/docs/lab/overview.html index 0cc9ee0..81d4def 100644 --- a/docs/lab/overview.html +++ b/docs/lab/overview.html @@ -154,6 +154,7 @@

On this page

@@ -188,7 +189,7 @@

Datasets

day 3 (PCA)

  • pima.txt
  • -
  • NCI60
  • +
  • NCI60 (done)
  • USarrest
  • Food
  • CH10Ex11
  • @@ -200,6 +201,40 @@

    Datasets

  • NCI60
  • CH10Ex11
+ +
+

Lab notes and R scripts

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
DayLab notesR script
Day 1Day 1: Introduction to R
Day 2Day 2: Multiple testing
Day 3Day 3: Principal Component Analysis
Day 4Day 4: Clustering
@@ -510,7 +545,7 @@

Datasets

day 3 (PCA) - pima.txt -- NCI60 +- NCI60 (done) - USarrest - Food - CH10Ex11 @@ -523,9 +558,16 @@

Datasets

- CH10Ex11 - +## Lab notes and R scripts - +| Day | Lab notes | R script | +|:-------------:|:------------------------------------:|:-----------------:| +| Day 1 | [Day 1: Introduction to R]() | | +| Day 2 | [Day 2: Multiple testing]() | | +| Day 3 | [Day 3: Principal Component Analysis](lab_day3_pca.qmd) | | +| Day 4 | [Day 4: Clustering](lab_day4_clustering.qmd) | | + + diff --git a/docs/search.json b/docs/search.json index 05fc312..1661779 100644 --- a/docs/search.json +++ b/docs/search.json @@ -123,7 +123,7 @@ "href": "lab/overview.html", "title": "R Lab: Overview", "section": "", - "text": "day 2 (MT)\n\nbrainshake (t-test)\nNCI60 (t-test, multiple testing)\nCh10Ex11\n\nday 3 (PCA)\n\npima.txt\nNCI60\nUSarrest\nFood\nCH10Ex11\n\nday 4 (clustering)\n\niris\nbirds\nNCI60\nCH10Ex11" + "text": "day 2 (MT)\n\nbrainshake (t-test)\nNCI60 (t-test, multiple testing)\nCh10Ex11\n\nday 3 (PCA)\n\npima.txt\nNCI60 (done)\nUSarrest\nFood\nCH10Ex11\n\nday 4 (clustering)\n\niris\nbirds\nNCI60\nCH10Ex11" }, { "objectID": "lab/overview.html#a-cancer-modeling-example", @@ -165,6 +165,55 @@ "href": "lab/overview.html#datasets", "title": "R Lab: Overview", "section": "", - "text": "day 2 (MT)\n\nbrainshake (t-test)\nNCI60 (t-test, multiple testing)\nCh10Ex11\n\nday 3 (PCA)\n\npima.txt\nNCI60\nUSarrest\nFood\nCH10Ex11\n\nday 4 (clustering)\n\niris\nbirds\nNCI60\nCH10Ex11" + "text": "day 2 (MT)\n\nbrainshake (t-test)\nNCI60 (t-test, multiple testing)\nCh10Ex11\n\nday 3 (PCA)\n\npima.txt\nNCI60 (done)\nUSarrest\nFood\nCH10Ex11\n\nday 4 (clustering)\n\niris\nbirds\nNCI60\nCH10Ex11" + }, + { + "objectID": "lab/lab_day2_pca.html", + "href": "lab/lab_day2_pca.html", + "title": "R Lab (day 2): PCA", + "section": "", + "text": "Datasets\nR script" + }, + { + "objectID": "lab/lab_day2_pca.html#nci60", + "href": "lab/lab_day2_pca.html#nci60", + "title": "R Lab (day 2): PCA", + "section": "NCI60", + "text": "NCI60\n\nlibrary(ISLR)\nnci.labs <- NCI60$labs # Sample labels (tissue type)\nnci.data <- NCI60$data # Gene expression data set\n\n\n# what if I would like to compute the mean of each gene within each tissue type?\ntissue.means <- apply(nci.data, 2, function(x){tapply(x, nci.labs, mean)})\ndim(tissue.means)\n\n[1] 14 6830\n\ntable(nci.labs)\n\nnci.labs\n BREAST CNS COLON K562A-repro K562B-repro LEUKEMIA \n 7 5 7 1 1 6 \nMCF7A-repro MCF7D-repro MELANOMA NSCLC OVARIAN PROSTATE \n 1 1 8 9 6 2 \n RENAL UNKNOWN \n 9 1 \n\n\nCompute the PC\n\n# PCA analysis after scaling the variables to standard deviation one:\npr.out <- prcomp(nci.data, scale=TRUE)\n\nPrint the summary output\n\nsummary(pr.out)\n\n\npr.var <- pr.out$sdev^2\npve <- pr.var/sum(pr.var)\npve <- 100*pve\n\npar(mfrow=c(1,2))\nplot(pve, type=\"o\", ylab=\"PVE\", xlab=\"Principal Component\", col=\"blue\")\nplot(cumsum(pve), type=\"o\", ylab=\"Cumulative PVE\", xlab=\"Principal Component\", col=\"brown3\")\n\n\n\n\n\nHow many PCs to keep?