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comparison test-data/output_summary.Rnw @ 2:9cd937788131 draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/mageck commit 49e456dda49db1f52fc876f406a10273a408b1a2
author iuc
date Wed, 04 Apr 2018 11:03:44 -0400
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1:93f268840b0c 2:9cd937788131
1 % This is a template file for Sweave used in MAGeCK
2 % Author: Wei Li, Shirley Liu lab
3 % Do not modify lines beginning with "#__".
4 \documentclass{article}
5
6 \usepackage{amsmath}
7 \usepackage{amscd}
8 \usepackage[tableposition=top]{caption}
9 \usepackage{ifthen}
10 \usepackage{fullpage}
11 \usepackage[utf8]{inputenc}
12
13 \begin{document}
14 \setkeys{Gin}{width=0.9\textwidth}
15
16 \title{MAGeCK Comparison Report}
17 \author{Wei Li}
18
19 \maketitle
20
21
22 \tableofcontents
23
24 \section{Summary}
25
26 %Function definition
27 <<label=funcdef,include=FALSE,echo=FALSE>>=
28 genreporttable<-function(comparisons,ngenes,direction,fdr1,fdr5,fdr25){
29 xtb=data.frame(Comparison=comparisons,Genes=ngenes,Selection=direction,FDR1=fdr1,FDR5=fdr5,FDR25=fdr25);
30 colnames(xtb)=c("Comparison","Genes","Selection","FDR1%","FDR5%","FDR25%");
31 return (xtb);
32 }
33 @
34
35 The statistics of comparisons is as indicated in the following table.
36
37 <<label=tab1,echo=FALSE,results=tex>>=
38 library(xtable)
39 comparisons=c("HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial neg.","HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial pos.");
40 ngenes=c(100,100);
41 direction=c("negative","positive");
42 fdr1=c(0,0);
43 fdr5=c(2,0);
44 fdr25=c(9,1);
45
46 cptable=genreporttable(comparisons,ngenes,direction,fdr1,fdr5,fdr25);
47 print(xtable(cptable, caption = "Summary of comparisons", label = "tab:one",
48 digits = c(0, 0, 0, 0, 0, 0, 0),
49 table.placement = "tbp",
50 caption.placement = "top"))
51 @
52
53 The meanings of the columns are as follows.
54
55 \begin{itemize}
56 \item \textbf{Comparison}: The label for comparisons;
57 \item \textbf{Genes}: The number of genes in the library;
58 \item \textbf{Selection}: The direction of selection, either positive selection or negative selection;
59 \item \textbf{FDR1\%}: The number of genes with FDR $<$ 1\%;
60 \item \textbf{FDR5\%}: The number of genes with FDR $<$ 5\%;
61 \item \textbf{FDR25\%}: The number of genes with FDR $<$ 25\%;
62 \end{itemize}
63
64 The following figures show:
65
66 \begin{itemize}
67 \item Individual sgRNA read counts of selected genes in selected samples;
68 \item The distribution of RRA scores and p values of all genes; and
69 \item The RRA scores and p values of selected genes.
70 \end{itemize}
71
72
73 \newpage\section{Comparison results of HL60 final,KBM7 final vs HL60 initial,KBM7 initial neg.}
74
75 The following figure shows the distribution of RRA score in the comparison HL60 final,KBM7 final vs HL60 initial,KBM7 initial neg., and the RRA scores of 10 genes.
76
77 <<echo=FALSE>>=
78 gstable=read.table('output.gene_summary.txt',header=T)
79 @
80 %
81
82
83 <<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=#
84 #
85 # parameters
86 # Do not modify the variables beginning with "__"
87
88 # gstablename='__GENE_SUMMARY_FILE__'
89 startindex=3
90 # outputfile='__OUTPUT_FILE__'
91 targetgenelist=c("ACIN1","ACTR8","AHCY","ACLY","AATF","AGBL5","AHCTF1","ABT1","ADIRF","ABCF1")
92 # samplelabel=sub('.\w+.\w+$','',colnames(gstable)[startindex]);
93 samplelabel='HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial neg.'
94
95
96 # You need to write some codes in front of this code:
97 # gstable=read.table(gstablename,header=T)
98 # pdf(file=outputfile,width=6,height=6)
99
100
101 # set up color using RColorBrewer
102 #library(RColorBrewer)
103 #colors <- brewer.pal(length(targetgenelist), "Set1")
104
105 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
106 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
107 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
108 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
109
110 ######
111 # function definition
112
113 plotrankedvalues<-function(val, tglist, ...){
114
115 plot(val,log='y',ylim=c(max(val),min(val)),type='l',lwd=2, ...)
116 if(length(tglist)>0){
117 for(i in 1:length(tglist)){
118 targetgene=tglist[i];
119 tx=which(names(val)==targetgene);ty=val[targetgene];
120 points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20)
121 # text(tx+50,ty,targetgene,col=colors[i])
122 }
123 legend('topright',tglist,pch=20,pt.cex = 2,cex=1,col=colors)
124 }
125 }
126
127
128
129 plotrandvalues<-function(val,targetgenelist, ...){
130 # choose the one with the best distance distribution
131
132 mindiffvalue=0;
133 randval=val;
134 for(i in 1:20){
135 randval0=sample(val)
136 vindex=sort(which(names(randval0) %in% targetgenelist))
137 if(max(vindex)>0.9*length(val)){
138 # print('pass...')
139 next;
140 }
141 mindiffind=min(diff(vindex));
142 if (mindiffind > mindiffvalue){
143 mindiffvalue=mindiffind;
144 randval=randval0;
145 # print(paste('Diff: ',mindiffvalue))
146 }
147 }
148 plot(randval,log='y',ylim=c(max(randval),min(randval)),pch=20,col='grey', ...)
