├── .DS_Store ├── .Rhistory ├── DESCRIPTION ├── NAMESPACE ├── R ├── .Rhistory ├── cnv_caller_functions_packaged.R └── data.R ├── README.md ├── cnv_caller_functions_packaged.R ├── copyscat_tutorial.R ├── data ├── scDataSamp.RData ├── scDataSamp.rda └── scDataSampClusters.rda ├── gpl_license.txt ├── hg19_references ├── hg19_1e+06_cpg_densities.tsv ├── hg19_1e+06_cytoband_densities_granges.tsv └── hg19_chrom_sizes.tsv ├── hg38_references ├── hg38_1e+06_cpg_densities.tsv ├── hg38_1e+06_cytoband_densities_granges.tsv └── hg38_chrom_sizes.tsv ├── man ├── annotateCNV3.Rd ├── annotateCNV4.Rd ├── annotateCNV4B.Rd ├── annotateLosses.Rd ├── clusterCNV.Rd ├── collapseChrom3N.Rd ├── computeCenters.Rd ├── cutAverage.Rd ├── estimateCellCycleFraction.Rd ├── filterCells.Rd ├── generateReferences.Rd ├── getAlteredSegments.Rd ├── getDoubleMinutes.Rd ├── getLOHRegions.Rd ├── graphCNVDistribution.Rd ├── identifyCNVClusters.Rd ├── identifyDoubleMinutes.Rd ├── identifyNonNeoplastic.Rd ├── initialiseEnvironment.Rd ├── initializeStandards.Rd ├── makeFakeCells.Rd ├── normalizeMatrixN.Rd ├── readInputTable.Rd ├── saveSummaryStats.Rd ├── scDataSamp.Rd ├── scDataSampClusters.Rd ├── scaleMatrix.Rd ├── scaleMatrixBins.Rd ├── setOutputFile.Rd ├── smoothClusters.Rd └── testOverlap.Rd ├── process_fragment_file.py └── scDataSampClusters.Rdata /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spcdot/CopyscAT/e785ed313cdcb64cf2f153c9e6cddf7f8351829d/.DS_Store -------------------------------------------------------------------------------- /.Rhistory: -------------------------------------------------------------------------------- 1 | #normalize ratio by general gain or loss of signal 2 | withRatio$ratio = withRatio$ratio/colMeans(withRatio["ratio"]) 3 | cpts=cpt.meanvar(as.numeric(unlist(withRatio %>% dplyr::select(ratio))),penalty = "AIC",method = "PELT",minseglen = 2) # can change to 3 for fewer bits 4 | cpt_list<-data.frame(end=cpts@cpts, start=lag(cpts@cpts,default=0)+1,mean=cpts@param.est$mean) %>% filter(mean>1.5 | mean<0.5) %>% mutate(chrom=curChrom) 5 | #append start and end based on loc 6 | cpt_list$end=withRatio$segment[cpt_list$end] 7 | cpt_list$start=withRatio$segment[cpt_list$start] 8 | allCpts=append(allCpts,list(cpt_list)) 9 | } 10 | } 11 | #? adjust and normalize? 12 | all_roi<-bind_rows(allCpts) 13 | #iterate through regions 14 | allVals=list() 15 | for (j in 1:nrow(all_roi)) 16 | { 17 | curStart=all_roi$start[j] 18 | curEnd=all_roi$end[j] 19 | cellSubset=scData_k_norm %>% filter(chrom==all_roi$chrom[j]) 20 | locStart=which(cellSubset$pos==curStart) 21 | locEnd=which(cellSubset$pos==curEnd) 22 | cellSubset=cellSubset[locStart:locEnd,] 23 | merged_by_cluster<-inner_join(rownames_to_column(data.frame(mean=colMeans(cellSubset %>% dplyr::select(ends_with("-1")))),var="barcode"), 24 | rownames_to_column(data.frame(cluster=nmf_results$cellAssigns),var="barcode")) 25 | merged_by_cluster<-merged_by_cluster %>% mutate(neoplastic=(cluster!=nmf_results$clusterNormal)) %>% mutate(cluster=factor(cluster),neoplastic=factor(neoplastic)) 26 | merged_by_cluster %>% group_by(neoplastic) %>% summarise(mean=median(mean),counts=n()) 27 | #can we feed residuals into the decomposition? 28 | #estimated ratio 29 | testdata=(merged_by_cluster %>% dplyr::select(-barcode,-neoplastic)) 30 | discrims<-lda(cluster~.,data=testdata) 31 | preds = discrims %>% predict(testdata) 32 | preds$class 33 | estimated_losses=bind_cols(left_join(data.frame(cluster=preds$class), 34 | rownames_to_column(data.frame(discrims$means),var="cluster")) %>% mutate(mean=mean/discrims$means[nmf_results$clusterNormal]),barcode=merged_by_cluster$barcode) %>% mutate(alteration=j) 35 | allVals=append(allVals,list(estimated_losses)) 36 | } 37 | #if range less than 0.4 remove column 38 | valsTable=bind_rows(allVals) 39 | alterationsTable=valsTable %>% dplyr::select(mean,barcode,alteration) # %>% spread(value=mean,key=barcode) 40 | #name alterations 41 | all_roi<-all_roi %>% mutate(name=sprintf("%s:%s-%s",chrom,start,end),alteration=row_number()) 42 | final_alterations<-left_join(alterationsTable,all_roi %>% dplyr::select(alteration,name),by="alteration") 43 | final_alterations<-final_alterations %>% dplyr::select(barcode, mean,name) %>% spread(key=name,value=mean) 44 | all_ranges<-apply(final_alterations %>% dplyr::select(-barcode),2,max)-apply(final_alterations %>% dplyr::select(-barcode),2,min) 45 | rangeThreshold=0.4 46 | which(all_ranges>rangeThreshold) 47 | final_alterations<-final_alterations[c("barcode",names(which(all_ranges>rangeThreshold)))] 48 | #then remove ones with max of one group < X (e.g. 50) 49 | minCutoff=50 50 | clean_list=unlist(final_alterations %>% gather("alteration","value",starts_with("chr")) %>% group_by(alteration,value) %>% summarise(min=n()) %>% arrange(alteration,min) %>% slice_head(n=1) %>% filter(min>minCutoff) %>% dplyr::select(alteration)) 51 | clean_list<-as.vector(clean_list) 52 | final_alterations<-final_alterations %>% dplyr::select(c("barcode",clean_list)) 53 | final_alterations 54 | write.table(file="~/p6d-chr_all-test.csv",x=final_alterations,quote=FALSE,sep=",",row.names=FALSE,col.names=TRUE) 55 | load("~/bing_ren_brain/rna_files/brain1rna.Rdata") 56 | gene.activities[1:5,1:5] 57 | colnames(gene.activities) 58 | rownames(gene.activities) 59 | genenames_to_convert<-rownames(gene.activities) 60 | hgnc.check<-checkGeneSymbols(genenames_to_convert) 61 | #rename to suggested symbols 62 | hgnc.check[which(!is.na(hgnc.check$Suggested.Symbol)),] 63 | final_rowlist<-which(!is.na(hgnc.check$Suggested.Symbol)) 64 | library(HGNChelper) 65 | getCurrentHumanMap() 66 | #checkGeneSymbols 67 | genenames_to_convert<-rownames(gene.activities) 68 | hgnc.check<-checkGeneSymbols(genenames_to_convert) 69 | #rename to suggested symbols 70 | hgnc.check[which(!is.na(hgnc.check$Suggested.Symbol)),] 71 | final_rowlist<-which(!is.na(hgnc.check$Suggested.Symbol)) 72 | final_rowlist 73 | final_rowlist 74 | gene.activities[final_rowlist,] 75 | gene.activities<-gene.activities[final_rowlist,] 76 | rownames(gene.activities)<-hgnc.check$Suggested.Symbol[final_rowlist] 77 | rnamod_genes<-read.delim("~/sf_peaklists/RNA_modules_may2022.tsv",header=TRUE,sep="\t") 78 | rnamod_genes<-rnamod_genes %>% mutate(Genes=strsplit(Genes,", ")) %>% unnest(Genes) 79 | rnamod_genes<-rnamod_genes %>% transmute(Module=RNAmodule,Gene=Genes) 80 | library(dplyr) 81 | library(tidyr) 82 | library(stringr) 83 | library(Matrix) 84 | library(ggplot2) 85 | library(pheatmap) 86 | rnamod_genes<-read.delim("~/sf_peaklists/RNA_modules_may2022.tsv",header=TRUE,sep="\t") 87 | rnamod_genes<-rnamod_genes %>% mutate(Genes=strsplit(Genes,", ")) %>% unnest(Genes) 88 | rnamod_genes<-rnamod_genes %>% transmute(Module=RNAmodule,Gene=Genes) 89 | read.delim("~/bing_ren_brain/GSE184462_metadata.tsv") 90 | all_meta<-read.delim("~/bing_ren_brain/GSE184462_metadata.tsv") 91 | total_means 92 | module_names=unique(rnamod_genes$Module) 93 | module_averages_by_sample<-data.frame() 94 | #peak_unfiltered %>% filter(Module=="white") %>% select(chrom,start,end) 95 | #USING PRE-NORMED VALUES SO NO CORRECTIONS NEEDED 96 | #pdf("gene_peaks_sf_529_2022.pdf",width=4,height=4) 97 | #rownames(gene.activities) 98 | rawData=data.frame() 99 | rnamod_genes$Gene[which(str_detect(rnamod_genes$Gene,"H2A"))] 100 | celltype_activity$V1[which(str_detect(celltype_activity$V1,"H2A"))] 101 | for (mod_name in module_names) 102 | { 103 | mod_genelist<-rnamod_genes %>% dplyr::filter(Module==mod_name) %>% dplyr::select(Gene) 104 | final_genes<-which(mod_genelist$Gene %in% rownames(gene.activities)) 105 | if(length(final_genes)>0) 106 | { 107 | total_means<-data.frame(mean=colMeans(gene.activities[rownames(gene.activities) %in% mod_genelist[final_genes]]),module=mod_name) 108 | #apply gene weight normalization 109 | total_means$mean<-total_means$mean 110 | if (ncol(rawData)>0) 111 | { 112 | rawData = bind_cols(rawData,total_means %>% dplyr::select(-module)) 113 | } 114 | else 115 | { 116 | rawData = total_means %>% dplyr::select(-module) 117 | } 118 | module_averages_by_sample = rbind.data.frame(module_averages_by_sample,rownames_to_column(total_means,var="barcode") %>% mutate(samp=substr(barcode,1,5)) %>% group_by(samp) %>% summarise(value=mean(mean)) %>% mutate(cluster=mod_name)) 119 | #module_averages_by_sample = rbind.data.frame(module_averages_by_sample,rownames_to_column(total_means,var="barcode") %>% mutate(samp=substr(barcode,1,5)) %>% group_by(samp) %>% summarise(value=mean(mean)) %>% mutate(cluster=mod_name)) 120 | #combined@meta.data[which(is.na(combined@meta.data[mod_name])),mod_name]<-0 121 | #print(FeaturePlot(combined,features = mod_name,order = TRUE,min.cutoff = "q20",max.cutoff = "q99")) 122 | } 123 | } 124 | rawData 125 | gene.activities[rownames(gene.activities) %in% mod_genelist[final_genes]]) 126 | gene.activities[rownames(gene.activities) %in% mod_genelist[final_genes]] 127 | rownames(gene.activities) %in% mod_genelist[final_genes]] 128 | rownames(gene.activities) %in% mod_genelist[final_genes] 129 | mod_genelist[final_genes] 130 | mod_genelist 131 | final_genes 132 | mod_genelist[final_genes] 133 | mod_genelist 134 | mod_genelist[1:5] 135 | mod_genelist[,1:5] 136 | str(mod_genelist) 137 | rawData=data.frame() 138 | rnamod_genes$Gene[which(str_detect(rnamod_genes$Gene,"H2A"))] 139 | celltype_activity$V1[which(str_detect(celltype_activity$V1,"H2A"))] 140 | for (mod_name in module_names) 141 | { 142 | mod_genelist<-rnamod_genes %>% dplyr::filter(Module==mod_name) %>% dplyr::select(Gene) 143 | final_genes<-which(mod_genelist$Gene %in% rownames(gene.activities)) 144 | if(length(final_genes)>0) 145 | { 146 | total_means<-data.frame(mean=colMeans(gene.activities[rownames(gene.activities) %in% mod_genelist$Gene[final_genes]]),module=mod_name) 147 | #apply gene weight normalization 148 | total_means$mean<-total_means$mean 149 | if (ncol(rawData)>0) 150 | { 151 | rawData = bind_cols(rawData,total_means %>% dplyr::select(-module)) 152 | } 153 | else 154 | { 155 | rawData = total_means %>% dplyr::select(-module) 156 | } 157 | module_averages_by_sample = rbind.data.frame(module_averages_by_sample,rownames_to_column(total_means,var="barcode") %>% mutate(samp=substr(barcode,1,5)) %>% group_by(samp) %>% summarise(value=mean(mean)) %>% mutate(cluster=mod_name)) 158 | #module_averages_by_sample = rbind.data.frame(module_averages_by_sample,rownames_to_column(total_means,var="barcode") %>% mutate(samp=substr(barcode,1,5)) %>% group_by(samp) %>% summarise(value=mean(mean)) %>% mutate(cluster=mod_name)) 159 | #combined@meta.data[which(is.na(combined@meta.data[mod_name])),mod_name]<-0 