## ---- Framework_figure，echo = false，figncap ='GovencyTo Gating框架是R / Biocumondlase的集合，用于容易地构建可再现的流量数据分析管道。'，消息= false，fig.Align ='Center'，fig.width = 8，缓存= true ---- require（png）需要（grid）img <-readpng（“资源/ framework.png”）grid.raster（img）## ---- eval =错误的 - - - - - - - - - - - - - - - - - - - - - - - - ---------- ##要求（Biocinstaller）## Bioclite（“Apencyto”）## ---- eval = False ---------------------------------------------------- ##要求（devtools）##包-c（“rglab / flowstats”，“rglab / flowcore”，“rglab / flowviz”，“rglab / ncdfflow”，“rglab / flowworkspace”，“rglab / flowworkspace”，“rglab / apencyto”）## install_github（包，quick = true）＃＃---- libs，echo = false，message = false，警告= false -----------------------索引（Apenceto）要求（数据.Table）要求（Reshape2）要求（Clue）要求（GGPLot2）## ----回声= 1，结果='标记'，结果='隐藏'------------------------------ WS <-OPENWORKSPACE（“数据/工作空间/ 080批量0882.xml“）ws ## ---- parseworkssspace_echo，echo = true，eval = false -------------------------- ## goting_set<-parseWorkspace（WS，Name =“0882样本”，Path =“Data / FCS /”，ISNCDF = True）## ---- ParseWorkspace，Echo = False，结果=“隐藏”，eval = True ---------------- - 如果（！file.exists（“data / manual_gating /”）））{gating_set <-parseworkspace（ws，name =“0882样本”，path =“数据/fcs /“，isncdf = true，overwrite = true）save_gs（gating_set，path =”data / manual_gating“）} else {gating_set <-load_gs（”data / manual_gating /“）} ## ---- eval = false，echo = true，结果='hide'------------------------------ ## save_gs（gating_set，path =“数据/手动”）## ---- Clean_Manual，Echo = False，结果=“隐藏”，警告= False，Message = False ---- RM（“不是4 +”，Gating_set）##-vis_manual_gates，echo = true，eval = true，结果='hide'，cache = false，message = false，warning = false，fign.align ='center'，fig.cap ='手动门的布局' -- 绘图（Gating_set [[1]]，xbin = 16，gpar = list（ncol = 5））＃binning for更快的绘图## ---- plot_manual_tree，echo = 2，结果=“隐藏”，消息= false，警告= false，fig.cap =''----绘图（gating_set）## ----关键字，回声= c（2,3,4,8,9,10），eval = true ------------------------------ GetKeywords <-Function（GS，KV）{R <-_AS.DATA.FRAME（DO.CALL（Cbind，Lapply（kV，函数（k）{关键字（gs，k）[1]}）））data.table :: setnames（r，r，'$ fil“，”名称“）r} wesponse_vars <-c（”$FIL","Stim","Sample Order","EXPERIMENT NAME") #relevant keywords pd<-data.table(getKeywords(gating_set,keyword_vars)) #extract relevant keywords to a data table annotations<-data.table:::fread("data/workspace/pd_submit.csv") # read the annotations from flowrepository setnames(annotations,"File Name","name") setkey(annotations,"name") setkey(pd,"name") pd<-data.frame(annotations[pd]) #data.table style merge setnames(pd,c("Timepoint","Individual"),c("VISITNO","PTID")) pData(gating_set)<-pd #annotate gating_subset<-gating_set[subset(pd,!is.na(Condition))$name] #subset only the GAG and negctrl ## ----keywords_tbl,eval=TRUE,echo=FALSE,results='asis',cache=FALSE-------- knitr::kable(head(subset(na.omit(pd)[,c(1:5)],PTID%in%"080-17"),4),row.names = FALSE) ## ----copy_and_save,eval=FALSE,message=FALSE,warining=FALSE,results='hide'---- ## auto_gating<-clone(gating_subset) ## Rm("S",auto_gating) ## save_gs(auto_gating,path="data/autogating",overwrite=TRUE) ## ----load_empty,eval=TRUE,echo=FALSE,results='hide',message=FALSE,warning=FALSE---- if(!file.exists("data/autogating")){ auto_gating<-clone(gating_subset) try(Rm("S",auto_gating)) save_gs(auto_gating,path="data/autogating") }else{ auto_gating<-load_gs("data/autogating/") } ## ----echo=TRUE,eval=TRUE------------------------------------------------- list.files("data/autogating") ## ----read_template,echo=FALSE,results='asis',cache=FALSE----------------- template<-read.csv("data//template//gt_080.csv",as.is=FALSE,allowEscapes=TRUE,stringsAsFactors=FALSE) template$collapseDataForGating[is.na(template$collapseDataForGating)]<-" " Kmisc::htmlTable(data.frame(template)[1:8,c(1:6,8,9)],attr="width=100%") ## ----read_plot_template,echo=TRUE,message=FALSE,warning=FALSE,results='hide',fig.align="center",fig.width=8,fig.height=5,out.width=600,dpi=200,fig.cap=''---- gt<-gatingTemplate("data/template/gt_080.csv") plot(gt) ## ----gate_subset,echo=TRUE,message=FALSE,warning=FALSE,results='hide',eval=FALSE,fig.cap=''---- ## gating(x = gt, y = auto_gating) ## ## Some output.. ## plot(auto_gating) ## ----plot_autogate_tree,echo=FALSE,message=FALSE,warning=FALSE,fig.align='center',out.width=600,results='hide'---- if(length(getNodes(auto_gating))<1){ gating(x = gt, y = auto_gating) save_gs(auto_gating,path="data//autogating",overwrite = TRUE) } setNode(auto_gating,"nonNeutro",FALSE) setNode(auto_gating,"DebrisGate",FALSE) setNode(auto_gating,"cd4/cd57-/gzb_gate",FALSE) setNode(auto_gating,"cd4/cd57-/prf_gate",FALSE) setNode(auto_gating,"cd8/cd57-/gzb_gate",FALSE) setNode(auto_gating,"cd8/cd57-",FALSE) setNode(auto_gating,"cd4/cd57-",FALSE) setNode(auto_gating,"cd8/cd57-/prf_gate",FALSE) setNode(auto_gating,"cd4neg",FALSE) setNode(auto_gating,"cd4pos",FALSE) setNode(auto_gating,"cd8+",FALSE) setNode(auto_gating,"cd8-",FALSE) plot(auto_gating) ## ----compare_to_manual,echo=TRUE,fig.cap='Automated and Manual Gates for CD4/CD8',cache=FALSE,fig.align='center',fig.width=5,fig.height=2.5,out.width=600---- p1<-plotGate(auto_gating[[1]],c("cd8","cd4"),arrange=FALSE, projections=list("cd4"=c(x="CD8",y="CD4"),"cd8"=c(x="CD8",y="CD4")), main="Automated Gate",path=2)[[1]] p2<-plotGate(gating_subset[[1]],c("8+","4+"), projections=list("4+"=c(x="CD8",y="CD4"),"8+"=c(x="CD8",y="CD4")), arrange=FALSE,main="Manual Gate",path=2)[[1]] grid.arrange(arrangeGrob(p1,p2,ncol=2)) ## ----extract_stats,echo=FALSE,message=FALSE, warning=FALSE,error=FALSE,results='hide',fig.cap='Comparison of Manual vs Automated Gating Cell Subset Counts',fig.width=12,fig.height=5,cache=FALSE,fig.align='center',out.width=800---- auto_stats<-getPopStats(auto_gating,statistic="count") manual_stats<-getPopStats(gating_subset,statistic="count") gates_to_remove<-rownames(manual_stats)[which(sapply(basename(rownames(manual_stats)),function(x)length(which(strsplit(x,"")[[1]]=="+"))>1))] gates_to_remove<-c(gates_to_remove,rownames(manual_stats)[which(sapply(basename(rownames(manual_stats)),function(x)length(which(strsplit(x,"")[[1]]=="-"))>1))],"/S/Lv/L/Not 4+","/S/Lv/L/3+/8+/Granzyme B-","/S/Lv/L/3+/8+/IFN\\IL2","/S/Lv/L/3+/4+/IFN\\IL2","/S/Lv/L/3+/8+/IFN+IL2-","/S/Lv/L/3+/4+/IFN+IL2-","/S/Lv/L/3+/8+/IFN-IL2+", "/S/Lv/L/3+/4+/Granzyme B-","4+/IFN-IL2+","/S/Lv/L/3+/8+/57-", "/S/Lv/L/3+/4+/57-" ) for(i in gates_to_remove){ try(Rm(i,gating_subset),silent=TRUE) } .getCounts<-function(stat="count"){ auto_stats<-getPopStats(auto_gating,statistic=stat) manual_stats<-getPopStats(gating_subset,statistic=stat) #combine melted_auto<-melt(auto_stats) melted_manual<-melt(manual_stats) setnames(melted_manual,c("population","file","counts")) setnames(melted_auto,c("population","file","counts")) melted_auto<-subset(melted_auto,!population%in%c("boundary","root")) melted_auto$population<-factor(melted_auto$population) melted_manual<-subset(melted_manual,!population%in%c("4+/57-","4+/Granzyme B-","4+/IFN+IL2-","4+/IFN-IL2+","4+/IFN\\IL2","8+/57-","8+/Granzyme B-","8+/IFN+IL2-", "8+/IFN-IL2+" ,"8+/IFN\\IL2","root")) melted_manual$population<-factor(melted_manual$population) D<-adist((levels(melted_auto$population)),(levels(melted_manual$population)),ignore.case=TRUE,partial=FALSE) D<-solve_LSAP(t(D)) data.frame(levels(melted_auto$population)[D],levels(melted_manual$population)) levels(melted_auto$population)[D]<-levels(melted_manual$population) merged<-rbind(cbind(melted_auto,method="auto"),cbind(melted_manual,method="manual")) merged } merged_counts<-.getCounts(stat="count") merged_props<-.getCounts(stat="freq") corr.coef<-cor(dcast(merged_counts,file+population~method,value.var = "counts")[,c("auto","manual")],use="complete") corr.coef.freq<-cor(dcast(merged_props,file+population~method,value.var = "counts")[,c("auto","manual")],use="complete") et<-element_text(size=20) et2<-element_text(size=12) p1<-ggplot(dcast(merged_counts,file+population~method,value.var = "counts"))+geom_point(aes(x=auto,y=manual,color=basename(as.character(population))))+scale_y_log10(Kmisc::wrap("Manual Gating Population Count",30))+scale_x_log10(Kmisc::wrap("Autogating Population Count",30))+theme_bw()+geom_abline(lty=3)+theme(axis.text.x=et,axis.text.y=et,axis.title.x=et,axis.title.y=et,legend.text=et2,legend.position="none",plot.title=et)+scale_color_discrete("Population")+geom_text(aes(x=10,y=100000,label=sprintf("rho=%s",signif(corr.coef[1,2],4))),data=data.frame(corr.coef))+ggtitle("Counts") p2<-ggplot(dcast(merged_props,file+population~method,value.var = "counts"))+geom_point(aes(x=auto,y=manual,color=basename(as.character(population))))+scale_y_log10(Kmisc::wrap("Manual Gating Population Proportion",30))+scale_x_log10(Kmisc::wrap("Autogating Population Proportion",30))+theme_bw()+geom_abline(lty=3)+theme(axis.text.x=et,axis.text.y=et,axis.title.x=et,axis.title.y=et,legend.text=et2,legend.position="left",plot.title=et)+scale_color_discrete("Population")+geom_text(aes(x=0.01,y=0.75,label=sprintf("rho=%s",signif(corr.coef.freq[1,2],4))),data=data.frame(corr.coef.freq))+ggtitle("Proportions") grid.draw(cbind(ggplotGrob(p1), ggplotGrob(p2), size="last")) ## ----extract_stats_echo,echo=TRUE,eval=FALSE----------------------------- ## #Extract stats ## auto_stats<-getPopStats(auto_gating,statistic="count") ## manual_stats<-getPopStats(gating_subset,statistic="count") ## ----example_Perforin,echo=FALSE,eval=TRUE,fig.cap='Automated and Manual Gating of Perforin/CD8',cache=FALSE,fig.align='center',fig.width=7.5,fig.height=3.5,out.width=500---- p1<-plotGate(auto_gating[[sampleNames(gating_subset)[7]]],"cd8/Prf",default.y=" “，xbin = 256，边缘= false，path = 2，排列= false，main =”自动“，xlab = list（cex = 1.5），ylab = list（cex = 1.5），scales = list（cex = 1.5），par.strip.text = list（cex = 1.5））[[1]] p2 <-plotgate（gating_subset [[samplenames（gating_subset）[7]]]，“8 + / perforin +”，default.y =“ “，xbin = 256，边缘= false，path = 2，xlab = list（cex = 1.5），ylab = list（cex = 1.5），scales = list（cex = 1.5），par.strip.text = list（cex= 1.5），排列= false，main =“手动”）[[1]] grid.arrange（p1，p2，ncol = 2）## ---- example_derivative_gate，echo = false，eval = true，fig.align='center'，out.width = 500，figthth = 7.5，fig.height = 3.5 ---- fr <-getdata（auto_gating [[7]]，“CD8 / CD57-”）切割<-tailgate（FR，Channel =“ “，filter_id =”perforin_gate“）DENS <-DENTY（EXPRS（FR [，” “]），调整= 2）second_deriv < - diff（符号（diff（dent $ y）））哪个_maxima < - 哪个（second_deriv == -2）+ 1 when_maxima < - findinginterval（dens $ x [that_maxima]，范围（exprs（fr [，' “]）））== 1]哪个_maxima < - 哪个_maxima [drens_maxima]，decenting = true）] peaks < - dens $ x [that_maxima] deriv_out < - apenceto ::。deriv_dentions（x = exprs（fr [，“ “），调整= 2，德国衍生= 1）par（mfrow = c（1,2））图（DENS，main =”密度“）aline（v =峰）abline（v = cut @ min，col =”红色“）绘图（deriv_out，type =”l“，main =”first arivative“，xlab =”x“，ylab =”密度“）abline（v = cut @ min，col =”红色“）abline（v =峰值）## ---- example_tnfa，echo = false，eval = true，cache = false，tim.cap =“tfna的自动和手动门控”，fig.Align ='Center'---- P1 <-Plotgate（auto_gating [[samplenames（gating_subset）[7]]]，c（“CD8 / TNFA”，“CD4 / TNFA”），default.y =“ “，xbin = 256，边缘= false，path = 2，排列= false，main =”自动化“，xlab = list（cex = 1.1），ylab = list（cex = 1.1），scales = list（cex = 1.1），par.strip.text = list（cex = 1.1））p2 <-plotgate（gating_subset [[samplenames（gating_subset）[7]]，c（“8 + / tnfa +”，“4 + / tnfa +”），默认.Y =“ “，xbin = 256，边缘= false，path = 2，xlab = list（cex = 1.1），ylab = list（cex = 1.1），scales = list（cex = 1.1），par.strip.text = list（cex= 1.1），排列= false，main =“手动”）do.call（grid.arrange，c（p1，p2，ncol = 2））## ---- getData，echo = true，eval = false ------------------------------------------------------------------g）## ----绘图，echo = true，eval = false ---------------------------------------------------- ## plotgate（auto_gating，“cd3”）## ----子集，echo = true，eval = false ------------------------------------------ ## first_ten_fcs_files <-auto_gating [1:10] ## ---- listgtmethods，echo = true，eval = false ------------------------------ ## listgtmethods（）## ---- regrart_plugin，echo = true，eval =false ----------------------------- ## registerPlugins（MyFunction，methodname，依赖关系，“预处理”|“gating”）## ---- gen_template，echo = true，eval = false，结果='Asis'------------------ ## templategen（goting_subset [[1]）## ---- Gen_Template_Noecho，Echo = False，Eval = True，结果='ASIS'------------- HD <-HED（DATA.FRAME（模板）gen（gating_subset [[1]]），7）HD [是.NA（HD）] < - “”kmisc :: htmltable（hd）## ---- echo = true，equ = false ----------------------------------------- ###########--数据子集## auto_subset <-auto_gating [1:20]＃First 20 Samples ###＃在模板中进行更改。保存，然后在R. ## gt <-gatingTemplate（“数据/模板/ gt_080.csv”中重新加载它）###从auto_gating ## RM（“可行”，auto_subset）####与新模板的门，停止在可行的门后（是非全日子）## gating（gt，auto_subset，stop.at =“nonneutro”）###查看新结果## plotgate（auto_subset，“可行”）