\n\nmysel80 <- which(cumsum(pve) > 80)[1] # explains 80% of the variability\nmysel70 <- which(cumsum(pve) > 70)[1] # explains 70% of the variability\n\npar(mfrow=c(1,2)) # plot contains two smaller plots next to each other\nplot(pve, type=\"o\", ylab=\"PVE\", xlab=\"Principal Component\", col=\"blue\")\nabline(v = mysel80)\nabline(v = mysel70, col=3)\nplot(cumsum(pve), type=\"o\", ylab=\"Cumulative PVE\", xlab=\"Principal Component\", col=\"brown3\")\nabline(v = mysel80)\nabline(h = 80)\nabline(v = mysel70, col=3)\nabline(h = 70, col=3)\n\n\n\n\nIf we decide to only keep the principal components that explains 70% of the variance, we end up with 24 components, which we can further analyse to better understand the relationships between the variables. For simplicity we only look at the first few components.\nWe plot the first few principal component score vectors, to visualize the results. The observations (cell lines) corresponding to a given cancer type will be plotted in the same colour.\n\nCols=function(vec){\n cols=rainbow(length(unique(vec)))\n return(cols[as.numeric(as.factor(vec))])\n}\n\n# Plot the first vs second and first vs third principal component score vectors,\n# with colors associated to labels (using the Cols() helper function)\npar(mfrow=c(1,2))\nplot(pr.out$x[,1:2], col=Cols(nci.labs), pch=19,xlab=\"PC 1\",ylab=\" PC 2\")\nplot(pr.out$x[,c(1,3)], col=Cols(nci.labs), pch=19,xlab=\"PC 1\",ylab=\" PC 3\")\nlegend('topleft', col=rainbow(length(unique(nci.labs))), legend=unique(nci.labs), bty='n', lwd=2, cex=.6)" + }, + { + "objectID": "lab/overview.html#lab-notes-and-r-scripts", + "href": "lab/overview.html#lab-notes-and-r-scripts", + "title": "R Lab: Overview", + "section": "Lab notes and R scripts", + "text": "Lab notes and R scripts\n\n\n\nDay\nLab notes\nR script\n\n\n\n\nDay 1\nDay 1: Introduction to R\n\n\n\nDay 2\nDay 2: Multiple testing\n\n\n\nDay 3\nDay 3: Principal Component Analysis\n\n\n\nDay 4\nDay 4: Clustering" + }, + { + "objectID": "lab/lab_day3_pca.html", + "href": "lab/lab_day3_pca.html", + "title": "R Lab (day 3): Principal Component Analysis", + "section": "", + "text": "Datasets\nR script" + }, + { + "objectID": "lab/lab_day3_pca.html#nci60", + "href": "lab/lab_day3_pca.html#nci60", + "title": "R Lab (day 3): Principal Component Analysis", + "section": "NCI60", + "text": "NCI60\n\nlibrary(ISLR)\nnci.labs <- NCI60$labs # Sample labels (tissue type)\nnci.data <- NCI60$data # Gene expression data set\n\n\n# what if I would like to compute the mean of each gene within each tissue type?\ntissue.means <- apply(nci.data, 2, function(x){tapply(x, nci.labs, mean)})\ndim(tissue.means)\n\n[1] 14 6830\n\ntable(nci.labs)\n\nnci.labs\n BREAST CNS COLON K562A-repro K562B-repro LEUKEMIA \n 7 5 7 1 1 6 \nMCF7A-repro MCF7D-repro MELANOMA NSCLC OVARIAN PROSTATE \n 1 1 8 9 6 2 \n RENAL UNKNOWN \n 9 1 \n\n\nCompute the PC\n\n# PCA analysis after scaling the variables to standard deviation one:\npr.out <- prcomp(nci.data, scale=TRUE)\n\nPrint the summary output\n\nsummary(pr.out)\n\n\npr.var <- pr.out$sdev^2\npve <- pr.var/sum(pr.var)\npve <- 100*pve\n\npar(mfrow=c(1,2))\nplot(pve, type=\"o\", ylab=\"PVE\", xlab=\"Principal Component\", col=\"blue\")\nplot(cumsum(pve), type=\"o\", ylab=\"Cumulative PVE\", xlab=\"Principal Component\", col=\"brown3\")\n\n\n\n\n\nHow many PCs to keep?