160 | #print(FeaturePlot(combined,features = mod_name,order = TRUE,min.cutoff = "q20",max.cutoff = "q99")) 161 | } 162 | } 163 | total_means 164 | data.frame(mean=colMeans(gene.activities[rownames(gene.activities) %in% mod_genelist$Gene[final_genes]]),module=mod_name) 165 | gene.activities[rownames(gene.activities) %in% mod_genelist$Gene[final_genes]] 166 | rownames(gene.activities) %in% mod_genelist$Gene[final_genes] 167 | gene.activities[rownames(gene.activities) %in% mod_genelist$Gene[final_genes],] 168 | mod_genelist$Gene[final_genes],] 169 | mod_genelist$Gene[final_genes] 170 | mod_name 171 | unique(mod_genelist$Gene[final_genes]) 172 | total_means<-data.frame(mean=colMeans(gene.activities[rownames(gene.activities) %in% unique(mod_genelist$Gene[final_genes]),]),module=mod_name) 173 | total_means 174 | inner_join 175 | ?inner_join 176 | module_names=unique(rnamod_genes$Module) 177 | module_averages_by_sample<-data.frame() 178 | rawData=data.frame() 179 | for (mod_name in module_names) 180 | { 181 | mod_genelist<-rnamod_genes %>% dplyr::filter(Module==mod_name) %>% dplyr::select(Gene) 182 | final_genes<-which(mod_genelist$Gene %in% rownames(gene.activities)) 183 | if(length(final_genes)>0) 184 | { 185 | total_means<-data.frame(mean=colMeans(gene.activities[rownames(gene.activities) %in% unique(mod_genelist$Gene[final_genes]),]),module=mod_name) 186 | #apply gene weight normalization 187 | total_means$mean<-total_means$mean 188 | if (ncol(rawData)>0) 189 | { 190 | rawData = bind_cols(rawData,total_means %>% dplyr::select(-module)) 191 | } 192 | else 193 | { 194 | rawData = total_means %>% dplyr::select(-module) 195 | } 196 | module_averages_by_sample = rbind.data.frame(module_averages_by_sample,rownames_to_column(total_means,var="barcode") %>% mutate(samp=substr(barcode,1,5)) %>% group_by(samp) %>% summarise(value=mean(mean)) %>% mutate(cluster=mod_name)) 197 | #module_averages_by_sample = rbind.data.frame(module_averages_by_sample,rownames_to_column(total_means,var="barcode") %>% mutate(samp=substr(barcode,1,5)) %>% group_by(samp) %>% summarise(value=mean(mean)) %>% mutate(cluster=mod_name)) 198 | #combined@meta.data[which(is.na(combined@meta.data[mod_name])),mod_name]<-0 199 | #print(FeaturePlot(combined,features = mod_name,order = TRUE,min.cutoff = "q20",max.cutoff = "q99")) 200 | } 201 | } 202 | library(tibble) 203 | total_means 204 | rownames(total_means) 205 | left_join(data.frame(barcode=rownames(total_means)),all_meta,by="barcode") 206 | all_meta 207 | colnames(all_meta) 208 | left_join(data.frame(cellID=rownames(total_means)),all_meta,by="barcode") 209 | left_join(data.frame(cellID=rownames(total_means)),all_meta,by="cellID") 210 | total_means 211 | all_meta 212 | all_meta %>% dplyr::filter(str_detect(sample,"brain")) %>% mutate(barcode=str_match(cellID,'[ACTG]+$')) 213 | all_meta %>% dplyr::filter(str_detect(sample,"brain")) %>% mutate(barcode=str_match(cellID,'[ACTG]+$')) 214 | all_meta %>% dplyr::filter(str_detect(sample,"cort")) %>% mutate(barcode=str_match(cellID,'[ACTG]+$')) 215 | all_meta %>% dplyr::filter(str_detect(tissue,"brain")) %>% mutate(barcode=str_match(cellID,'[ACTG]+$')) 216 | unique(all_meta$tissue) 217 | all_meta 218 | tail(all_meta) 219 | all_meta<-read.delim("~/bing_ren_brain/GSE184462_metadata.tsv") 220 | tail(all_meta) 221 | unique(all_meta$tissue) 222 | all_meta %>% dplyr::filter(str_detect(tissue,"brain")) %>% mutate(barcode=str_match(cellID,'[ACTG]+$')) 223 | all_meta_clean<-all_meta %>% dplyr::filter(str_detect(tissue,"brain")) %>% mutate(barcode=str_match(cellID,'[ACTG]+$')) 224 | rm(all_meta) 225 | left_join(data.frame(barcode=rownames(total_means)),all_meta_clean,by="barcode") 226 | cell_meta<-left_join(data.frame(barcode=rownames(total_means)),all_meta_clean,by="barcode") 227 | total_means 228 | module_names=unique(rnamod_genes$Module) 229 | module_averages_by_sample<-data.frame() 230 | #peak_unfiltered %>% filter(Module=="white") %>% select(chrom,start,end) 231 | #USING PRE-NORMED VALUES SO NO CORRECTIONS NEEDED 232 | #pdf("gene_peaks_sf_529_2022.pdf",width=4,height=4) 233 | #rownames(gene.activities) 234 | rawData=data.frame() 235 | library(tibble) 236 | for (mod_name in module_names) 237 | { 238 | mod_genelist<-rnamod_genes %>% dplyr::filter(Module==mod_name) %>% dplyr::select(Gene) 239 | final_genes<-which(mod_genelist$Gene %in% rownames(gene.activities)) 240 | if(length(final_genes)>0) 241 | { 242 | total_means<-data.frame(mean=colMeans(gene.activities[rownames(gene.activities) %in% unique(mod_genelist$Gene[final_genes]),]),module=mod_name) 243 | #apply gene weight normalization 244 | total_means$mean<-total_means$mean 245 | if (ncol(rawData)>0) 246 | { 247 | rawData = bind_cols(rawData,total_means %>% dplyr::select(-module)) 248 | } 249 | else 250 | { 251 | rawData = total_means %>% dplyr::select(-module) 252 | } 253 | cell_meta<-left_join(data.frame(barcode=rownames(total_means)),all_meta_clean,by="barcode") 254 | module_averages_by_sample = full_join(rbind.data.frame(module_averages_by_sample,rownames_to_column(total_means,var="barcode"),cell_meta,by="barcode") %>% group_by(cell.type) %>% summarise(value=mean(mean)) %>% mutate(cluster=mod_name)) 255 | #module_averages_by_sample = rbind.data.frame(module_averages_by_sample,rownames_to_column(total_means,var="barcode") %>% mutate(samp=substr(barcode,1,5)) %>% group_by(samp) %>% summarise(value=mean(mean)) %>% mutate(cluster=mod_name)) 256 | #combined@meta.data[which(is.na(combined@meta.data[mod_name])),mod_name]<-0 257 | #print(FeaturePlot(combined,features = mod_name,order = TRUE,min.cutoff = "q20",max.cutoff = "q99")) 258 | } 259 | } 260 | module_names=unique(rnamod_genes$Module) 261 | module_averages_by_sample<-data.frame() 262 | #peak_unfiltered %>% filter(Module=="white") %>% select(chrom,start,end) 263 | #USING PRE-NORMED VALUES SO NO CORRECTIONS NEEDED 264 | #pdf("gene_peaks_sf_529_2022.pdf",width=4,height=4) 265 | #rownames(gene.activities) 266 | rawData=data.frame() 267 | library(tibble) 268 | for (mod_name in module_names) 269 | { 270 | mod_genelist<-rnamod_genes %>% dplyr::filter(Module==mod_name) %>% dplyr::select(Gene) 271 | final_genes<-which(mod_genelist$Gene %in% rownames(gene.activities)) 272 | if(length(final_genes)>0) 273 | { 274 | total_means<-data.frame(mean=colMeans(gene.activities[rownames(gene.activities) %in% unique(mod_genelist$Gene[final_genes]),]),module=mod_name) 275 | #apply gene weight normalization 276 | total_means$mean<-total_means$mean 277 | if (ncol(rawData)>0) 278 | { 279 | rawData = bind_cols(rawData,total_means %>% dplyr::select(-module)) 280 | } 281 | else 282 | { 283 | rawData = total_means %>% dplyr::select(-module) 284 | } 285 | cell_meta<-left_join(data.frame(barcode=rownames(total_means)),all_meta_clean,by="barcode") 286 | module_averages_by_sample = rbind.data.frame(module_averages_by_sample,full_join(rownames_to_column(total_means,var="barcode"),cell_meta,by="barcode") %>% group_by(cell.type) %>% summarise(value=mean(mean)) %>% mutate(cluster=mod_name)) 287 | #module_averages_by_sample = rbind.data.frame(module_averages_by_sample,rownames_to_column(total_means,var="barcode") %>% mutate(samp=substr(barcode,1,5)) %>% group_by(samp) %>% summarise(value=mean(mean)) %>% mutate(cluster=mod_name)) 288 | #combined@meta.data[which(is.na(combined@meta.data[mod_name])),mod_name]<-0 289 | #print(FeaturePlot(combined,features = mod_name,order = TRUE,min.cutoff = "q20",max.cutoff = "q99")) 290 | } 291 | } 292 | colnames(rawData)<-module_names 293 | rawData[1:5,1:5] 294 | module_averages_by_sample 295 | module_averages_by_sample 296 | pheatmap(module_averages_by_sample,scale="column",cluster_cols = TRUE,clustering_method = "ward.D",show_rownames = TRUE,show_colnames=TRUE) 297 | module_averages_by_sample 298 | module_averages_by_sample %>% spread(cluster) 299 | module_averages_by_sample %>% spread(cluster) 300 | module_averages_by_sample %>% spread(key="cluster",value="value") 301 | column_to_rownames()module_averages_by_sample %>% spread(key="cluster",value="value"),var="cell.type") 302 | column_to_rownames(module_averages_by_sample %>% spread(key="cluster",value="value"),var="cell.type") 303 | heatmap_stuff<-column_to_rownames(module_averages_by_sample %>% spread(key="cluster",value="value"),var="cell.type") 304 | pheatmap(heatmap_stuff,scale="column",cluster_cols = TRUE,clustering_method = "ward.D",show_rownames = TRUE,show_colnames=TRUE) 305 | set.seed(42) 306 | random_genes<-sample(1:nrow(gene.activities),size=3000,replace=F) 307 | activityScoreWeights<-colSums(gene.activities[random_genes,]) 308 | activityScoreWeights 309 | module_averages_by_sample<-data.frame() 310 | #peak_unfiltered %>% filter(Module=="white") %>% select(chrom,start,end) 311 | #USING PRE-NORMED VALUES SO NO CORRECTIONS NEEDED 312 | #pdf("gene_peaks_sf_529_2022.pdf",width=4,height=4) 313 | #rownames(gene.activities) 314 | rawData=data.frame() 315 | library(tibble) 316 | for (mod_name in module_names) 317 | { 318 | mod_genelist<-rnamod_genes %>% dplyr::filter(Module==mod_name) %>% dplyr::select(Gene) 319 | final_genes<-which(mod_genelist$Gene %in% rownames(gene.activities)) 320 | if(length(final_genes)>0) 321 | { 322 | total_means<-data.frame(mean=colMeans(gene.activities[rownames(gene.activities) %in% unique(mod_genelist$Gene[final_genes]),]),module=mod_name) 323 | #apply gene weight normalization 324 | total_means$mean<-total_means$mean / activityScoreWeights 325 | if (ncol(rawData)>0) 326 | { 327 | rawData = bind_cols(rawData,total_means %>% dplyr::select(-module)) 328 | } 329 | else 330 | { 331 | rawData = total_means %>% dplyr::select(-module) 332 | } 333 | cell_meta<-left_join(data.frame(barcode=rownames(total_means)),all_meta_clean,by="barcode") 334 | module_averages_by_sample = rbind.data.frame(module_averages_by_sample,full_join(rownames_to_column(total_means,var="barcode"),cell_meta,by="barcode") %>% group_by(cell.type) %>% summarise(value=mean(mean)) %>% mutate(cluster=mod_name)) 335 | #module_averages_by_sample = rbind.data.frame(module_averages_by_sample,rownames_to_column(total_means,var="barcode") %>% mutate(samp=substr(barcode,1,5)) %>% group_by(samp) %>% summarise(value=mean(mean)) %>% mutate(cluster=mod_name)) 336 | #combined@meta.data[which(is.na(combined@meta.data[mod_name])),mod_name]<-0 337 | #print(FeaturePlot(combined,features = mod_name,order = TRUE,min.cutoff = "q20",max.cutoff = "q99")) 338 | } 339 | } 340 | colnames(rawData)<-module_names 341 | dev.off() 342 | rawData[1:5,1:5] 343 | module_averages_by_sample 344 | heatmap_stuff<-column_to_rownames(module_averages_by_sample %>% spread(key="cluster",value="value"),var="cell.type") 345 | pheatmap(heatmap_stuff,scale="column",cluster_cols = TRUE,clustering_method = "ward.D",show_rownames = TRUE,show_colnames=TRUE) 346 | write.table(rawData,file="~/bing_ren_brain/adult_rna_modules_all_may2022_1.tsv",col.names=TRUE,quote=FALSE,sep='\t') 347 | write.table(module_averages_by_sample,file="~/bing_ren_brain/adult_rna_modules_celltypes_may2022_1.tsv",col.names=TRUE,quote=FALSE,sep='\t') 348 | dev.off() 349 | dev.off() 350 | pdf("~/bing_ren_brain/sf_rna_module_heatmap.pdf",width=5,height=5) 351 | pheatmap(heatmap_stuff,scale="column",cluster_cols = TRUE,clustering_method = "ward.D",show_rownames = TRUE,show_colnames=TRUE) 352 | dev.off() 353 | write.table(rawData,file="~/bing_ren_brain/adult_rna_modules_all_may2022_1.tsv",col.names=TRUE,quote=FALSE,sep='\t') 354 | write.table(module_averages_by_sample,file="~/bing_ren_brain/adult_rna_modules_celltypes_may2022_1.tsv",col.names=TRUE,quote=FALSE,sep='\t') 355 | load("~/bing_ren_brain/rna_files/brain2rna.Rdata") 356 | #metadata 357 | all_meta<-read.delim("~/bing_ren_brain/GSE184462_metadata.tsv") 358 | tail(all_meta) 359 | unique(all_meta$tissue) 360 | all_meta_clean<-all_meta %>% dplyr::filter(str_detect(tissue,"brain")) %>% mutate(barcode=str_match(cellID,'[ACTG]+$')) 361 | rm(all_meta) 362 | gene.activities[1:5,1:5] 363 | rownames(gene.activities) 364 | library(HGNChelper) 365 | getCurrentHumanMap() 366 | #checkGeneSymbols 367 | genenames_to_convert<-rownames(gene.activities) 368 | hgnc.check<-checkGeneSymbols(genenames_to_convert) 369 | #rename to suggested symbols 370 | hgnc.check[which(!is.na(hgnc.check$Suggested.Symbol)),] 371 | final_rowlist<-which(!is.na(hgnc.check$Suggested.Symbol)) 372 | gene.activities<-gene.activities[final_rowlist,] 373 | rownames(gene.activities)<-hgnc.check$Suggested.Symbol[final_rowlist] 374 | set.seed(42) 375 | random_genes<-sample(1:nrow(gene.activities),size=3000,replace=F) 376 | activityScoreWeights<-colSums(gene.activities[random_genes,]) 377 | #gene.activities<-gene.activities/ 378 | #normalie values 379 | #RUN THROUGH MODULES 380 | module_names=unique(rnamod_genes$Module) 381 | module_averages_by_sample<-data.frame() 382 | #peak_unfiltered %>% filter(Module=="white") %>% select(chrom,start,end) 383 | #USING PRE-NORMED VALUES SO NO CORRECTIONS NEEDED 384 | #pdf("gene_peaks_sf_529_2022.pdf",width=4,height=4) 385 | #rownames(gene.activities) 386 | rawData=data.frame() 387 | library(tibble) 388 | for (mod_name in module_names) 389 | { 390 | mod_genelist<-rnamod_genes %>% dplyr::filter(Module==mod_name) %>% dplyr::select(Gene) 391 | final_genes<-which(mod_genelist$Gene %in% rownames(gene.activities)) 392 | if(length(final_genes)>0) 393 | { 394 | total_means<-data.frame(mean=colMeans(gene.activities[rownames(gene.activities) %in% unique(mod_genelist$Gene[final_genes]),]),module=mod_name) 395 | #apply gene weight normalization 396 | total_means$mean<-total_means$mean / activityScoreWeights 397 | if (ncol(rawData)>0) 398 | { 399 | rawData = bind_cols(rawData,total_means %>% dplyr::select(-module)) 400 | } 401 | else 402 | { 403 | rawData = total_means %>% dplyr::select(-module) 404 | } 405 | cell_meta<-left_join(data.frame(barcode=rownames(total_means)),all_meta_clean,by="barcode") 406 | module_averages_by_sample = rbind.data.frame(module_averages_by_sample,full_join(rownames_to_column(total_means,var="barcode"),cell_meta,by="barcode") %>% group_by(cell.type) %>% summarise(value=mean(mean)) %>% mutate(cluster=mod_name)) 407 | #module_averages_by_sample = rbind.data.frame(module_averages_by_sample,rownames_to_column(total_means,var="barcode") %>% mutate(samp=substr(barcode,1,5)) %>% group_by(samp) %>% summarise(value=mean(mean)) %>% mutate(cluster=mod_name)) 408 | #combined@meta.data[which(is.na(combined@meta.data[mod_name])),mod_name]<-0 409 | #print(FeaturePlot(combined,features = mod_name,order = TRUE,min.cutoff = "q20",max.cutoff = "q99")) 410 | } 411 | } 412 | colnames(rawData)<-module_names 413 | dev.off() 414 | rawData[1:5,1:5] 415 | module_averages_by_sample 416 | heatmap_stuff<-column_to_rownames(module_averages_by_sample %>% spread(key="cluster",value="value"),var="cell.type") 417 | pheatmap(heatmap_stuff,scale="column",cluster_cols = TRUE,clustering_method = "ward.D",show_rownames = TRUE,show_colnames=TRUE) 418 | pheatmap(heatmap_stuff,scale="column",cluster_cols = TRUE,clustering_method = "ward.D",show_rownames = TRUE,show_colnames=TRUE) 419 | write.table(rawData,file="~/bing_ren_brain/adult_rna_modules_all_may2022_2.tsv",col.names=TRUE,quote=FALSE,sep='\t') 420 | write.table(module_averages_by_sample,file="~/bing_ren_brain/adult_rna_modules_celltypes_may2022_2.tsv",col.names=TRUE,quote=FALSE,sep='\t') 421 | pdf("~/bing_ren_brain/sf_rna_module_adult_brain_heatmap_brain2.pdf",width=5,height=5) 422 | pheatmap(heatmap_stuff,scale="column",cluster_cols = TRUE,clustering_method = "ward.D",show_rownames = TRUE,show_colnames=TRUE) 423 | dev.off() 424 | dev.off() 425 | pdf("~/bing_ren_brain/sf_rna_module_adult_brain_heatmap_brain2.pdf",width=5,height=5) 426 | pheatmap(heatmap_stuff,scale="column",cluster_cols = TRUE,clustering_method = "ward.D",show_rownames = TRUE,show_colnames=TRUE) 427 | dev.off() 428 | read.delim(file="~/pasca_brain/GSE162170_atac_cell_metadata.txt",header=TRUE) 429 | library(GenomicRanges) 430 | library(data.table) 431 | library(Signac) 432 | library(Seurat) 433 | library(tibble) 434 | test1<-fread("~/Downloads/GSE141460_EPN_count_matrix_TPM.txt") 435 | #load("~/ep_seurat_object.Rdata") 436 | test1<-column_to_rownames(test1,var="V1") 437 | library(stringr) 438 | t1<-str_match(rownames(test1),"H3[A-Z-0-9]+") 439 | t1[!is.na(t1)] 440 | ?CreateSeuratObject 441 | #rm(atac_file) 442 | test2<-CreateSeuratObject(test1,"epe") 443 | test1 444 | test2<-ScaleData(test2) 445 | #need the genes of interest 446 | test2<-FindVariableFeatures(test2, selection.method = "vst") 447 | #test2<-CellCycleScoring(test2) 448 | test2 <- RunPCA(test2, features = VariableFeatures(test2), ndims.print = 6:10, nfeatures.print = 10) 449 | test2 <- RunTSNE(test2,perplexity=50,dims=2:10) 450 | FeaturePlot(test2, features=c("CTNNA3","CTNND2","LMNA","LAMA2"),min="q5",max="q95",order=TRUE) 451 | pdf("~/filbin_gojo_ctn.pdf",width=5,height=5) 452 | FeaturePlot(test2, features=c("CTNNA3","CTNND2","LMNA","LAMA2"),min="q5",max="q95",order=TRUE) 453 | dev.off() 454 | FeaturePlot(test2, features=c("CTNNA3","CTNND1"),min="q5",max="q95",order=TRUE) 455 | FeaturePlot(test2, features=c("CTNNA3","CTNND1","MUC1"),min="q5",max="q95",order=TRUE) 456 | FeaturePlot(test2, features=c("CTNNA3","CTNND1","MUC1","PDPN"),min="q5",max="q95",order=TRUE) 457 | FeaturePlot(test2, features=c("CTNNA3","CTNND2","LMNA","LAMA2"),min="q5",max="q95",order=TRUE) 458 | library(CopyscAT) 459 | library(MASS) 460 | sessionInfo() 461 | library("roxygen2") 462 | library("devtools") 463 | setwd("~/scATAC_CNV_TOOL") 464 | devtools::document() 465 | setwd("~/scATAC_CNV_TOOL") 466 | devtools::document() 467 | setwd("~/scATAC_CNV_TOOL") 468 | devtools::document() 469 | setwd("~/scATAC_CNV_TOOL") 470 | devtools::document() 471 | setwd("~/scATAC_CNV_TOOL") 472 | devtools::document() 473 | setwd("~/scATAC_CNV_TOOL") 474 | devtools::document() 475 | setwd("~/scATAC_CNV_TOOL") 476 | devtools::document() 477 | install("~/scATAC_CNV_TOOL") 478 | library(CopyscAT) 479 | initialiseEnvironment(genomeFile="~/hg38_chrom_sizes.tsv", 480 | cytobandFile="~/hg38_1e+06_cytoband_densities_granges.tsv", 481 | cpgFile="~/hg38_1e+06_cpg_densities.tsv", 482 | binSize=1e6, 483 | minFrags=1e4, 484 | cellSuffix=c("-1","-2"), 485 | lowerTrim=0.5, 486 | upperTrim=0.8) 487 | #for this tutorial we will use the sample data included in scData 488 | #to create your own use the process_fragment_file.py script included in the package and run it on a fragments.tsv.gz file of your choosing 489 | #SET OUTPUT DEFAULT DIRECTORY AND NAME 490 | setOutputFile("~","samp_dataset") 491 | #PART 1: INITIAL DATA NORMALIZATION 492 | #step 1 normalize the matrix 493 | #USING SAMPLE DATA FROM PACKAGE 494 | #option: if using your own file replace below with the following 495 | #scData<-readInputTable("myInputFile.tsv") 496 | scData<-scDataSamp 497 | scData_k_norm <- normalizeMatrixN(scData,logNorm = FALSE,maxZero=2000,imputeZeros = FALSE,blacklistProp = 0.8,blacklistCutoff=125,dividingFactor=1,upperFilterQuantile = 0.95) 498 | #when using your own data, please make sure you don't have any excess / alt chromosomes 499 | #collapse into chromosome arm level 500 | summaryFunction<-cutAverage 501 | scData_collapse<-collapseChrom3N(scData_k_norm,summaryFunction=summaryFunction,binExpand = 1,minimumChromValue = 100,logTrans = FALSE,tssEnrich = 1,logBase=2,minCPG=300,powVal=0.73) 502 | #apply additional filters 503 | scData_collapse<-filterCells(scData_collapse,minimumSegments = 40,minDensity = 0.1) 504 | nmf_results<-identifyNonNeoplastic(scData_collapse,methodHclust="ward.D",cutHeight = 0.4) 505 | #?identifyNonNeoplastic 506 | write.table(x=rownames_to_column(data.frame(nmf_results$cellAssigns),var="Barcode"),file=str_c(scCNVCaller$locPrefix,scCNVCaller$outPrefix,"_nmf_clusters.csv"),quote=FALSE,row.names = FALSE,sep=",") 507 | print(paste("Normal cluster is: ",nmf_results$clusterNormal)) 508 | altered_segments<-getAlteredSegments(scData_k_norm,nmf_results,lossThreshold = 0.6) 509 | altered_segments 510 | library("roxygen2") 511 | setwd("~/scATAC_CNV_TOOL") 512 | devtools::document() 513 | -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: CopyscAT 2 | Version: 0.40 3 | Depends: 4 | viridis (>= 0.5.1), 5 | NMF (>= 0.23.0), 6 | fastcluster (>= 1.1.25), 7 | biomaRt (>= 2.42.1), 8 | Rtsne (>= 0.15), 9 | FNN (>= 1.1.3), 10 | igraph (>= 1.2.5), 11 | data.table (>= 1.12.8), 12 | changepoint (>= 2.2.2), 13 | zoo (>= 1.8-8), 14 | mclust (>= 5.4.6), 15 | stringr (>= 1.4.0), 16 | edgeR (>= 3.28.1), 17 | limma (>= 3.42.2), 18 | tidyr (>= 1.1.0), 19 | tibble (>= 3.0.3), 20 | gplots (>= 3.0.4), 21 | dplyr (>= 1.0.0), 22 | rtracklayer (>= 1.46.0), 23 | sp (>= 1.4.0), 24 | ggplot2 (>= 3.3.0), 25 | jsonlite (>= 1.7.0), 26 | MASS (>= 7.3.54), 27 | R (>= 2.10) 28 | RoxygenNote: 7.1.2 29 | LazyData: true 30 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | export(.onLoad) 4 | export(annotateCNV3) 5 | export(annotateCNV4) 6 | export(annotateCNV4B) 7 | export(annotateLosses) 8 | export(clusterCNV) 9 | export(collapseChrom3N) 10 | export(computeCenters) 11 | export(cutAverage) 12 | export(estimateCellCycleFraction) 13 | export(filterCells) 14 | export(generateReferences) 15 | export(getAlteredSegments) 16 | export(getDoubleMinutes) 17 | export(getLOHRegions) 18 | export(graphCNVDistribution) 19 | export(identifyCNVClusters) 20 | export(identifyDoubleMinutes) 21 | export(identifyNonNeoplastic) 22 | export(initialiseEnvironment) 23 | export(initializeStandards) 24 | export(makeFakeCells) 25 | export(normalizeMatrixN) 26 | export(readInputTable) 27 | export(saveSummaryStats) 28 | export(scCNVCaller) 29 | export(scaleMatrix) 30 | export(scaleMatrixBins) 31 | export(setOutputFile) 32 | export(smoothClusters) 33 | export(testOverlap) 34 | -------------------------------------------------------------------------------- /R/.Rhistory: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spcdot/CopyscAT/e785ed313cdcb64cf2f153c9e6cddf7f8351829d/R/.Rhistory -------------------------------------------------------------------------------- /R/data.R: -------------------------------------------------------------------------------- 1 | #' Sample processed matrix for testing 2 | #' @format data.frame with 3103 columns and 1371 rows 3 | #' @source 1371 adult GBM cells 4 | "scDataSamp" 5 | #' Sample processed cluster data frame for testing 6 | #' @format data.frame 7 | #' @source adult GBM cells 8 | "scDataSampClusters" 9 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # CopyscAT 2 | Copy number variant inference with single-cell ATAC seq 3 | 4 | INTRODUCTION 5 | CopyscAT is designed to identify large-scale and local alterations in chromosomes without need for a control file. It is currently in early beta - documentation and testing are still works in progress. Development is ongoing with R 4.0.0, so please install within an R 4.0.0 environment. 6 | 7 | Update (Mar 2021): 8 | Now with semi-automated neoplastic vs non-neoplastic cell distinction and baseline correction (check tutorial for more details) 9 | 10 | INSTALLATION INSTRUCTIONS 11 | To install, please download copyscat_tutorial.R and follow the instructions within. 12 | To generate fragment matrices, use the process_fragment_file.py script (type python3 process_fragment_file.py for details regarding input parameters) 13 | 14 | ------------ 15 | 16 | Copyright (C) 2020 University of Calgary 17 | 18 | This program is free software: you can redistribute it and/or modify 19 | it under the terms of the GNU General Public License as published by 20 | the Free Software Foundation, either version 3 of the License, or 21 | (at your option) any later version. 22 | 23 | This program is distributed in the hope that it will be useful, 24 | but WITHOUT ANY WARRANTY; without even the implied warranty of 25 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 26 | GNU General Public License for more details. 27 | 28 | You should have received a copy of the GNU General Public License 29 | along with this program. If not, see . 30 | -------------------------------------------------------------------------------- /copyscat_tutorial.R: -------------------------------------------------------------------------------- 1 | library("devtools") 2 | #####INITIAL ONE-TIME SETUP ##### 3 | #STEP 1: replace "~/CopyscAT" with wherever you git cloned the repo to 4 | install("~/CopyscAT") 5 | 6 | #alternate option: devools install-github option 7 | #some versions of R throw an error because of code warnings with Github install, the below line should fix this issue 8 | Sys.setenv("R_REMOTES_NO_ERRORS_FROM_WARNINGS" = "true") 9 | install_github("spcdot/copyscat") 10 | 11 | #load the package 12 | library(CopyscAT) 13 | 14 | 15 | #load your favourite genome 16 | library(BSgenome.Hsapiens.UCSC.hg38) 17 | #use it to save references - this will create a genome chrom.sizes file, a cytobands file and a CpG file 18 | #NOTE: updated 21-Oct-2021 with a fallback to the REST API 19 | generateReferences(BSgenome.Hsapiens.UCSC.hg38,genomeText = "hg38",tileWidth = 1e6,outputDir = "~") 20 | 21 | ##### REGULAR WORKFLOW ##### 22 | #initialize the environment (note: this needs to be done with every session in which you run Copy-scAT) 23 | initialiseEnvironment(genomeFile="~/hg38_chrom_sizes.tsv", 24 | cytobandFile="~/hg38_1e+06_cytoband_densities_granges.tsv", 25 | cpgFile="~/hg38_1e+06_cpg_densities.tsv", 26 | binSize=1e6, 27 | minFrags=1e4, 28 | cellSuffix=c("-1","-2"), 29 | lowerTrim=0.5, 30 | upperTrim=0.8) 31 | 32 | #for this tutorial we will use the sample data included in scData 33 | #to create your own use the process_fragment_file.py script included in the package and run it on a fragments.tsv.gz file of your choosing 34 | 35 | #SET OUTPUT DEFAULT DIRECTORY AND NAME 36 | setOutputFile("~","samp_dataset") 37 | 38 | #PART 1: INITIAL DATA NORMALIZATION 39 | #step 1 normalize the matrix 40 | #USING SAMPLE DATA FROM PACKAGE 41 | #option: if using your own file replace below with the following 42 | #scData<-readInputTable("myInputFile.tsv") 43 | scData<-scDataSamp 44 | scData_k_norm <- normalizeMatrixN(scData,logNorm = FALSE,maxZero=2000,imputeZeros = FALSE,blacklistProp = 0.8,blacklistCutoff=125,dividingFactor=1,upperFilterQuantile = 0.95) 45 | #when using your own data, please make sure you don't have any excess / alt chromosomes 46 | 47 | #collapse into chromosome arm level 48 | summaryFunction<-cutAverage 49 | scData_collapse<-collapseChrom3N(scData_k_norm,summaryFunction=summaryFunction,binExpand = 1,minimumChromValue = 100,logTrans = FALSE,tssEnrich = 1,logBase=2,minCPG=300,powVal=0.73) 50 | #apply additional filters 51 | scData_collapse<-filterCells(scData_collapse,minimumSegments = 40,minDensity = 0.1) 52 | 53 | #show unscaled chromosome list 54 | graphCNVDistribution(scData_collapse,outputSuffix = "test_violinsn2") 55 | 56 | #compute centers 57 | median_iqr <- computeCenters(scData_collapse,summaryFunction=summaryFunction) 58 | 59 | #PART 2: ASSESSMENT OF CHROMOSOME-LEVEL CNVs 60 | #OPTION 1: identify chromosome-level amplifications using all cells to generate 'normal' control 61 | #identify chromosome-level amplifications 62 | candidate_cnvs<-identifyCNVClusters(scData_collapse,median_iqr,useDummyCells = TRUE,propDummy=0.25,minMix=0.01,deltaMean = 0.03,deltaBIC2 = 0.25,bicMinimum = 0.1, subsetSize=600,fakeCellSD = 0.08, uncertaintyCutoff = 0.55,summaryFunction=summaryFunction,maxClust = 4,mergeCutoff = 3,IQRCutoff= 0.2,medianQuantileCutoff = 0.4) 63 | #cleanup step 64 | candidate_cnvs_clean<-clusterCNV(initialResultList = candidate_cnvs,medianIQR = candidate_cnvs[[3]],minDiff=1.5) #= 1.5) 65 | #final results and annotation 66 | final_cnv_list<-annotateCNV4(candidate_cnvs_clean, saveOutput=TRUE,outputSuffix = "clean_cnv",sdCNV = 0.5,filterResults=TRUE,filterRange=0.8) 67 | 68 | 69 | 70 | #OPTION 2: automatically identify non-neoplastic cells and use these for control 71 | #note: still somewhat experimental - use if the copy number calls from A don't make sense (e.g. baseline appears incorrect) 72 | #works best if tumor cellularity is <90% (very small populations of non-neoplastic cells may get missed) 73 | #run this after filterCells 74 | #this generates a heatmap in your working directory - check it and adjust parameters as needed 75 | #default parameters: estimatedCellularity=0.8,nmfComponents=5,outputHeatmap=TRUE,cutHeight=0.6 76 | #change cutHeight (height at which target dendrogram is cut) and nmfComponents as necessary to improve segeregation of clusters in your sample 77 | 78 | nmf_results<-identifyNonNeoplastic(scData_collapse,methodHclust="ward.D",cutHeight = 0.4) 79 | #?identifyNonNeoplastic 80 | write.table(x=rownames_to_column(data.frame(nmf_results$cellAssigns),var="Barcode"),file=str_c(scCNVCaller$locPrefix,scCNVCaller$outPrefix,"_nmf_clusters.csv"),quote=FALSE,row.names = FALSE,sep=",") 81 | print(paste("Normal cluster is: ",nmf_results$clusterNormal)) 82 | 83 | #ALTERNATE METHOD FOR CNV CALLING (with normal cells as background) 84 | #you can use normals identified by NMF or known/suspected normals in the sample to refine the CNV calls (i.e. as a vector of barcode strings) 85 | #compute central tendencies based on normal cells only 86 | median_iqr <- computeCenters(scData_collapse %>% select(chrom,nmf_results$normalBarcodes),summaryFunction=summaryFunction) 87 | #setting medianQuantileCutoff to -1 and feeding non-neoplastic barcodes in as normalCells can improve accuracy of CNV calls 88 | candidate_cnvs<-identifyCNVClusters(scData_collapse,median_iqr,useDummyCells = TRUE,propDummy=0.25,minMix=0.01,deltaMean = 0.03,deltaBIC2 = 0.25,bicMinimum = 0.1, subsetSize=800,fakeCellSD = 0.09, uncertaintyCutoff = 0.65,summaryFunction=summaryFunction,maxClust = 4,mergeCutoff = 3,IQRCutoff = 0.25,medianQuantileCutoff = -1,normalCells=nmf_results$normalBarcodes) 89 | candidate_cnvs_clean<-clusterCNV(initialResultList = candidate_cnvs,medianIQR = candidate_cnvs[[3]],minDiff=1.0) #= 1.5) 90 | 91 | #to save this data you can use annotateCNV4 as per usual 92 | final_cnv_list<-annotateCNV4(candidate_cnvs_clean, saveOutput=TRUE,outputSuffix = "clean_cnv",sdCNV = 0.6,filterResults=TRUE,filterRange=0.4) 93 | 94 | #the other option is to use annotateCNV4B and feed in the normalBarcodes - this will set the "normal" population to 2 -- if the data is noisy it may lead to false positives so use with caution 95 | #you may also use this version if you have a list of normal barcodes generated elsewhere 96 | final_cnv_list<-annotateCNV4B(candidate_cnvs_clean, nmf_results$normalBarcodes, saveOutput=TRUE,outputSuffix = "clean_cnv_b2",sdCNV = 0.6,filterResults=TRUE,filterRange=0.4,minAlteredCellProp = 0.5) 97 | 98 | #PART 2B: smoothing CNV calls with clusters 99 | #data smoothing: can provide CNV as list or as an input file (CSV) 100 | smoothedCNVList<-smoothClusters(scDataSampClusters,inputCNVList = final_cnv_list[[3]],percentPositive = 0.4,removeEmpty = FALSE) 101 | 102 | 103 | #PART 3: identify double minutes / amplifications 104 | #note: this is slow, and may take ~5 minutes 105 | #if very large dataset, may run on subset of the data to estimate the amplifications in distinct clusters 106 | #option to compile this code 107 | library(compiler) 108 | dmRead<-cmpfun(identifyDoubleMinutes) 109 | # 110 | #minThreshold is a time-saving option that doesn't call changepoints on any cell with a maximum Z score less than 4 - you can adjust this to adjust sensitivity of double minute calls (note - lower value = slower) 111 | dm_candidates<-dmRead(scData_k_norm,minCells=100,qualityCutoff2 = 100,minThreshold = 4) 112 | 113 | write.table(x=dm_candidates,file=str_c(scCNVCaller$locPrefix,scCNVCaller$outPrefix,"samp_dm.csv"),quote=FALSE,row.names = FALSE,sep=",") 114 | 115 | 116 | #PART 4: 117 | #CALLING SEGMENTAL LOSSES/GAINS (on datasets with NMF-detected normals only) 118 | altered_segments<-getAlteredSegments(scData_k_norm,nmf_results,lossThreshold = 0.6) 119 | #output here are chromosomal segments and relative estimated copy number 120 | write.table(x=altered_segments,file=str_c(scCNVCaller$locPrefix,scCNVCaller$outPrefix,"samp_segments.csv"),quote=FALSE,row.names = FALSE,sep=",") 121 | 122 | 123 | #PART 5: quantify cycling cells 124 | #this uses the signal from a particular chromosome (we use chromosome X as typically not altered in our samples) to identify 2N versus 4N DNA content within cells 125 | #if there is a known alteration in X in your samples, try using a different chromosome 126 | barcodeCycling<-estimateCellCycleFraction(scData,sampName="sample",cutoff=1000) 127 | #take max as 128 | write.table(barcodeCycling[order(names(barcodeCycling))]==max(barcodeCycling),file=str_c(scCNVCaller$locPrefix,scCNVCaller$outPrefix,"_cycling_cells.tsv"),sep="\t",quote=FALSE,row.names=TRUE,col.names=FALSE) 129 | 130 | -------------------------------------------------------------------------------- /data/scDataSamp.