149
150 if(length(targetgenelist)>0){
151 for(i in 1:length(targetgenelist)){
152 targetgene=targetgenelist[i];
153 tx=which(names(randval)==targetgene);ty=randval[targetgene];
154 points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20)
155 text(tx+50,ty,targetgene,col=colors[i])
156 }
157 }
158
159 }
160
161
162
163
164 # set.seed(1235)
165
166
167
168 pvec=gstable[,startindex]
169 names(pvec)=gstable[,'id']
170 pvec=sort(pvec);
171
172 plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \n',samplelabel))
173
174 # plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \n',samplelabel))
175
176
177 pvec=gstable[,startindex+1]
178 names(pvec)=gstable[,'id']
179 pvec=sort(pvec);
180
181 plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \n',samplelabel))
182
183 # plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \n',samplelabel))
184
185
186
187 # you need to write after this code:
188 # dev.off()
189
190
191
192
193 @
194 %%
195 \clearpage
196 \newpage
197 The following figures show the distribution of sgRNA read counts (normalized) of selected genes in selected samples.
198 %
199
200
201 <<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=
202 par(mfrow=c(2,2));
203
204 # parameters
205 # Do not modify the variables beginning with "__"
206 targetmat=list(c(561.4907165816957,824.0396348113272,428.37415340969943,579.047491896501),c(3424.79939695118,3818.2871009576584,1992.3498917052,690.0506672205338),c(846.6456878299913,985.6508562937211,335.0024675413113,415.97581680707134),c(2432.636481525409,2122.257249136931,1067.465489792653,155.6333179800872),c(1308.1851773762019,2186.1913587343615,1482.5909580453515,997.3120339679854),c(405.68439208520414,268.16807081144486,170.34023773287015,109.85881269182627),c(640.8637498157573,559.4234589775174,711.6436598617687,632.2603542941043),c(946.5969148654764,470.6260845366416,663.0651476194316,457.74505288260946),c(246.9383256170808,177.59474888175154,28.39003962214503,0.0),c(568.8400715107754,612.7018836420428,564.0154538266146,270.64176251684285))
207 targetgene="ACIN1"
208 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
209
210 # set up color using RColorBrewer
211 #library(RColorBrewer)
212 #colors <- brewer.pal(length(targetgenelist), "Set1")
213
214 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
215 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
216 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
217 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
218
219
220 ## code
221
222 targetmatvec=unlist(targetmat)+1
223 yrange=range(targetmatvec[targetmatvec>0]);
224 # yrange[1]=1; # set the minimum value to 1
225 for(i in 1:length(targetmat)){
226 vali=targetmat[[i]]+1;
227 if(i==1){
228 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
229 axis(1,at=1:length(vali),labels=(collabel),las=2)
230 # lines(0:100,rep(1,101),col='black');
231 }else{
232 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
233 }
234 }
235
236
237
238 # parameters
239 # Do not modify the variables beginning with "__"
240 targetmat=list(c(2484.0819660289676,2349.578527705573,2172.7843657481662,910.9126552363929),c(992.1629154257711,1005.1862786707138,743.8190381001997,200.26346063614164),c(1267.0287897733551,1156.1418152202027,251.09412821363824,42.34141739164138),c(1500.738276518092,1315.977089213779,800.5991173444897,1476.2277955464156),c(1925.5309914189038,2054.7712445618654,194.94493873872918,235.16652091844063),c(351.29916561001374,781.4168950797068,227.75120674654121,624.2498158686586),c(1719.74905340467,1006.9622261595313,356.45271970026533,222.0063506480656),c(903.9706562768137,1445.6212558974576,1482.5909580453515,1055.1023468944147),c(651.152846716469,1081.552020689867,576.0023594448536,1072.2677863775127),c(285.1549712482957,408.46792242802854,99.0496937928171,44.630142656054424))
241 targetgene="ACTR8"
242 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
243
244 # set up color using RColorBrewer
245 #library(RColorBrewer)
246 #colors <- brewer.pal(length(targetgenelist), "Set1")
247
248 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
249 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
250 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
251 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
252
253
254 ## code
255
256 targetmatvec=unlist(targetmat)+1
257 yrange=range(targetmatvec[targetmatvec>0]);
258 # yrange[1]=1; # set the minimum value to 1
259 for(i in 1:length(targetmat)){
260 vali=targetmat[[i]]+1;
261 if(i==1){
262 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
263 axis(1,at=1:length(vali),labels=(collabel),las=2)
264 # lines(0:100,rep(1,101),col='black');
265 }else{
266 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
267 }
268 }
269
270
271
272 # parameters
273 # Do not modify the variables beginning with "__"
274 targetmat=list(c(301.3235520922712,657.1005708624807,228.38209651592223,137.32351586478285),c(1142.0897559789987,1099.311495578042,112.92926871919911,100.70391163417409),c(789.3207193831689,671.3081507730209,723.6305654800077,588.7745742702564),c(392.45555321286054,412.0198174056636,334.37157777193033,213.99581222261992),c(2009.3136376104133,2235.917888421252,2437.1271791188055,1937.9781176417478),c(1071.5359486598327,406.69197493921104,645.4002340767636,349.602784139093),c(61.7345814042702,218.44154112455442,614.4866353770946,452.5954210376801),c(651.152846716469,879.0940069646701,237.21455328725622,18.88198343140764),c(1625.6773103124485,1410.1023061211074,2146.286995434164,1986.613529510525),c(1053.8974968300413,882.6459019423052,106.6203710253891,105.85354347910344))
275 targetgene="AHCY"
276 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
277
278 # set up color using RColorBrewer
279 #library(RColorBrewer)
280 #colors <- brewer.pal(length(targetgenelist), "Set1")
281
282 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
283 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
284 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
285 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
286
287
288 ## code
289
290 targetmatvec=unlist(targetmat)+1
291 yrange=range(targetmatvec[targetmatvec>0]);
292 # yrange[1]=1; # set the minimum value to 1
293 for(i in 1:length(targetmat)){
294 vali=targetmat[[i]]+1;
295 if(i==1){
296 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
297 axis(1,at=1:length(vali),labels=(collabel),las=2)
298 # lines(0:100,rep(1,101),col='black');
299 }else{
300 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
301 }