\n\nmysel80 <- which(cumsum(pve) > 80)[1] # explains 80% of the variability\nmysel70 <- which(cumsum(pve) > 70)[1] # explains 70% of the variability\n\npar(mfrow=c(1,2)) # plot contains two smaller plots next to each other\nplot(pve, type=\"o\", ylab=\"PVE\", xlab=\"Principal Component\", col=\"blue\")\nabline(v = mysel80)\nabline(v = mysel70, col=3)\nplot(cumsum(pve), type=\"o\", ylab=\"Cumulative PVE\", xlab=\"Principal Component\", col=\"brown3\")\nabline(v = mysel80)\nabline(h = 80)\nabline(v = mysel70, col=3)\nabline(h = 70, col=3)\n\n\n\n\nIf we decide to only keep the principal components that explains 70% of the variance, we end up with 24 components, which we can further analyse to better understand the relationships between the variables. For simplicity we only look at the first few components.\nWe plot the first few principal component score vectors, to visualize the results. The observations (cell lines) corresponding to a given cancer type will be plotted in the same colour.\n\nCols=function(vec){\n cols=rainbow(length(unique(vec)))\n return(cols[as.numeric(as.factor(vec))])\n}\n\n# Plot the first vs second and first vs third principal component score vectors,\n# with colors associated to labels (using the Cols() helper function)\npar(mfrow=c(1,2))\nplot(pr.out$x[,1:2], col=Cols(nci.labs), pch=19,xlab=\"PC 1\",ylab=\" PC 2\")\nplot(pr.out$x[,c(1,3)], col=Cols(nci.labs), pch=19,xlab=\"PC 1\",ylab=\" PC 3\")\nlegend('topleft', col=rainbow(length(unique(nci.labs))), legend=unique(nci.labs), bty='n', lwd=2, cex=.6)" + }, + { + "objectID": "lab/lab_day4_clustering.html", + "href": "lab/lab_day4_clustering.html", + "title": "R Lab (day 4): Clustering", + "section": "", + "text": "Datasets\nR script" + }, + { + "objectID": "lab/lab_day4_clustering.html#nci60", + "href": "lab/lab_day4_clustering.html#nci60", + "title": "R Lab (day 4): Clustering", + "section": "NCI60", + "text": "NCI60\n\nlibrary(ISLR)\nnci.labs <- NCI60$labs # Sample labels (tissue type)\nnci.data <- NCI60$data # Gene expression data set" } ] \ No newline at end of file diff --git a/lab/.DS_Store b/lab/.DS_Store index a041b2afcf28806e73fce7b0947525139db07eb2..5788a4695eccf832b903bdbb709a5b2805c618ec 100644 GIT binary patch delta 168 zcmZp1XfcprU|?W$DortDU=RQ@Ie-{Mvv5r;6q~50D98(v2Z`mR8wMxm=N4=%T+PhL zJK0{)M7=Qc?Vr}gM;$@x8Il?D8B!QhbMsSNl5+BsfMOg_`N^{cl{Q}yWMbM_R>QcM qor6P=87K<`0^C5t6=d1Q!tczJ`DHvoRx>a`oB?tf!{&INIm`e@;Uu;I delta 409 zcmZoMXmOBWU|?W$DortDU;r^WfEYvza8FDWo2aKKDg=@T@);Q78H^YV7z`Oq8T2*_ za)_~PEa+uk%+A3vv4BM$q>7ipk)f2Kh#?hOB|`y23PalD95#7>7KUVod>}8?GbcYe zDJMS(WDO9n2V%YdU;tz>Fu>I%l@}L4)$L3w$jK}&F)+Bs$i&RT%Er#YKKU9;v;YTZ zyntkNwXUI=v5tbJx!Gg~R%yoW$uX?*j6IWiSWMJU+`_`n?0lkq4^i~egqs2h4 z1~K>mWs?