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spcdot/CopyscAT/e785ed313cdcb64cf2f153c9e6cddf7f8351829d/data/scDataSamp.RData -------------------------------------------------------------------------------- /data/scDataSamp.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spcdot/CopyscAT/e785ed313cdcb64cf2f153c9e6cddf7f8351829d/data/scDataSamp.rda -------------------------------------------------------------------------------- /data/scDataSampClusters.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spcdot/CopyscAT/e785ed313cdcb64cf2f153c9e6cddf7f8351829d/data/scDataSampClusters.rda -------------------------------------------------------------------------------- /gpl_license.txt: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /hg19_references/hg19_chrom_sizes.tsv: -------------------------------------------------------------------------------- 1 | chr1 249250621 2 | chr2 243199373 3 | chr3 198022430 4 | chr4 191154276 5 | chr5 180915260 6 | chr6 171115067 7 | chr7 159138663 8 | chr8 146364022 9 | chr9 141213431 10 | chr10 135534747 11 | chr11 135006516 12 | chr12 133851895 13 | chr13 115169878 14 | chr14 107349540 15 | chr15 102531392 16 | chr16 90354753 17 | chr17 81195210 18 | chr18 78077248 19 | chr19 59128983 20 | chr20 63025520 21 | chr21 48129895 22 | chr22 51304566 23 | chrX 155270560 24 | chrY 59373566 25 | chrM 16571 26 | -------------------------------------------------------------------------------- /hg38_references/hg38_1e+06_cytoband_densities_granges.tsv: -------------------------------------------------------------------------------- 1 | chr1 1 1000000 p 2 | chr1 1000001 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| chrX 61000001 62000000 cen 2950 | chrX 62000001 63000000 cen 2951 | chrX 63000001 64000000 cen 2952 | chrX 64000001 65000000 q 2953 | chrX 65000001 66000000 q 2954 | chrX 66000001 67000000 q 2955 | chrX 67000001 68000000 q 2956 | chrX 68000001 69000000 q 2957 | chrX 69000001 70000000 q 2958 | chrX 70000001 71000000 q 2959 | chrX 71000001 72000000 q 2960 | chrX 72000001 73000000 q 2961 | chrX 73000001 74000000 q 2962 | chrX 74000001 75000000 q 2963 | chrX 75000001 76000000 q 2964 | chrX 76000001 77000000 q 2965 | chrX 77000001 78000000 q 2966 | chrX 78000001 79000000 q 2967 | chrX 79000001 80000000 q 2968 | chrX 80000001 81000000 q 2969 | chrX 81000001 82000000 q 2970 | chrX 82000001 83000000 q 2971 | chrX 83000001 84000000 q 2972 | chrX 84000001 85000000 q 2973 | chrX 85000001 86000000 q 2974 | chrX 86000001 87000000 q 2975 | chrX 87000001 88000000 q 2976 | chrX 88000001 89000000 q 2977 | chrX 89000001 90000000 q 2978 | chrX 90000001 91000000 q 2979 | chrX 91000001 92000000 q 2980 | chrX 92000001 93000000 q 2981 | chrX 93000001 94000000 q 2982 | chrX 94000001 95000000 q 2983 | chrX 95000001 96000000 q 2984 | chrX 96000001 97000000 q 2985 | chrX 97000001 98000000 q 2986 | chrX 98000001 99000000 q 2987 | chrX 99000001 100000000 q 2988 | chrX 100000001 101000000 q 2989 | chrX 101000001 102000000 q 2990 | chrX 102000001 103000000 q 2991 | chrX 103000001 104000000 q 2992 | chrX 104000001 105000000 q 2993 | chrX 105000001 106000000 q 2994 | chrX 106000001 107000000 q 2995 | chrX 107000001 108000000 q 2996 | chrX 108000001 109000000 q 2997 | chrX 109000001 110000000 q 2998 | chrX 110000001 111000000 q 2999 | chrX 111000001 112000000 q 3000 | chrX 112000001 113000000 q 3001 | chrX 113000001 114000000 q 3002 | chrX 114000001 115000000 q 3003 | chrX 115000001 116000000 q 3004 | chrX 116000001 117000000 q 3005 | chrX 117000001 118000000 q 3006 | chrX 118000001 119000000 q 3007 | chrX 119000001 120000000 q 3008 | chrX 120000001 121000000 q 3009 | chrX 121000001 122000000 q 3010 | chrX 122000001 123000000 q 3011 | chrX 123000001 124000000 q 3012 | chrX 124000001 125000000 q 3013 | chrX 125000001 126000000 q 3014 | chrX 126000001 127000000 q 3015 | chrX 127000001 128000000 q 3016 | chrX 128000001 129000000 q 3017 | chrX 129000001 130000000 q 3018 | chrX 130000001 131000000 q 3019 | chrX 131000001 132000000 q 3020 | chrX 132000001 133000000 q 3021 | chrX 133000001 134000000 q 3022 | chrX 134000001 135000000 q 3023 | chrX 135000001 136000000 q 3024 | chrX 136000001 137000000 q 3025 | chrX 137000001 138000000 q 3026 | chrX 138000001 139000000 q 3027 | chrX 139000001 140000000 q 3028 | chrX 140000001 141000000 q 3029 | chrX 141000001 142000000 q 3030 | chrX 142000001 143000000 q 3031 | chrX 143000001 144000000 q 3032 | chrX 144000001 145000000 q 3033 | chrX 145000001 146000000 q 3034 | chrX 146000001 147000000 q 3035 | chrX 147000001 148000000 q 3036 | chrX 148000001 149000000 q 3037 | chrX 149000001 150000000 q 3038 | chrX 150000001 151000000 q 3039 | chrX 151000001 152000000 q 3040 | chrX 152000001 153000000 q 3041 | chrX 153000001 154000000 q 3042 | chrX 154000001 155000000 q 3043 | chrX 155000001 156000000 q 3044 | chrX 156000001 156040895 q 3045 | chrY 1 1000000 p 3046 | chrY 1000001 2000000 p 3047 | chrY 2000001 3000000 p 3048 | chrY 3000001 4000000 p 3049 | chrY 4000001 5000000 p 3050 | chrY 5000001 6000000 p 3051 | chrY 6000001 7000000 p 3052 | chrY 7000001 8000000 p 3053 | chrY 8000001 9000000 p 3054 | chrY 9000001 10000000 p 3055 | chrY 10000001 11000000 p 3056 | chrY 11000001 12000000 q 3057 | chrY 12000001 13000000 q 3058 | chrY 13000001 14000000 q 3059 | chrY 14000001 15000000 q 3060 | chrY 15000001 16000000 q 3061 | chrY 16000001 17000000 q 3062 | chrY 17000001 18000000 q 3063 | chrY 18000001 19000000 q 3064 | chrY 19000001 20000000 q 3065 | chrY 20000001 21000000 q 3066 | chrY 21000001 22000000 q 3067 | chrY 22000001 23000000 q 3068 | chrY 23000001 24000000 q 3069 | chrY 24000001 25000000 q 3070 | chrY 25000001 26000000 q 3071 | chrY 26000001 27000000 q 3072 | chrY 27000001 28000000 q 3073 | chrY 28000001 29000000 q 3074 | chrY 29000001 30000000 q 3075 | chrY 30000001 31000000 q 3076 | chrY 31000001 32000000 q 3077 | chrY 32000001 33000000 q 3078 | chrY 33000001 34000000 q 3079 | chrY 34000001 35000000 q 3080 | chrY 35000001 36000000 q 3081 | chrY 36000001 37000000 q 3082 | chrY 37000001 38000000 q 3083 | chrY 38000001 39000000 q 3084 | chrY 39000001 40000000 q 3085 | chrY 40000001 41000000 q 3086 | chrY 41000001 42000000 q 3087 | chrY 42000001 43000000 q 3088 | chrY 43000001 44000000 q 3089 | chrY 44000001 45000000 q 3090 | chrY 45000001 46000000 q 3091 | chrY 46000001 47000000 q 3092 | chrY 47000001 48000000 q 3093 | chrY 48000001 49000000 q 3094 | chrY 49000001 50000000 q 3095 | chrY 50000001 51000000 q 3096 | chrY 51000001 52000000 q 3097 | chrY 52000001 53000000 q 3098 | chrY 53000001 54000000 q 3099 | chrY 54000001 55000000 q 3100 | chrY 55000001 56000000 q 3101 | chrY 56000001 57000000 q 3102 | chrY 57000001 57227415 q 3103 | chrM 1 16569 - 3104 | -------------------------------------------------------------------------------- /hg38_references/hg38_chrom_sizes.tsv: -------------------------------------------------------------------------------- 1 | chr1 248956422 2 | chr2 242193529 3 | chr3 198295559 4 | chr4 190214555 5 | chr5 181538259 6 | chr6 170805979 7 | chr7 159345973 8 | chr8 145138636 9 | chr9 138394717 10 | chr10 133797422 11 | chr11 135086622 12 | chr12 133275309 13 | chr13 114364328 14 | chr14 107043718 15 | chr15 101991189 16 | chr16 90338345 17 | chr17 83257441 18 | chr18 80373285 19 | chr19 58617616 20 | chr20 64444167 21 | chr21 46709983 22 | chr22 50818468 23 | chrX 156040895 24 | chrY 57227415 25 | chrM 16569 26 | -------------------------------------------------------------------------------- /man/annotateCNV3.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{annotateCNV3} 4 | \alias{annotateCNV3} 5 | \title{annotateCNV3} 6 | \usage{ 7 | annotateCNV3(cnvResults, saveOutput = TRUE, maxClust2 = 4, outputSuffix = "_1") 8 | } 9 | \arguments{ 10 | \item{cnvResults}{output of filtered CNVs (set of lists)} 11 | 12 | \item{saveOutput}{save output to a file on disk} 13 | 14 | \item{maxClust2}{maximum # of clusters} 15 | 16 | \item{outputSuffix}{output suffix to save files} 17 | } 18 | \description{ 19 | Annotates the filtered CNV calls and saves the results, and estimates absolute copy numbers 20 | } 21 | \examples{ 22 | annotateCNV3(cleanCNV,saveOutput=TRUE,outputSuffix="_nolog") 23 | } 24 | \keyword{CNV} 25 | \keyword{output} 26 | -------------------------------------------------------------------------------- /man/annotateCNV4.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{annotateCNV4} 4 | \alias{annotateCNV4} 5 | \title{annotateCNV4} 6 | \usage{ 7 | annotateCNV4( 8 | cnvResults, 9 | saveOutput = TRUE, 10 | maxClust2 = 4, 11 | outputSuffix = "_1", 12 | sdCNV = 0.6, 13 | filterResults = TRUE, 14 | filterRange = 0.8, 15 | minAlteredCells = 40 16 | ) 17 | } 18 | \arguments{ 19 | \item{cnvResults}{output of filtered CNVs (set of lists)} 20 | 21 | \item{saveOutput}{save output to a file on disk} 22 | 23 | \item{maxClust2}{maximum # of clusters} 24 | 25 | \item{outputSuffix}{output suffix to save files} 26 | 27 | \item{sdCNV}{sd for CNV copy number estimation} 28 | 29 | \item{filterResults}{filter results to remove likely unaltered chromosomes} 30 | 31 | \item{filterRange}{copy number range minimum} 32 | 33 | \item{minAlteredCells}{filtering parameter - remove all alterations with a smallest group less than X cells (default is 40)} 34 | } 35 | \description{ 36 | Annotates the filtered CNV calls and saves the results, and estimates absolute copy numbers 37 | } 38 | \examples{ 39 | annotateCNV4(cleanCNV,saveOutput=TRUE,outputSuffix="_nolog",sdCNV=0.5) 40 | } 41 | \keyword{CNV} 42 | \keyword{output} 43 | -------------------------------------------------------------------------------- /man/annotateCNV4B.