302 }
303
304
305
306 # parameters
307 # Do not modify the variables beginning with "__"
308 targetmat=list(c(1268.498660759171,1411.8782536099247,1136.2324746551822,603.6512884889412),c(327.78122983695846,454.642557137284,51.73296108924205,24.031615276336996),c(132.28838872343613,241.5288584791821,123.02350502929512,65.80085135187511),c(495.34652221997754,586.0626713097802,279.4841678357833,243.74924065998954),c(1009.8013672555626,1102.8633905556771,1237.174837756142,1004.7503910773278),c(877.5129785321263,715.7068379934587,538.1489732819936,594.496387431289),c(1594.8100196103135,1108.1912330221296,605.6541786057605,127.59643349102738),c(314.5523909646148,252.1845434120872,88.95545748272109,359.9020478289517),c(512.984974049769,269.94401830026237,205.67006481820619,126.45207085882086),c(761.3931706526657,475.9539270030942,559.5992254409475,596.7851126957021))
309 targetgene="ACLY"
310 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
311
312 # set up color using RColorBrewer
313 #library(RColorBrewer)
314 #colors <- brewer.pal(length(targetgenelist), "Set1")
315
316 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
317 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
318 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
319 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
320
321
322 ## code
323
324 targetmatvec=unlist(targetmat)+1
325 yrange=range(targetmatvec[targetmatvec>0]);
326 # yrange[1]=1; # set the minimum value to 1
327 for(i in 1:length(targetmat)){
328 vali=targetmat[[i]]+1;
329 if(i==1){
330 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
331 axis(1,at=1:length(vali),labels=(collabel),las=2)
332 # lines(0:100,rep(1,101),col='black');
333 }else{
334 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
335 }
336 }
337
338
339
340 par(mfrow=c(1,1));
341 @
342 %
343
344
345 <<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=
346 par(mfrow=c(2,2));
347
348 # parameters
349 # Do not modify the variables beginning with "__"
350 targetmat=list(c(659.9720726313648,809.832054900787,880.7221180558769,802.1982051767731),c(724.6463960072668,1086.8798631563195,695.2405258578626,307.26136674745163),c(836.3565909292796,1289.3378768815162,468.75109865008346,177.94838930811443),c(367.46774645398926,571.85509139924,300.30353022535627,116.72498848506541),c(518.8644579930328,632.2373060190355,627.7353205340956,308.9779106957614),c(405.68439208520414,259.28833336735727,324.27734146183434,166.5047629860492),c(2096.0360257735547,1960.6460276545372,1573.4390848362154,629.9716290296913),c(277.8056163192159,435.1071347602913,182.32714335110919,0.0),c(995.1026573974029,477.7298744919117,728.0467938656747,275.21921304566894),c(2185.6981559083283,1482.9161531626255,1741.8866532609427,1862.4501839161173))
351 targetgene="AATF"
352 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
353
354 # set up color using RColorBrewer
355 #library(RColorBrewer)
356 #colors <- brewer.pal(length(targetgenelist), "Set1")
357
358 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
359 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
360 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
361 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
362
363
364 ## code
365
366 targetmatvec=unlist(targetmat)+1
367 yrange=range(targetmatvec[targetmatvec>0]);
368 # yrange[1]=1; # set the minimum value to 1
369 for(i in 1:length(targetmat)){
370 vali=targetmat[[i]]+1;
371 if(i==1){
372 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
373 axis(1,at=1:length(vali),labels=(collabel),las=2)
374 # lines(0:100,rep(1,101),col='black');
375 }else{
376 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
377 }
378 }
379
380
381
382 # parameters
383 # Do not modify the variables beginning with "__"
384 targetmat=list(c(640.8637498157573,602.0461987091378,307.2433176885473,192.82510352679924),c(354.23890758164566,280.5997032331675,204.4082852794442,275.79139436177223),c(779.0316224824572,932.3724316291956,778.5179754161547,905.1908420753603),c(624.6951689717818,554.0956165110648,370.96318439602834,558.4489645167836),c(1133.270530064103,1394.1187787217498,639.0913363829536,1131.2024619361487),c(423.32284391499564,412.0198174056636,224.59675789963623,426.84726181303336),c(296.91393913482335,829.3674772777797,489.5704610396565,1233.0507362025292),c(684.959879390236,546.9918265557948,394.30610586312537,566.4595029422292),c(440.96129574478715,630.461358530218,434.6830511035094,457.1728715665062),c(1108.2827233052317,1969.5257650986248,1066.2037102538911,1333.7546478367033))
385 targetgene="AGBL5"
386 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
387
388 # set up color using RColorBrewer
389 #library(RColorBrewer)
390 #colors <- brewer.pal(length(targetgenelist), "Set1")
391
392 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
393 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
394 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
395 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
396
397
398 ## code
399
400 targetmatvec=unlist(targetmat)+1
401 yrange=range(targetmatvec[targetmatvec>0]);
402 # yrange[1]=1; # set the minimum value to 1
403 for(i in 1:length(targetmat)){
404 vali=targetmat[[i]]+1;
405 if(i==1){
406 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
407 axis(1,at=1:length(vali),labels=(collabel),las=2)
408 # lines(0:100,rep(1,101),col='black');
409 }else{
410 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
411 }
412 }
413
414
415
416 # parameters
417 # Do not modify the variables beginning with "__"
418 targetmat=list(c(196.96271209933826,301.9110730989776,423.9579250240324,34.33087896619571),c(1106.8128523194157,1056.6887558464218,1743.1484327997048,807.3478370217025),c(748.1643317803222,488.3855594248168,239.73811236478022,477.77139894622366),c(1095.053884432888,882.6459019423052,837.8216137379688,365.05167967388104),c(677.6105244611563,316.11865300951774,613.8557456077136,819.3636446598709),c(1078.8853035889126,1609.008424868669,348.88204246769334,193.96946615900578),c(1437.533824128006,1095.759600600407,320.4920028455483,161.35513114111984),c(845.1758168441753,660.6524658401157,541.3034221288985,640.8430740356532),c(551.2016196809839,740.570102836904,1103.42620664737,622.5332719203489),c(601.1772331987264,900.4053768304803,735.6174710982467,754.1349746240991))
419 targetgene="AHCTF1"
420 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
421
422 # set up color using RColorBrewer
423 #library(RColorBrewer)
424 #colors <- brewer.pal(length(targetgenelist), "Set1")