~6AYMiF9Ah`oAgJdy%k#Wv=91tBinxLT7#N7(nJ4p$cuw}`;oty 80)[1] # explains 80% of the variability +mysel70 <- which(cumsum(pve) > 70)[1] # explains 70% of the variability + +par(mfrow=c(1,2)) # plot contains two smaller plots next to each other +plot(pve, type="o", ylab="PVE", xlab="Principal Component", col="blue") +abline(v = mysel80) +abline(v = mysel70, col=3) +plot(cumsum(pve), type="o", ylab="Cumulative PVE", xlab="Principal Component", col="brown3") +abline(v = mysel80) +abline(h = 80) +abline(v = mysel70, col=3) +abline(h = 70, col=3) + +## If we decide to only keep the principal components that explains 70% of the variance, +## we end up with 24 components, which we can further analyse to better understand the +## relationships between the variables. For simplicity we only look at the first few components. +## We plot the first few principal component score vectors, to visualize the results. +## The observations (cell lines) corresponding to a given cancer type +## will be plotted in the same colour. + +# we here define a "helper function", which assigns a different colour to each sample +# label (nci.labs) in the next plot +Cols=function(vec){ + cols=rainbow(length(unique(vec))) + return(cols[as.numeric(as.factor(vec))]) +} + +# Plot the first vs second and first vs third principal component score vectors, +# with colors associated to labels (using the Cols() helper function) +par(mfrow=c(1,2)) +plot(pr.out$x[,1:2], col=Cols(nci.labs), pch=19,xlab="PC 1",ylab=" PC 2") +plot(pr.out$x[,c(1,3)], col=Cols(nci.labs), pch=19,xlab="PC 1",ylab=" PC 3") +legend('topleft', col=rainbow(length(unique(nci.labs))), legend=unique(nci.labs), bty='n', lwd=2, cex=.6) + + + + + +# Ch10Ex11 ---- + + +## Consider again the gene expression data set "Ch10Ex11.csv" +## (which can be also found on the book website, www.StatLearning.com) +## that consists of 40 tissue samples with measurements on 1,000 genes. +## The first 20 samples are from healthy patients, +## while the second 20 are from a diseased group. + +## 1. Load in the data using read.csv(). You will need to select header=F. +## Alternatively: load in the data using "Import dataset" in the upper right window, +## and click "no" on the "Heading" option. +## 2. Perform a PCA of these data and visualize the results. Note: remember to check +## if the variables (genes) are on the columns in the dataset before running the PCA. +## If they are not: use t() to transform the dataset. + + + diff --git a/lab/lab_day3_pca.qmd b/lab/lab_day3_pca.qmd new file mode 100644 index 0000000..d93bb61 --- /dev/null +++ b/lab/lab_day3_pca.qmd @@ -0,0 +1,115 @@ +--- +title: "R Lab (day 3): Principal Component Analysis" +format: + html: + code-fold: false + code-tools: true +--- + +Datasets + +R script + + +## NCI60 + +```{r} +#| label: pca-nci-loaddata +#| warning: false +#| echo: true + +library(ISLR) +nci.labs <- NCI60$labs # Sample labels (tissue type) +nci.data <- NCI60$data # Gene expression data set +``` + +```{r} +#| label: pca-nci-summary +#| warning: false +#| echo: true +# what if I would like to compute the mean of each gene within each tissue type? +tissue.means <- apply(nci.data, 2, function(x){tapply(x, nci.labs, mean)}) +dim(tissue.means) +table(nci.labs) +``` + +Compute the PC + +```{r} +#| label: pca-nci-pc +#| warning: false +#| echo: true +# PCA analysis after scaling the variables to standard deviation one: +pr.out <- prcomp(nci.data, scale=TRUE) +``` + + +Print the summary output + +```{r} +#| label: pca-nci-pcsummary +#| warning: false +#| echo: true +#| eval: false +summary(pr.out) +``` + + +```{r} +#| label: pca-nci-plotpc +#| warning: false +#| echo: true +pr.var <- pr.out$sdev^2 +pve <- pr.var/sum(pr.var) +pve <- 100*pve + +par(mfrow=c(1,2)) +plot(pve, type="o", ylab="PVE", xlab="Principal Component", col="blue") +plot(cumsum(pve), type="o", ylab="Cumulative PVE", xlab="Principal Component", col="brown3") +``` + +### How many PCs to keep? + +```{r} +#| label: pca-nci-keeppc +#| warning: false +#| echo: true +mysel80 <- which(cumsum(pve) > 80)[1] # explains 80% of the variability +mysel70 <- which(cumsum(pve) > 70)[1] # explains 70% of the variability + +par(mfrow=c(1,2)) # plot contains two smaller plots next to each other +plot(pve, type="o", ylab="PVE", xlab="Principal Component", col="blue") +abline(v = mysel80) +abline(v = mysel70, col=3) +plot(cumsum(pve), type="o", ylab="Cumulative PVE", xlab="Principal Component", col="brown3") +abline(v = mysel80) +abline(h = 80) +abline(v = mysel70, col=3) +abline(h = 70, col=3) +``` + + +If we decide to only keep the principal components that explains 70% of the variance, we end up with 24 components, which we can further analyse to better understand the relationships between the variables. For simplicity we only look at the first few components. + +We plot the first few principal component score vectors, to visualize the results. The observations (cell lines) corresponding to a given cancer type will be plotted in the same colour. + +```{r} +#| label: pca-nci-plot2 +#| warning: false +#| echo: true +Cols=function(vec){ + cols=rainbow(length(unique(vec))) + return(cols[as.numeric(as.factor(vec))]) +} + +# Plot the first vs second and first vs third principal component score vectors, +# with colors associated to labels (using the Cols() helper function) +par(mfrow=c(1,2)) +plot(pr.out$x[,1:2], col=Cols(nci.labs), pch=19,xlab="PC 1",ylab=" PC 2") +plot(pr.out$x[,c(1,3)], col=Cols(nci.labs), pch=19,xlab="PC 1",ylab=" PC 3") +legend('topleft', col=rainbow(length(unique(nci.labs))), legend=unique(nci.labs), bty='n', lwd=2, cex=.6) +``` + + + + diff --git a/lab/lab_day4_clustering.qmd b/lab/lab_day4_clustering.qmd new file mode 100644 index 0000000..cb6fc8f --- /dev/null +++ b/lab/lab_day4_clustering.qmd @@ -0,0 +1,25 @@ +--- +title: "R Lab (day 4): Clustering" +format: + html: + code-fold: false + code-tools: true +--- + +Datasets + +R script + + +## NCI60 + +```{r} +#| label: clust-loaddata +#| warning: false +#| echo: true + +library(ISLR) +nci.labs <- NCI60$labs # Sample labels (tissue type) +nci.data <- NCI60$data # Gene expression data set +``` + diff --git a/lab/overview.qmd b/lab/overview.qmd index 765002c..9f4d830 100644 --- a/lab/overview.qmd +++ b/lab/overview.qmd @@ -17,7 +17,7 @@ day 2 (MT) day 3 (PCA) - pima.txt -- NCI60 +- NCI60 (done) - USarrest - Food - CH10Ex11 @@ -30,7 +30,14 @@ day 4 (clustering) - CH10Ex11 +## Lab notes and R scripts +| Day | Lab notes | R script | +|:-------------:|:------------------------------------:|:-----------------:| +| Day 1 | [Day 1: Introduction to R]() | | +| Day 2 | [Day 2: Multiple testing]() | | +| Day 3 | [Day 3: Principal Component Analysis](lab_day3_pca.qmd) | | +| Day 4 | [Day 4: Clustering](lab_day4_clustering.qmd) | |

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