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{annotateCNV4B} 4 | \alias{annotateCNV4B} 5 | \title{annotateCNV4B} 6 | \usage{ 7 | annotateCNV4B( 8 | cnvResults, 9 | expectedNormals, 10 | saveOutput = TRUE, 11 | maxClust2 = 4, 12 | outputSuffix = "_1", 13 | sdCNV = 0.6, 14 | filterResults = TRUE, 15 | filterRange = 0.8, 16 | minAlteredCellProp = 0.75 17 | ) 18 | } 19 | \arguments{ 20 | \item{cnvResults}{output of filtered CNVs (set of lists)} 21 | 22 | \item{expectedNormals}{list of normal barcodes} 23 | 24 | \item{saveOutput}{save output to a file on disk} 25 | 26 | \item{maxClust2}{maximum # of clusters} 27 | 28 | \item{outputSuffix}{output suffix to save files} 29 | 30 | \item{sdCNV}{sd for CNV copy number estimation} 31 | 32 | \item{filterResults}{filter results to remove likely unaltered chromosomes} 33 | 34 | \item{filterRange}{copy number range minimum} 35 | 36 | \item{minAlteredCellProp}{filtering parameter - proportion of normal cell population that should be unaltered, default is 0.75} 37 | } 38 | \description{ 39 | Annotates the filtered CNV calls and saves the results, and estimates absolute copy numbers 40 | unlike the regular version, this uses predicted normal cells to set the normal values to 2 41 | } 42 | \keyword{CNV} 43 | \keyword{output} 44 | -------------------------------------------------------------------------------- /man/annotateLosses.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{annotateLosses} 4 | \alias{annotateLosses} 5 | \title{AnnotateLosses} 6 | \usage{ 7 | annotateLosses(lossFile) 8 | } 9 | \arguments{ 10 | \item{lossFile}{the file for LOH putative reginos} 11 | } 12 | \description{ 13 | This function allows you to annotate loss regions 14 | } 15 | \keyword{Losses} 16 | -------------------------------------------------------------------------------- /man/clusterCNV.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{clusterCNV} 4 | \alias{clusterCNV} 5 | \title{clusterCNVs} 6 | \usage{ 7 | clusterCNV(initialResultList, medianIQR, maxClust = 4, minDiff = 0.25) 8 | } 9 | \arguments{ 10 | \item{initialResultList}{output list generated by identifyCNVClusters} 11 | 12 | \item{medianIQR}{median and IQR list of sample} 13 | 14 | \item{maxClust}{maximum # of clusters fed into identifyCNVClusters} 15 | 16 | \item{minDiff}{minimum Z-score difference between clusters} 17 | } 18 | \description{ 19 | Cleans the putative CNV calls 20 | } 21 | \keyword{CNV} 22 | -------------------------------------------------------------------------------- /man/collapseChrom3N.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{collapseChrom3N} 4 | \alias{collapseChrom3N} 5 | \title{collapseChrom3N} 6 | \usage{ 7 | collapseChrom3N( 8 | inputMatrix, 9 | minimumSegments = 40, 10 | summaryFunction = cutAverage, 11 | logTrans = FALSE, 12 | binExpand = 1, 13 | minimumChromValue = 2, 14 | tssEnrich = 5, 15 | logBase = 2, 16 | minCPG = 300, 17 | powVal = 0.73 18 | ) 19 | } 20 | \arguments{ 21 | \item{tssEnrich}{multiply CpG by weighting factor (default 1)} 22 | 23 | \item{logBase}{log base to apply if doing a log weighting of the CpG scaling factor} 24 | 25 | \item{minCPG}{keep only regions with at least CpG score of X (300)} 26 | 27 | \item{powVal}{scaling factor adjustment (cpg^powVal, 0.7-0.75 works well for hg38 and hg19)} 28 | 29 | \item{inputMatrix:}{normalised matrix} 30 | 31 | \item{summaryFunction:}{function to use to summarise signal} 32 | 33 | \item{logTrans:}{whether matrix is log-transformed} 34 | 35 | \item{binExpand:}{whether to merge adjacent bins, if so how many (merges if > 1)} 36 | 37 | \item{minimumChromValue:}{cutoff to keep a chromosome} 38 | } 39 | \description{ 40 | This function summarises signal across chromosomes and normalises using the CpG content 41 | } 42 | \keyword{CNV} 43 | -------------------------------------------------------------------------------- /man/computeCenters.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{computeCenters} 4 | \alias{computeCenters} 5 | \title{computeCenters} 6 | \usage{ 7 | computeCenters(inputMatrix, summaryFunction = cutAverage) 8 | } 9 | \arguments{ 10 | \item{inputMatrix}{- normalised, filtered matrix} 11 | 12 | \item{summaryFunction}{- summary function to use (defaults to cutAverage)} 13 | } 14 | \description{ 15 | Recompute range values after normalization and filtering. Returns a list containing values with summary values and IQRs 16 | } 17 | \examples{ 18 | listCenters <- computeCenters(inputMatrix,cutAverage) 19 | } 20 | \keyword{CNV} 21 | \keyword{median} 22 | -------------------------------------------------------------------------------- /man/cutAverage.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{cutAverage} 4 | \alias{cutAverage} 5 | \title{cutAverage} 6 | \usage{ 7 | cutAverage(inputVector) 8 | } 9 | \arguments{ 10 | \item{inputVector}{- vector of numbers} 11 | } 12 | \description{ 13 | Averages regions of vector between prespecified quantiles 14 | } 15 | \keyword{average`} 16 | -------------------------------------------------------------------------------- /man/estimateCellCycleFraction.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{estimateCellCycleFraction} 4 | \alias{estimateCellCycleFraction} 5 | \title{estimateCellCycleFraction 6 | Detect cycling cells using chromosome X 7 | Returns list of barcodes and cluster assignments, and list of non-neoplastic cells} 8 | \usage{ 9 | estimateCellCycleFraction( 10 | inputMatrix, 11 | sampName, 12 | cutoff = 10000, 13 | maxG = 4, 14 | logTrans = FALSE, 15 | modelName = "E", 16 | chromToUse = "chrX" 17 | ) 18 | } 19 | \arguments{ 20 | \item{inputMatrix}{Matrix (unfiltered before normalization only)} 21 | 22 | \item{cutoff}{Minimum cutoff to discard noisy cell} 23 | 24 | \item{maxG}{maximum number of G components to use (default is 4)} 25 | 26 | \item{logTrans}{log transform data (default is faulse)} 27 | 28 | \item{modelName}{Model option for MCLUST (default is E)} 29 | 30 | \item{chromToUse}{Chromosome to use for normalization (default is chrX)} 31 | } 32 | \description{ 33 | estimateCellCycleFraction 34 | Detect cycling cells using chromosome X 35 | Returns list of barcodes and cluster assignments, and list of non-neoplastic cells 36 | } 37 | \keyword{CNV} 38 | \keyword{cell} 39 | \keyword{cycle} 40 | -------------------------------------------------------------------------------- /man/filterCells.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{filterCells} 4 | \alias{filterCells} 5 | \title{filterCells} 6 | \usage{ 7 | filterCells(inputMatrix, minimumSegments = 40, minDensity = 0, signalSDcut = 2) 8 | } 9 | \arguments{ 10 | \item{inputMatrix}{- collapsed, normalised matrix} 11 | 12 | \item{minimumSegments}{the minimum segments required to keep a cell} 13 | 14 | \item{minDensity}{minimum segment density to consider useful (defaults to zero)} 15 | 16 | \item{signalSDcut}{Number of standard deviations average signal of a cell must be from an average per-cell signal to be considered an outlier (to identify putative doublets; default: 2)} 17 | } 18 | \description{ 19 | This function filters the collapsed chromosome signal density matrix based on predefined parameters (minimum non-zero chromosome segments) 20 | } 21 | \examples{ 22 | filterCells(inputMatrix,minimumSegments=41) 23 | } 24 | \keyword{CNV} 25 | \keyword{filter} 26 | -------------------------------------------------------------------------------- /man/generateReferences.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{generateReferences} 4 | \alias{generateReferences} 5 | \title{generateReferences 6 | Generate reference files from UCSC for genome of interest 7 | Returns CPG, chromosome size and cytoband data into target directory} 8 | \usage{ 9 | generateReferences( 10 | genomeObject, 11 | genomeText = "hg38", 12 | tileWidth = 1e+06, 13 | outputDir = "~" 14 | ) 15 | } 16 | \arguments{ 17 | \item{genomeObject}{BSgenome object of interest} 18 | 19 | \item{genomeText}{Shorthand for genome in UCSC (defaults to hg38)} 20 | 21 | \item{tileWidth}{Width of tiles - (default: 1e6)} 22 | 23 | \item{outputDir}{Output directory to save reference files to (default: ~)} 24 | } 25 | \description{ 26 | generateReferences 27 | Generate reference files from UCSC for genome of interest 28 | Returns CPG, chromosome size and cytoband data into target directory 29 | } 30 | \keyword{CNV} 31 | \keyword{reference} 32 | -------------------------------------------------------------------------------- /man/getAlteredSegments.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{getAlteredSegments} 4 | \alias{getAlteredSegments} 5 | \title{getAlteredSegments} 6 | \usage{ 7 | getAlteredSegments( 8 | inputMatrix, 9 | clusterResults, 10 | minSeglen = 3, 11 | lossThreshold = 0.5, 12 | gainThreshold = 1.6, 13 | minCutoff = 50, 14 | rangeThreshold = 0.4 15 | ) 16 | } 17 | \arguments{ 18 | \item{inputMatrix}{Unprocessed and normalized input matrix} 19 | 20 | \item{clusterResults}{Result list of identifyNonNeoplastic} 21 | 22 | \item{minSeglen}{Minimum segment length for changepoint analysis} 23 | 24 | \item{lossThreshold}{fold change threshold to call something a relative loss (default 0.5)} 25 | 26 | \item{gainThreshold}{fold change threshold to call something a relative gain (default 1.6)} 27 | 28 | \item{minCutoff}{Number of altered cells in smallest group to keep a putative alteration} 29 | 30 | \item{rangeThreshold}{threshold filter between max and min fold change to keep an alteration (Default 0.4)} 31 | } 32 | \description{ 33 | This function relies on pre-determined 'normal' cell set to compute smaller regions of altered accessibility 34 | } 35 | \keyword{CNV} 36 | \keyword{comparison} 37 | -------------------------------------------------------------------------------- /man/getDoubleMinutes.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{getDoubleMinutes} 4 | \alias{getDoubleMinutes} 5 | \title{getDoubleMinutes} 6 | \usage{ 7 | getDoubleMinutes( 8 | inputMatrix, 9 | targetCell, 10 | doPlot = FALSE, 11 | penalty_type = "SIC", 12 | doLosses = FALSE, 13 | peakCutoff = 5, 14 | lossCutoff = -1, 15 | minThreshold = 4 16 | ) 17 | } 18 | \arguments{ 19 | \item{minThreshold}{set threshold of Z score to run changepoint analysis; higher value = less sensitive but faster} 20 | } 21 | \description{ 22 | This function allows you to express your love of cats. 23 | } 24 | \keyword{double} 25 | \keyword{minutes} 26 | -------------------------------------------------------------------------------- /man/getLOHRegions.