425
426 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
427 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
428 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
429 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
430
431
432 ## code
433
434 targetmatvec=unlist(targetmat)+1
435 yrange=range(targetmatvec[targetmatvec>0]);
436 # yrange[1]=1; # set the minimum value to 1
437 for(i in 1:length(targetmat)){
438 vali=targetmat[[i]]+1;
439 if(i==1){
440 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
441 axis(1,at=1:length(vali),labels=(collabel),las=2)
442 # lines(0:100,rep(1,101),col='black');
443 }else{
444 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
445 }
446 }
447
448
449
450 # parameters
451 # Do not modify the variables beginning with "__"
452 targetmat=list(c(487.9971672908978,367.6211301852257,312.2904358435953,441.15179471561487),c(358.6485205390935,394.2603425174884,593.0363832181406,268.35303725242983),c(1743.266989177725,1980.1814500315297,837.1907239685878,281.5132075228048),c(1597.7497615819454,1465.1566782744503,1065.57282048451,992.7345834391593),c(119.05954985109253,378.2768151181308,185.48159219801417,128.7407961232339),c(986.2834314825072,745.8979453033566,328.0626800781203,302.11173490252224),c(523.2740709504807,694.3954681276485,336.89513684945433,597.9294753279087),c(1562.4728579223624,763.6574201915316,422.0652557158894,220.28980669975581),c(30.8672907021351,179.37069637056908,238.47633282601822,184.81456510135357),c(339.5401977234861,447.5387671820139,310.3977665354523,205.98527379717427))
453 targetgene="ABT1"
454 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
455
456 # set up color using RColorBrewer
457 #library(RColorBrewer)
458 #colors <- brewer.pal(length(targetgenelist), "Set1")
459
460 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
461 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
462 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
463 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
464
465
466 ## code
467
468 targetmatvec=unlist(targetmat)+1
469 yrange=range(targetmatvec[targetmatvec>0]);
470 # yrange[1]=1; # set the minimum value to 1
471 for(i in 1:length(targetmat)){
472 vali=targetmat[[i]]+1;
473 if(i==1){
474 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
475 axis(1,at=1:length(vali),labels=(collabel),las=2)
476 # lines(0:100,rep(1,101),col='black');
477 }else{
478 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
479 }
480 }
481
482
483
484 par(mfrow=c(1,1));
485 @
486 %
487
488
489 <<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=
490 par(mfrow=c(2,2));
491
492 # parameters
493 # Do not modify the variables beginning with "__"
494 targetmat=list(c(492.4067802483456,221.99343610218943,309.7668767660713,102.99263689858714),c(243.9985836454489,239.7529109903646,130.59418226186713,174.51530141149487),c(734.9354929079785,673.0840982618383,620.7955330709046,470.9052231529845),c(1074.4756906314647,950.1319065173708,1100.902647569846,743.8357109342404),c(702.5983312200275,1010.5141211371663,1291.4313579229083,1017.3383800315995),c(1647.7253750996879,760.1055252138966,685.7771793171477,608.2287390177673),c(951.0065278229242,864.8864270541301,606.9159581445226,769.0116888427839),c(435.0818118015233,435.1071347602913,275.69882921949727,339.8757017653375),c(89.66213013477338,209.56180368046682,208.8245136651112,304.4004601669353),c(1328.7633711776252,1571.7135276035012,1122.983789498181,1356.6419004808338))
495 targetgene="ADIRF"
496 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
497
498 # set up color using RColorBrewer
499 #library(RColorBrewer)
500 #colors <- brewer.pal(length(targetgenelist), "Set1")
501
502 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
503 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
504 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
505 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
506
507
508 ## code
509
510 targetmatvec=unlist(targetmat)+1
511 yrange=range(targetmatvec[targetmatvec>0]);
512 # yrange[1]=1; # set the minimum value to 1
513 for(i in 1:length(targetmat)){
514 vali=targetmat[[i]]+1;
515 if(i==1){
516 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
517 axis(1,at=1:length(vali),labels=(collabel),las=2)
518 # lines(0:100,rep(1,101),col='black');
519 }else{
520 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
521 }
522 }
523
524
525
526 # parameters
527 # Do not modify the variables beginning with "__"
528 targetmat=list(c(216.0710349149457,289.479440677255,192.42137966120518,498.36992632594104),c(1127.391046120839,1198.764554951823,371.5940741654094,370.2013115188104),c(1111.2224652768637,1038.9292809582466,948.227323379644,922.3562815584581),c(1164.137820766238,1204.0923974182756,1686.9992433247955,2089.033985093009),c(48.505742531926586,248.63264843445216,665.5887066969557,248.8988725049189),c(501.2260061632414,387.1565525622184,436.5757204116524,314.69972385679404),c(1975.5066049366465,1797.2588586833258,1628.3264947723626,1289.6966864967521),c(213.13129294331378,376.5008676293133,404.4003421732214,482.921030791153),c(2012.2533795820452,1989.0611874756173,1064.3110409457481,431.9968936579627),c(264.57677744687226,353.4135502746856,442.25372833608145,191.6807408945927))
529 targetgene="ABCF1"
530 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
531
532 # set up color using RColorBrewer
533 #library(RColorBrewer)
534 #colors <- brewer.pal(length(targetgenelist), "Set1")
535
536 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
537 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
538 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
539 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
540
541
542 ## code
543
544 targetmatvec=unlist(targetmat)+1
545 yrange=range(targetmatvec[targetmatvec>0]);
546 # yrange[1]=1; # set the minimum value to 1
547 for(i in 1:length(targetmat)){
548 vali=targetmat[[i]]+1;
549 if(i==1){
550 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
551 axis(1,at=1:length(vali),labels=(collabel),las=2)
552 # lines(0:100,rep(1,101),col='black');
553 }else{
554 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
555 }
556 }
557
558
559
560 par(mfrow=c(1,1));
561 @
562
563 \newpage\section{Comparison results of HL60 final,KBM7 final vs HL60 initial,KBM7 initial pos.}
564
565 The following figure shows the distribution of RRA score in the comparison HL60 final,KBM7 final vs HL60 initial,KBM7 initial pos., and the RRA scores of 10 genes.
566
567 <<echo=FALSE>>=
568 gstable=read.table('output.gene_summary.txt',header=T)
569 @
570 %
571
572
573 <<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=#
574 #
575 # parameters
576 # Do not modify the variables beginning with "__"
577
578 # gstablename='__GENE_SUMMARY_FILE__'
579 startindex=9
580 # outputfile='__OUTPUT_FILE__'
581 targetgenelist=c("ACRC","AGAP3","ADCK4","AHRR","ADRBK1","ADK","ADCK1","ADARB2","ACSS2","ADNP")
582 # samplelabel=sub('.\w+.\w+$','',colnames(gstable)[startindex]);
583 samplelabel='HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial pos.'
584
585
586 # You need to write some codes in front of this code:
587 # gstable=read.table(gstablename,header=T)
588 # pdf(file=outputfile,width=6,height=6)
589
590
591 # set up color using RColorBrewer
592 #library(RColorBrewer)
593 #colors <- brewer.pal(length(targetgenelist), "Set1")
594
595 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
596 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
597 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
598 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
599
600 ######
601 # function definition
602
603 plotrankedvalues<-function(val, tglist, ...){
604
605 plot(val,log='y',ylim=c(max(val),min(val)),type='l',lwd=2, ...)
606 if(length(tglist)>0){
607 for(i in 1:length(tglist)){
608 targetgene=tglist[i];
609 tx=which(names(val)==targetgene);ty=val[targetgene];
610 points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20)
611 # text(tx+50,ty,targetgene,col=colors[i])
612 }
613 legend('topright',tglist,pch=20,pt.cex = 2,cex=1,col=colors)
614 }
615 }
616
617
618
619 plotrandvalues<-function(val,targetgenelist, ...){
620 # choose the one with the best distance distribution
621
622 mindiffvalue=0;
623 randval=val;
624 for(i in 1:20){
625 randval0=sample(val)
626 vindex=sort(which(names(randval0) %in% targetgenelist))
627 if(max(vindex)>0.9*length(val)){
628 # print('pass...')
629 next;
630 }
631 mindiffind=min(diff(vindex));
632 if (mindiffind > mindiffvalue){
633 mindiffvalue=mindiffind;
634 randval=randval0;
635 # print(paste('Diff: ',mindiffvalue))
636 }
637 }
638 plot(randval,log='y',ylim=c(max(randval),min(randval)),pch=20,col='grey', ...)
639
640 if(length(targetgenelist)>0){
641 for(i in 1:length(targetgenelist)){
642 targetgene=targetgenelist[i];
643 tx=which(names(randval)==targetgene);ty=randval[targetgene];
644 points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20)
645 text(tx+50,ty,targetgene,col=colors[i])
646 }
647 }
648
649 }
650
651
652
653
654 # set.seed(1235)
655
656
657
658 pvec=gstable[,startindex]
659 names(pvec)=gstable[,'id']
660 pvec=sort(pvec);
661
662 plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \n',samplelabel))
663
664 # plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \n',samplelabel))
665
666
667 pvec=gstable[,startindex+1]
668 names(pvec)=gstable[,'id']
669 pvec=sort(pvec);
670
671 plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \n',samplelabel))
672
673 # plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \n',samplelabel))
674
675
676
677 # you need to write after this code:
678 # dev.off()