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{getLOHRegions} 4 | \alias{getLOHRegions} 5 | \title{getLOHRegions} 6 | \usage{ 7 | getLOHRegions( 8 | inputMatrixIn, 9 | lossCutoff = (-0.25), 10 | uncertaintyCutLoss = 0.5, 11 | diffThreshold = 0.9, 12 | minLength = 3e+06, 13 | minSeg = 3, 14 | lossCutoffCells = 100, 15 | targetFun = IQR, 16 | quantileLimit = 0.3, 17 | cpgCutoff = 0, 18 | meanThreshold = 4, 19 | dummyQuantile = 0.5, 20 | dummyPercentile = 0.2, 21 | dummySd = 0.2 22 | ) 23 | } 24 | \arguments{ 25 | \item{lossCutoff}{- minimum Z score for losses (default is -0.25)} 26 | 27 | \item{uncertaintyCutLoss}{- maximum uncertainty to accept a cell assignment} 28 | 29 | \item{diffThreshold}{- difference threshold between bins} 30 | 31 | \item{minLength}{- minimum length of a LOH region} 32 | 33 | \item{minSeg}{- minimum number of bins for changepoint analysis} 34 | 35 | \item{lossCutoffCells}{- minimum number of cells to accept a LOH (default is 100)} 36 | 37 | \item{targetFun}{- target function to identify LOH regions} 38 | 39 | \item{quantileLimit}{quantile of signal per cell population to use to identify losses in changepoints (default is 0.3)} 40 | 41 | \item{inputMatrix}{- normalised input fragment count matrix} 42 | 43 | \item{signalBoost}{- value to add prior to logtransforming signal} 44 | 45 | \item{lossCutoffReads}{minimum number of cells showing alteration to keep in final list (filter)} 46 | } 47 | \description{ 48 | Calls putative regions of LOH 49 | } 50 | \keyword{CNV} 51 | \keyword{LOH} 52 | -------------------------------------------------------------------------------- /man/graphCNVDistribution.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{graphCNVDistribution} 4 | \alias{graphCNVDistribution} 5 | \title{graphCNVDistribution} 6 | \usage{ 7 | graphCNVDistribution(inputMatrix, outputSuffix = "_all") 8 | } 9 | \arguments{ 10 | \item{inputMatrix}{(the normalised matrix)} 11 | 12 | \item{outputSuffix}{suffix to append to pdf output file} 13 | } 14 | \description{ 15 | Makes graph of normalised, collapsed fragment data 16 | } 17 | \examples{ 18 | graphCNVDistribution(inputMatrix,outputSuffix="unfiltered") 19 | } 20 | \keyword{output} 21 | -------------------------------------------------------------------------------- /man/identifyCNVClusters.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{identifyCNVClusters} 4 | \alias{identifyCNVClusters} 5 | \title{identifyCNVClusters} 6 | \usage{ 7 | identifyCNVClusters( 8 | inputMatrix, 9 | median_iqr, 10 | useDummyCells = TRUE, 11 | propDummy = 0.25, 12 | deltaMean = 0.03, 13 | subsetSize = 500, 14 | fakeCellSD = 0.1, 15 | summaryFunction = cutAverage, 16 | minDiff = 0.25, 17 | deltaBIC2 = 0.25, 18 | bicMinimum = 0.1, 19 | minMix = 0.3, 20 | uncertaintyCutoff = 0.55, 21 | mergeCutoff = 3, 22 | summarySuffix = "", 23 | shrinky = 0, 24 | maxClust = 4, 25 | IQRCutoff = 0.25, 26 | medianQuantileCutoff = 0.3, 27 | normalCells = NULL 28 | ) 29 | } 30 | \arguments{ 31 | \item{inputMatrix}{input matrix after collapsing and filtering steps} 32 | 33 | \item{median_iqr}{matrix of medians and IQRs} 34 | 35 | \item{useDummyCells}{use pseudodiploid control} 36 | 37 | \item{propDummy}{pseudodiploid control as proportion of normal cells (0.25 = 25 percent)} 38 | 39 | \item{deltaMean}{minimum difference between clusters} 40 | 41 | \item{subsetSize}{subset of cells to use for initial computation (useful in large samples)} 42 | 43 | \item{fakeCellSD}{SD of dummy cells as proportion of range of normal cells (0.10 is default, 0.10-0.20 works best in most cases)} 44 | 45 | \item{summaryFunction}{summarising function for chromosome arms (default is trimmed average)} 46 | } 47 | \description{ 48 | Uses Gaussian decomposition to analyse a filtered, normalised input matrix and call putative CNVs 49 | } 50 | -------------------------------------------------------------------------------- /man/identifyDoubleMinutes.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{identifyDoubleMinutes} 4 | \alias{identifyDoubleMinutes} 5 | \title{identifyDoubleMinutes} 6 | \usage{ 7 | identifyDoubleMinutes( 8 | inputMatrix, 9 | minCells = 100, 10 | qualityCutoff2 = 100, 11 | peakCutoff = 5, 12 | lossCutoff = -1, 13 | doPlots = FALSE, 14 | imageNumber = 1000, 15 | logTrans = FALSE, 16 | cpgTransform = FALSE, 17 | doLosses = FALSE, 18 | minThreshold = 4 19 | ) 20 | } 21 | \arguments{ 22 | \item{inputMatrix}{normalized input matrix} 23 | 24 | \item{minCells}{minimum cells to retain a DM} 25 | 26 | \item{qualityCutoff2}{minimum cells to retain DM (filter 2)} 27 | 28 | \item{peakCutoff}{Z score minimum to retain a putative DM/amplification} 29 | 30 | \item{doPlots}{Plot occasional cells as QC} 31 | 32 | \item{imageNumber}{Plot every Nth cell (use in conjunction with doPlots = TRUE)} 33 | 34 | \item{minThreshold}{Z-score maximum to continue processing cells (can save time in larger datasets; default 4)} 35 | } 36 | \description{ 37 | Identify double minutes on all chromosomes using changepoint analysis 38 | } 39 | \keyword{amplification} 40 | \keyword{double} 41 | \keyword{ecDNA} 42 | \keyword{minutes} 43 | -------------------------------------------------------------------------------- /man/identifyNonNeoplastic.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{identifyNonNeoplastic} 4 | \alias{identifyNonNeoplastic} 5 | \title{identifyNonNeoplastic 6 | Detect neoplastic vs neoplastic cells using unsupervised clustering 7 | Returns list of barcodes and cluster assignments, and list of non-neoplastic cells} 8 | \usage{ 9 | identifyNonNeoplastic( 10 | inputMatrix, 11 | estimatedCellularity = 0.8, 12 | nmfComponents = 5, 13 | outputHeatmap = TRUE, 14 | cutHeight = 0.6, 15 | methodHclust = "ward.D" 16 | ) 17 | } 18 | \arguments{ 19 | \item{inputMatrix}{Matrix (after filterCells is called)} 20 | 21 | \item{estimatedCellularity}{Expected cellularity of tumour (default 0.8)} 22 | 23 | \item{nmfComponents}{# of components to use for NMF/SVD} 24 | 25 | \item{outputHeatmap}{Output a heatmap of the clustering (default is yes)} 26 | 27 | \item{cutHeight}{Cutting height as percent of max for dendrogram} 28 | 29 | \item{methodHclust}{Method for clustering (default is Ward.D)} 30 | } 31 | \description{ 32 | identifyNonNeoplastic 33 | Detect neoplastic vs neoplastic cells using unsupervised clustering 34 | Returns list of barcodes and cluster assignments, and list of non-neoplastic cells 35 | } 36 | \keyword{CNV} 37 | \keyword{clustering} 38 | -------------------------------------------------------------------------------- /man/initialiseEnvironment.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{initialiseEnvironment} 4 | \alias{initialiseEnvironment} 5 | \title{initialiseEnvironment} 6 | \usage{ 7 | initialiseEnvironment( 8 | genomeFile, 9 | cytobandFile, 10 | cpgFile, 11 | binSize = 1e+06, 12 | minFrags = 10000, 13 | cellSuffix = c("-1"), 14 | lowerTrim = 0.5, 15 | upperTrim = 0.8 16 | ) 17 | } 18 | \arguments{ 19 | \item{genomeFile}{Chromosome size file for genome of interest} 20 | 21 | \item{cytobandFile}{Processed cytoband file} 22 | 23 | \item{cpgFile}{Processed CpG island file} 24 | 25 | \item{binSize}{Size of chromosome bins (defaults to 1e6)} 26 | 27 | \item{minFrags}{Minimum # of fragments per cell (default 1e4)} 28 | 29 | \item{cellSuffix}{Suffix appended to cells} 30 | 31 | \item{lowerTrim}{Lower quantile trim for trimmed mean (default 0.5)} 32 | 33 | \item{upperTrim}{Upper quantile trim for trimmed mean (default 0.8)} 34 | } 35 | \description{ 36 | This function prepares and preloads the genome data required for CNV analysis. 37 | } 38 | \keyword{configuration} 39 | -------------------------------------------------------------------------------- /man/initializeStandards.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{initializeStandards} 4 | \alias{initializeStandards} 5 | \title{initializeStandards} 6 | \usage{ 7 | initializeStandards(chromSizeFile, cpgDataFile, cytobandFile) 8 | } 9 | \arguments{ 10 | \item{chromSizeFile}{- a tsv chrom sizes file} 11 | 12 | \item{cpgDataFile}{- a binned cpg density file (see documentation for how to prepare} 13 | 14 | \item{cytobandFile}{- a binned cytoband file for your genome of interest (see documentation for how to prepare)} 15 | } 16 | \description{ 17 | This function prepares and preloads the genome data required for CNV analysis. 18 | } 19 | \examples{ 20 | initializeStandards("hg38.chrom.sizes","hg38.cpg.1e6.bed","hg38.cytoband.1e6.bed" 21 | } 22 | \keyword{initialisation} 23 | -------------------------------------------------------------------------------- /man/makeFakeCells.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{makeFakeCells} 4 | \alias{makeFakeCells} 5 | \title{makeFakeCells} 6 | \usage{ 7 | makeFakeCells(x, num = 300, sd_fact = 0.1) 8 | } 9 | \arguments{ 10 | \item{x}{(mean of values)} 11 | 12 | \item{num}{(number of cells)} 13 | 14 | \item{sd_fact}{(scaling factor for cell SD)} 15 | } 16 | \description{ 17 | simulate a pseudodiploid control 18 | } 19 | -------------------------------------------------------------------------------- /man/normalizeMatrixN.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{normalizeMatrixN} 4 | \alias{normalizeMatrixN} 5 | \title{normalizeMatrixN} 6 | \usage{ 7 | normalizeMatrixN( 8 | inputMatrix, 9 | logNorm = FALSE, 10 | maxZero = 2000, 11 | imputeZeros = FALSE, 12 | blacklistProp = 0.8, 13 | priorCount = 1, 14 | blacklistCutoff = 100, 15 | dividingFactor = 1e+06, 16 | upperFilterQuantile = 0.95 17 | ) 18 | } 19 | \arguments{ 20 | \item{inputMatrix}{- processed fragment table from your input} 21 | 22 | \item{logNorm}{- whether to log normalize input file. default: FALSE} 23 | 24 | \item{maxZero}{- maximum # of zeros per cell after removal of uninformative regions} 25 | 26 | \item{imputeZeros}{- default to FALSE; will impute cells with medians - don't use it} 27 | 28 | \item{blacklistProp}{- proportion of cells that have zeros in an interval to consider that interval blacklisted/uninformative - lower = more stringency} 29 | 30 | \item{priorCount}{- use if log normalizing data - priorCount to add to fields before normalisation} 31 | 32 | \item{blacklistCutoff}{- minimum signal in a bin to count it as "empty" for the blacklist cutoff} 33 | 34 | \item{dividingFactor}{- deprecated} 35 | 36 | \item{upperFilterQuantile}{- exclude cells with reads above certain percentile (to avoid doublets)} 37 | } 38 | \description{ 39 | This function normalizes the input matrix. Do this first. 40 | } 41 | -------------------------------------------------------------------------------- /man/readInputTable.