679
680
681
682
683 @
684 %%
685 \clearpage
686 \newpage
687 The following figures show the distribution of sgRNA read counts (normalized) of selected genes in selected samples.
688 %
689
690
691 <<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=
692 par(mfrow=c(2,2));
693
694 # parameters
695 # Do not modify the variables beginning with "__"
696 targetmat=list(c(461.5394895462105,502.5931393353569,445.40817718298644,889.1697652244688),c(76.43329126242978,90.5733219296933,447.30084649112945,357.0411412484354),c(258.6972935036084,685.515730683561,533.7327448963265,560.7376897811967),c(232.23961575892122,681.9638357059259,275.69882921949727,467.47213525636494),c(1393.4376945535273,1472.2604682297203,1039.706339939889,532.7008052921368),c(2395.88970688001,2441.927797124084,2462.9936596634266,2461.5240218762324),c(495.34652221997754,605.5980936867728,1159.575396122279,1617.5565806239213),c(682.0201374186041,822.2636873225097,1572.1773052974536,1333.7546478367033),c(961.2956247236359,1097.5355480892247,959.5833392285019,905.1908420753603),c(1940.2297012770634,1289.3378768815162,942.5493154552149,1103.737758763192))
697 targetgene="ACRC"
698 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
699
700 # set up color using RColorBrewer
701 #library(RColorBrewer)
702 #colors <- brewer.pal(length(targetgenelist), "Set1")
703
704 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
705 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
706 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
707 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
708
709
710 ## code
711
712 targetmatvec=unlist(targetmat)+1
713 yrange=range(targetmatvec[targetmatvec>0]);
714 # yrange[1]=1; # set the minimum value to 1
715 for(i in 1:length(targetmat)){
716 vali=targetmat[[i]]+1;
717 if(i==1){
718 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
719 axis(1,at=1:length(vali),labels=(collabel),las=2)
720 # lines(0:100,rep(1,101),col='black');
721 }else{
722 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
723 }
724 }
725
726
727
728 # parameters
729 # Do not modify the variables beginning with "__"
730 targetmat=list(c(1387.5582106102636,1120.6228654438523,1214.4628060584262,1111.1761158725344),c(388.0459402554127,509.69692929062694,933.0859689144999,750.1297054113762),c(326.3113588511425,635.7892009966705,960.8451187672639,615.6670961271097),c(1328.7633711776252,1038.9292809582466,1346.3187678590552,1596.3858719281006),c(352.7690365958297,234.42506852391205,310.3977665354523,429.1359870774464),c(693.7791053051318,678.4119407282909,784.1959833405838,895.4637597016048),c(837.8264619150956,719.2587329710938,374.74852301231437,993.8789460713658),c(365.99787546817333,369.3970776740432,333.74068800254935,746.6966175147567),c(707.0079441774753,635.7892009966705,837.1907239685878,1465.3563505404536),c(486.5272963050818,673.0840982618383,784.8268731099647,734.6808098765882))
731 targetgene="AGAP3"
732 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
733
734 # set up color using RColorBrewer
735 #library(RColorBrewer)
736 #colors <- brewer.pal(length(targetgenelist), "Set1")
737
738 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
739 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
740 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
741 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
742
743
744 ## code
745
746 targetmatvec=unlist(targetmat)+1
747 yrange=range(targetmatvec[targetmatvec>0]);
748 # yrange[1]=1; # set the minimum value to 1
749 for(i in 1:length(targetmat)){
750 vali=targetmat[[i]]+1;
751 if(i==1){
752 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
753 axis(1,at=1:length(vali),labels=(collabel),las=2)
754 # lines(0:100,rep(1,101),col='black');
755 }else{
756 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
757 }
758 }
759
760
761
762 # parameters
763 # Do not modify the variables beginning with "__"
764 targetmat=list(c(830.4771069860158,864.8864270541301,1349.4732167059603,740.974804353724),c(1481.6299537024847,1994.38902994207,2044.082852794442,1810.9538654668238),c(1234.6916280854039,1299.9935618144214,1357.6747837079133,2232.6514954349277),c(224.89026082984142,188.25043381465665,700.2876440129107,81.24974688666317),c(812.8386551562243,845.3510046771374,946.334654071501,999.6007592323984),c(1978.4463469082782,1751.0842239740703,2659.2003779409174,2851.1794981425537),c(565.9003295391435,776.0890526132542,878.1985589783528,445.72924524444096),c(680.5502664327881,534.5601941340722,550.7667686696135,1025.9210997731484),c(161.68580843975528,333.87812789769293,275.0679394501163,465.18340999195186),c(2523.768482645998,2445.4796921017187,2153.226782897355,1516.8526689897471))
765 targetgene="ADCK4"
766 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
767
768 # set up color using RColorBrewer
769 #library(RColorBrewer)
770 #colors <- brewer.pal(length(targetgenelist), "Set1")
771
772 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
773 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
774 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
775 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
776
777
778 ## code
779
780 targetmatvec=unlist(targetmat)+1
781 yrange=range(targetmatvec[targetmatvec>0]);
782 # yrange[1]=1; # set the minimum value to 1
783 for(i in 1:length(targetmat)){
784 vali=targetmat[[i]]+1;
785 if(i==1){
786 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
787 axis(1,at=1:length(vali),labels=(collabel),las=2)
788 # lines(0:100,rep(1,101),col='black');
789 }else{
790 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
791 }
792 }
793
794
795
796 # parameters
797 # Do not modify the variables beginning with "__"
798 targetmat=list(c(345.4196816667499,163.38716897121142,474.42910657451245,481.2044868428432),c(415.9734889859159,372.9489726516783,212.6098522813972,349.03060282298975),c(1.469870985815957,83.46953197442323,0.0,62.9399447713588),c(351.29916561001374,150.9555365494888,288.9475143764983,416.54799812317464),c(561.4907165816957,170.49095892648148,199.3611671243962,411.97054759434855),c(251.34793857452865,221.99343610218943,1564.6066280648815,1502.5481360871656),c(736.4053638937945,893.3015868752103,1114.782222496228,459.46159683091923),c(338.07032673767014,607.3740411755903,378.5338616286004,65.22867003577186),c(1230.2820151279561,525.6804566899846,837.1907239685878,945.2435342025885))
799 targetgene="AHRR"
800 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
801
802 # set up color using RColorBrewer
803 #library(RColorBrewer)
804 #colors <- brewer.pal(length(targetgenelist), "Set1")
805
806 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
807 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
808 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
809 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
810
811
812 ## code
813
814 targetmatvec=unlist(targetmat)+1
815 yrange=range(targetmatvec[targetmatvec>0]);
816 # yrange[1]=1; # set the minimum value to 1
817 for(i in 1:length(targetmat)){
818 vali=targetmat[[i]]+1;
819 if(i==1){
820 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
821 axis(1,at=1:length(vali),labels=(collabel),las=2)
822 # lines(0:100,rep(1,101),col='black');
823 }else{
824 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
825 }
826 }
827
828
829
830 par(mfrow=c(1,1));
831 @
832 %
833
834
835 <<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=
836 par(mfrow=c(2,2));
837
838 # parameters
839 # Do not modify the variables beginning with "__"
840 targetmat=list(c(371.87735941143717,877.3180594758527,2395.4884543396593,1564.9158995424211),c(1109.7525942910477,1138.3823403320275,970.308465307979,999.0285779162951),c(1462.5216308868773,1209.420239884728,1537.4783679814984,1519.14139425416),c(586.4785233405669,987.4268037825386,743.8190381001997,1312.0117578247794),c(1018.6205931704583,717.4827854822763,1070.619938639558,1144.3626322065236),c(1269.9685317449869,1212.9721348623632,1591.1039983788835,1624.9949377332637),c(1321.4140162485455,1795.4829111945082,1478.8056194290655,1237.056005415252),c(908.3802692342615,832.9193722554148,1639.6825106212207,1268.5259778009315),c(923.078979092421,758.3295777250792,1479.4365091984464,1275.964334910274),c(680.5502664327881,634.013253507853,318.5993335374053,631.1159916618979))
841 targetgene="ADRBK1"
842 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
843
844 # set up color using RColorBrewer
845 #library(RColorBrewer)
846 #colors <- brewer.pal(length(targetgenelist), "Set1")
847
848 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
849 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
850 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
851 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
852
853
854 ## code
855
856 targetmatvec=unlist(targetmat)+1
857 yrange=range(targetmatvec[targetmatvec>0]);
858 # yrange[1]=1; # set the minimum value to 1
859 for(i in 1:length(targetmat)){
860 vali=targetmat[[i]]+1;
861 if(i==1){
862 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
863 axis(1,at=1:length(vali),labels=(collabel),las=2)
864 # lines(0:100,rep(1,101),col='black');
865 }else{
866 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
867 }
868 }
869
870
871
872 # parameters
873 # Do not modify the variables beginning with "__"
874 targetmat=list(c(1472.810727787589,1829.225913482041,1263.0413183007631,1315.444845721399),c(208.7216799858659,65.71005708624807,292.1019632234033,350.17496545519623),c(1011.2712382413785,1166.7975001531076,652.9709113093356,860.5606994193058),c(557.0811036242477,685.515730683561,875.0441101314478,1019.6271052960126),c(363.0581334965414,825.8155823001447,736.8792506370087,349.602784139093),c(1505.14788947554,451.09066215964896,653.6018010787167,991.0180394908496),c(198.43258308515422,28.41515982108025,249.83234867487624,114.43626322065236),c(438.02155377315523,74.58979453033565,254.87946682992424,231.16125170571777),c(804.0194292413286,472.4020320254591,1336.2245315489592,1203.2973077651598),c(454.19013461713075,490.1615069136343,896.4943622904019,685.4732166917076))
875 targetgene="ADK"
876 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
877
878 # set up color using RColorBrewer
879 #library(RColorBrewer)
880 #colors <- brewer.pal(length(targetgenelist), "Set1")
881
882 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
883 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
884 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
885 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
886
887
888 ## code
889
890 targetmatvec=unlist(targetmat)+1
891 yrange=range(targetmatvec[targetmatvec>0]);
892 # yrange[1]=1; # set the minimum value to 1
893 for(i in 1:length(targetmat)){
894 vali=targetmat[[i]]+1;
895 if(i==1){
896 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
897 axis(1,at=1:length(vali),labels=(collabel),las=2)
898 # lines(0:100,rep(1,101),col='black');
899 }else{
900 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
901 }
902 }
903
904
905
906 # parameters
907 # Do not modify the variables beginning with "__"
908 targetmat=list(c(662.9118146029966,1008.7381736483488,1101.533537339227,1694.8010582978616),c(1547.7741480642028,1965.9738701209897,1869.9572764452857,2353.9539344488194),c(1459.5818889152454,1179.2291325748304,1296.4784760779562,1222.1792911965672),c(1193.5352404825571,1355.0479339677643,1622.0175970785526,1905.9359639399652),c(868.6937526172306,701.4992580829187,720.4761166331027,603.6512884889412),c(798.1399452980647,768.9852626579842,1478.8056194290655,1756.0244591209105),c(1168.5474337236858,907.5091667857504,879.4603385171149,977.8578692204745),c(809.8989131845924,687.2916781723785,678.8373918539567,865.7103312642352),c(1246.4505959719315,753.0017352586266,1301.5255942330043,1264.5207085882087),c(826.0674940285679,797.4004224790644,977.8791425405509,2066.7189137649816))
909 targetgene="ADCK1"
910 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
911
912 # set up color using RColorBrewer
913 #library(RColorBrewer)
914 #colors <- brewer.pal(length(targetgenelist), "Set1")
915
916 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
917 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
918 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
919 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