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{readInputTable} 4 | \alias{readInputTable} 5 | \title{readInputTable} 6 | \usage{ 7 | readInputTable(inputFile, sep = "\\t") 8 | 9 | readInputTable(inputFile, sep = "\\t") 10 | } 11 | \arguments{ 12 | \item{inputFile}{input count fragment matrix} 13 | 14 | \item{sep}{separator (defaults to tab)} 15 | } 16 | \description{ 17 | This function prepares and preloads the genome data required for CNV analysis. 18 | } 19 | \keyword{CNV} 20 | \keyword{files} 21 | \keyword{import} 22 | \keyword{initialisation} 23 | \keyword{loading} 24 | -------------------------------------------------------------------------------- /man/saveSummaryStats.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{saveSummaryStats} 4 | \alias{saveSummaryStats} 5 | \title{saveSummaryStats} 6 | \usage{ 7 | saveSummaryStats(outputSuffix = "_stats") 8 | } 9 | \arguments{ 10 | \item{outputSuffix}{suffix to append to output file names. defaults to _stats} 11 | } 12 | \description{ 13 | saves summary statistics about loaded files 14 | } 15 | \keyword{summary} 16 | -------------------------------------------------------------------------------- /man/scDataSamp.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{scDataSamp} 5 | \alias{scDataSamp} 6 | \title{Sample processed matrix for testing} 7 | \format{ 8 | data.frame with 3103 columns and 1371 rows 9 | } 10 | \source{ 11 | 1371 adult GBM cells 12 | } 13 | \usage{ 14 | scDataSamp 15 | } 16 | \description{ 17 | Sample processed matrix for testing 18 | } 19 | \keyword{datasets} 20 | -------------------------------------------------------------------------------- /man/scDataSampClusters.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{scDataSampClusters} 5 | \alias{scDataSampClusters} 6 | \title{Sample processed cluster data frame for testing} 7 | \format{ 8 | data.frame 9 | } 10 | \source{ 11 | adult GBM cells 12 | } 13 | \usage{ 14 | scDataSampClusters 15 | } 16 | \description{ 17 | Sample processed cluster data frame for testing 18 | } 19 | \keyword{datasets} 20 | -------------------------------------------------------------------------------- /man/scaleMatrix.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{scaleMatrix} 4 | \alias{scaleMatrix} 5 | \title{scaleMatrix} 6 | \usage{ 7 | scaleMatrix(inputMatrix, median_iqr_list, spread = FALSE) 8 | } 9 | \arguments{ 10 | \item{inputMatrix}{normalised, filtered matrix} 11 | 12 | \item{median_iqr_list}{list containing summary lists with median/trimmed mean and IQR (computed by computeCenters)} 13 | 14 | \item{spread}{return the list in Tidyverse "spread" format - for plotting} 15 | } 16 | \description{ 17 | Scales the normalised, filtered input matrix based on provided median and IQR data 18 | } 19 | \examples{ 20 | scaleMatrix(inputMatrix,medianIQR) 21 | } 22 | \keyword{CNV} 23 | \keyword{scale} 24 | -------------------------------------------------------------------------------- /man/scaleMatrixBins.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{scaleMatrixBins} 4 | \alias{scaleMatrixBins} 5 | \title{Scale matrix bins} 6 | \usage{ 7 | scaleMatrixBins(inputMatrix, binSizeRatio, inColumn) 8 | } 9 | \arguments{ 10 | \item{inputMatrix}{matrix of input stuff} 11 | 12 | \item{binSizeRatio}{ratio to expand by (2 = double bin size)} 13 | 14 | \item{inColumn}{input column} 15 | } 16 | \description{ 17 | Merges together adjacent windows 18 | } 19 | -------------------------------------------------------------------------------- /man/setOutputFile.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{setOutputFile} 4 | \alias{setOutputFile} 5 | \title{setOutputFile} 6 | \usage{ 7 | setOutputFile(outputDir, outputFileSuffix) 8 | } 9 | \arguments{ 10 | \item{outputDir}{Output directory} 11 | 12 | \item{outputFileSuffix}{Suffix to add to output files} 13 | } 14 | \description{ 15 | This function prepares and preloads the genome data required for CNV analysis. 16 | } 17 | \keyword{configuration} 18 | -------------------------------------------------------------------------------- /man/smoothClusters.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{smoothClusters} 4 | \alias{smoothClusters} 5 | \title{smoothClusters 6 | Read clusters from file and CNV calls from input or an external file 7 | Note: assumes files are comma delimited 8 | Returns a smoothed matrix} 9 | \usage{ 10 | smoothClusters( 11 | inputClusters, 12 | inputCNVList, 13 | inputCNVClusterFile = "", 14 | percentPositive = 0.5, 15 | removeEmpty = TRUE 16 | ) 17 | } 18 | \arguments{ 19 | \item{inputClusters}{input cluster matrix} 20 | 21 | \item{inputCNVList}{list of CNVs with copy number estimates (2 as normal)} 22 | 23 | \item{inputCNVClusterFile}{alternate option to specify external comma delimited file for CNV calls} 24 | 25 | \item{percentPositive}{minimum percentage of a cluster to use to call positive for CNV, default is 0.5 (50 percent)} 26 | } 27 | \description{ 28 | smoothClusters 29 | Read clusters from file and CNV calls from input or an external file 30 | Note: assumes files are comma delimited 31 | Returns a smoothed matrix 32 | } 33 | \keyword{CNV} 34 | \keyword{clusters} 35 | -------------------------------------------------------------------------------- /man/testOverlap.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/cnv_caller_functions_packaged.R 3 | \name{testOverlap} 4 | \alias{testOverlap} 5 | \title{Test overlap} 6 | \usage{ 7 | testOverlap(interval.1, interval.2) 8 | } 9 | \arguments{ 10 | \item{interval.1}{(text interval)} 11 | 12 | \item{interval.2}{(text interval)} 13 | } 14 | \description{ 15 | Tests overlap between two intervals 16 | Internal use 17 | } 18 | -------------------------------------------------------------------------------- /process_fragment_file.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # coding: utf-8 3 | 4 | #initialize module 5 | import sys, getopt 6 | import math 7 | import numpy as np 8 | import gzip 9 | 10 | #initialize variables 11 | #matrix of cells x regions 12 | cell_ids = {} 13 | #dictionary of cell barcodes with total counts per chr 14 | cell_counts = {} 15 | chrom_step = 1000000 16 | chrom_max = 0 17 | file_init = '' 18 | line1 = '' 19 | min_filter = 10000 20 | output_file = '' 21 | #chrom sizes file 22 | chrom_sizefile = '' 23 | #initialization 24 | try: 25 | opts, args = getopt.getopt(sys.argv[1:],"hi:o:b:f:g:",["help","ifile=","ofile=","binsize=","frags=","genome="]) 26 | #opts, args = getopt.getopt(sys.argv[1:],"h",["help"]) 27 | print("process_fragment_file",len(opts)) 28 | except getopt.GetoptError as err: 29 | print('process_fragment_file.py -i -o -b -f -g ') 30 | print(str(err),"process_fragment_file") 31 | sys.exit(2) 32 | for opt, arg in opts: 33 | print("process_fragment_file",opt) 34 | if opt in ("-h","help"): 35 | print('process_fragment_file.py -i -o -b -f -g ') 36 | sys.exit() 37 | elif opt in ("-i", "--ifile"): 38 | file_init = arg 39 | elif opt in ("-o", "--ofile"): 40 | output_file = arg 41 | elif opt in ("-b", "--binsize"): 42 | chrom_step = int(arg) 43 | elif opt in ("-f", "--frags"): 44 | min_filter = int(arg) 45 | elif opt in ("-g", "--genome"): 46 | chrom_sizefile = arg 47 | 48 | if len(opts)<5: 49 | print('process_fragment_file.py -i -o -b -f -g ') 50 | sys.exit(2) 51 | 52 | #load sizes 53 | chrom_sizes = {} 54 | def init_chrom_sizes(): 55 | chrom_size = {} 56 | with open(chrom_sizefile) as reader: 57 | line1 = reader.readline().rstrip() 58 | while line1 != '': 59 | line_split = line1.split('\t') 60 | chrom_size[line_split[0]]=line_split[1] 61 | line1 = reader.readline().rstrip() 62 | return chrom_size 63 | 64 | chrom_sizes = init_chrom_sizes() 65 | print(chrom_sizes) 66 | print("Initializing chromosomes") 67 | #initialize chromosome bins 68 | def init_chrom_list_all(): 69 | chrom_min = 0 70 | chrom_total_list = {} 71 | for chrom in chrom_sizes: 72 | #chrom_list_temp = {} 73 | chrom_max1 = int(chrom_sizes[chrom]) 74 | chrom_list_temp = np.zeros(int(chrom_max1 / chrom_step)+1) 75 | # chrom_list_temp[i*chrom_step] = 0 76 | chrom_total_list[chrom] = chrom_list_temp 77 | return chrom_total_list 78 | 79 | blank_list_all = init_chrom_list_all() 80 | 81 | 82 | print("Reading file") 83 | #read file 84 | lines_read = 0 85 | with gzip.open(file_init,'rt') as reader: 86 | for line1 in reader: 87 | #print(line1 + " " + str(len(cell_ids))) 88 | #remove header from new version of cellranger-atac 89 | if (line1.find('#')!=-1): 90 | print("commented header line detected...skipping") 91 | continue 92 | line_split = line1.split('\t') 93 | readLength=int(line_split[2])-int(line_split[1])-1 94 | #test if chromosome is in the list 95 | if (line_split[0] in chrom_sizes.keys()): 96 | #print(line_split[0] + "\n") 97 | if (line_split[3] in cell_ids): 98 | setIndex = int(math.floor(int(line_split[1]) / chrom_step)) 99 | cell_ids[line_split[3]][line_split[0]][setIndex] += readLength 100 | cell_counts[line_split[3]] += 1 101 | else: 102 | cell_ids[line_split[3]]=init_chrom_list_all() 103 | setIndex = int(math.floor(int(line_split[1]) / chrom_step)) 104 | cell_ids[line_split[3]][line_split[0]][setIndex] += readLength 105 | cell_counts[line_split[3]]=1 106 | else: 107 | #abnormal chromosome - will not include in final results 108 | print("warning: chromosome " + line_split[0] + " found in fragment file but not in chromosome file - skipping fragment") 109 | lines_read+=1 110 | if lines_read%100000==0: 111 | print("Reading line " + str(lines_read) + "\n") 112 | print("File loaded into memory") 113 | 114 | 115 | #how many pass filter 116 | filter_passing = 0 117 | 118 | #minimum number of fragments per barcode - quality filter 119 | 120 | print("Writing output") 121 | with open(output_file,'w') as writer: 122 | #initialize header 123 | writer.write("Cell_id") 124 | for chrom in chrom_sizes: 125 | for i in range(0,len(blank_list_all[chrom])): 126 | writer.write("\t" + chrom + "_" + str(i*chrom_step)) 127 | writer.write("\n") 128 | for cell in cell_ids: 129 | if (cell_counts[cell] > min_filter): 130 | chr_list = cell_ids[cell] 131 | filter_passing += 1 132 | writer.write(cell) 133 | for chrom in chrom_sizes: 134 | writer.write('\t') 135 | writer.write('\t'.join(map(str, chr_list[chrom]))) 136 | 137 | writer.write("\n") 138 | 139 | print("Done writing output") 140 | 141 | 142 | -------------------------------------------------------------------------------- /scDataSampClusters.Rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/spcdot/CopyscAT/e785ed313cdcb64cf2f153c9e6cddf7f8351829d/scDataSampClusters.Rdata --------------------------------------------------------------------------------