920
921
922 ## code
923
924 targetmatvec=unlist(targetmat)+1
925 yrange=range(targetmatvec[targetmatvec>0]);
926 # yrange[1]=1; # set the minimum value to 1
927 for(i in 1:length(targetmat)){
928 vali=targetmat[[i]]+1;
929 if(i==1){
930 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
931 axis(1,at=1:length(vali),labels=(collabel),las=2)
932 # lines(0:100,rep(1,101),col='black');
933 }else{
934 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
935 }
936 }
937
938
939
940 # parameters
941 # Do not modify the variables beginning with "__"
942 targetmat=list(c(1863.7964100146337,1585.9211075140413,1761.4442361117538,1464.211987908247),c(742.2848478370584,598.4943037315028,943.8110949939769,820.5080072920774),c(1568.3523418656262,2083.1864043829455,1810.6536381234716,1887.6261618246608),c(1018.6205931704583,513.248824268262,679.4682816233377,824.5132765048003),c(1140.6198849931827,1191.6607649965529,880.0912282864958,977.8578692204745),c(135.22813069506805,118.98848175077354,351.40560154521734,399.95473995618005),c(665.8515565746286,701.4992580829187,986.7115993118849,746.6966175147567),c(418.9132309575478,300.1351256101601,376.6411923204574,645.4205245644794),c(561.4907165816957,543.4399315781598,881.9838975946388,580.7640358448108),c(442.4311667306031,229.0972260574595,395.5678854018874,651.142337725512))
943 targetgene="ADARB2"
944 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
945
946 # set up color using RColorBrewer
947 #library(RColorBrewer)
948 #colors <- brewer.pal(length(targetgenelist), "Set1")
949
950 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
951 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
952 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
953 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
954
955
956 ## code
957
958 targetmatvec=unlist(targetmat)+1
959 yrange=range(targetmatvec[targetmatvec>0]);
960 # yrange[1]=1; # set the minimum value to 1
961 for(i in 1:length(targetmat)){
962 vali=targetmat[[i]]+1;
963 if(i==1){
964 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
965 axis(1,at=1:length(vali),labels=(collabel),las=2)
966 # lines(0:100,rep(1,101),col='black');
967 }else{
968 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
969 }
970 }
971
972
973
974 par(mfrow=c(1,1));
975 @
976 %
977
978
979 <<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=
980 par(mfrow=c(2,2));
981
982 # parameters
983 # Do not modify the variables beginning with "__"
984 targetmat=list(c(734.9354929079785,358.74139274113816,541.9343118982795,378.7840312603593),c(595.2977492554626,591.3905137762326,1061.787481868224,887.4532212761591),c(1655.0747300287676,943.0281165621008,1069.358159100796,2038.1098479598186),c(626.1650399575977,884.4218494311227,517.3296108924205,858.2719741548927),c(680.5502664327881,747.673892792174,533.1018551269456,1016.194017399393),c(662.9118146029966,777.8650001020718,864.9498738213518,787.3214909580882),c(880.4527205037583,621.5816210861304,671.8976043907657,1040.7978139918332),c(94.07174309222125,447.5387671820139,711.6436598617687,927.5059134033875),c(399.80490814194036,806.280159923152,1147.58849050404,1059.1076161071376),c(698.1887182625796,531.0082991564371,504.0809257354195,347.8862401907832))
985 targetgene="ACSS2"
986 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
987
988 # set up color using RColorBrewer
989 #library(RColorBrewer)
990 #colors <- brewer.pal(length(targetgenelist), "Set1")
991
992 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
993 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
994 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
995 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
996
997
998 ## code
999
1000 targetmatvec=unlist(targetmat)+1
1001 yrange=range(targetmatvec[targetmatvec>0]);
1002 # yrange[1]=1; # set the minimum value to 1
1003 for(i in 1:length(targetmat)){
1004 vali=targetmat[[i]]+1;
1005 if(i==1){
1006 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
1007 axis(1,at=1:length(vali),labels=(collabel),las=2)
1008 # lines(0:100,rep(1,101),col='black');
1009 }else{
1010 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
1011 }
1012 }
1013
1014
1015
1016 # parameters
1017 # Do not modify the variables beginning with "__"
1018 targetmat=list(c(408.62413405683606,523.9045092011671,483.89245311522745,701.494293542599),c(1805.0015705819953,1434.9655709645526,1712.2348341000356,2152.546111180471),c(3017.64513388016,2642.609863360463,1834.6274493599499,3573.2723190648703),c(1649.1952460855039,783.1928425685244,773.4708572611067,1332.0381038883936),c(959.82575373782,1397.6706736993847,1429.5962174173474,2811.126806015325),c(495.34652221997754,301.9110730989776,336.89513684945433,555.015876620164),c(1491.9190506031964,1331.9606166131366,2087.614246881731,1983.1804416139055),c(429.2023278582595,889.7496918975753,567.8007924429005,1132.9190058844583),c(427.7324568724435,573.6310388880576,655.4944703868597,899.4690289143276),c(690.8393633334998,767.2093151691668,1040.33722970927,993.3067647552625))
1019 targetgene="ADNP"
1020 collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
1021
1022 # set up color using RColorBrewer
1023 #library(RColorBrewer)
1024 #colors <- brewer.pal(length(targetgenelist), "Set1")
1025
1026 colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",
1027 "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3",
1028 "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
1029 "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
1030
1031
1032 ## code
1033
1034 targetmatvec=unlist(targetmat)+1
1035 yrange=range(targetmatvec[targetmatvec>0]);
1036 # yrange[1]=1; # set the minimum value to 1
1037 for(i in 1:length(targetmat)){
1038 vali=targetmat[[i]]+1;
1039 if(i==1){
1040 plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
1041 axis(1,at=1:length(vali),labels=(collabel),las=2)
1042 # lines(0:100,rep(1,101),col='black');
1043 }else{
1044 lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
1045 }
1046 }
1047
1048
1049
1050 par(mfrow=c(1,1));
1051 @
1052 %__INDIVIDUAL_PAGE__
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062 \end{document}
1063