From 5e5b507cdf2cf58a90171ae4a3b32f3fda20d10d Mon Sep 17 00:00:00 2001 From: sigven Date: Fri, 14 Apr 2017 23:31:55 +0200 Subject: [PATCH] cna bug and docs update --- README.md | 15 ++++++++------- docs/_build/doctrees/environment.pickle | Bin 20720 -> 20720 bytes docs/_build/doctrees/getting_started.doctree | Bin 32704 -> 32749 bytes .../html/_sources/getting_started.rst.txt | 12 ++++++------ docs/_build/html/getting_started.html | 12 ++++++------ docs/_build/html/searchindex.js | 2 +- docs/getting_started.md | 12 ++++++------ docs/getting_started.rst | 12 ++++++------ pcgr.py | 6 +++--- src/Dockerfile | 2 +- src/R/pcgrr2/R/utils.R | 12 +++++++----- src/R/pcgrr2_0.1.0.tar.gz | Bin 37086 -> 37095 bytes src/pcgr.tgz | Bin 22217 -> 22217 bytes 13 files changed, 44 insertions(+), 41 deletions(-) diff --git a/README.md b/README.md index 4a6d975e..67bf1c35 100644 --- a/README.md +++ b/README.md @@ -53,15 +53,16 @@ A local installation of Python (it has been tested with [version 2.7.13](https:/ #### STEP 2: Download PCGR -April 12th 2017: New release (v0.3) -1. Download and unpack the [latest release (v0.3)](https://github.com/sigven/pcgr/releases/latest) +April 12th 2017: New release (0.3.1) + +1. Download and unpack the [latest release (0.3.1)](https://github.com/sigven/pcgr/releases/latest) 2. Download and unpack the data bundle (approx. 17Gb) in the PCGR directory - * Download [the latest data bundle](https://drive.google.com/file/d/0B8aYD2TJ472mQjZOMmg4djZfT1k/) from Google Drive to `~/pcgr-X.X` (replace _X.X_ with the version number, e.g `~/pcgr-0.3`) + * Download [the latest data bundle](https://drive.google.com/file/d/0B8aYD2TJ472mQjZOMmg4djZfT1k/) from Google Drive to `~/pcgr-X.X` (replace _X.X_ with the version number, e.g `~/pcgr-0.3.1`) * Unpack the data bundle, e.g. through the following Unix command: `gzip -dc pcgr.databundle.GRCh37.YYYYMMDD.tgz | tar xvf -` A _data/_ folder within the _pcgr-X.X_ software folder should now have been produced -3. Pull the [PCGR Docker image (v0.3)](https://hub.docker.com/r/sigven/pcgr/) from DockerHub (3.1Gb): - * `docker pull sigven/pcgr:0.3` (PCGR annotation engine) +3. Pull the [PCGR Docker image (0.3.1)](https://hub.docker.com/r/sigven/pcgr/) from DockerHub (3.1Gb): + * `docker pull sigven/pcgr:0.3.1` (PCGR annotation engine) #### STEP 3: Input preprocessing @@ -111,7 +112,7 @@ A tumor sample report is generated by calling the Python script __pcgr.py__ in t positional arguments: pcgr_dir PCGR base directory with accompanying data directory, - e.g. ~/pcgr-0.3 + e.g. ~/pcgr-0.3.1 output_dir Output directory sample_id Tumor sample/cancer genome identifier - prefix for output files @@ -145,7 +146,7 @@ A tumor sample report is generated by calling the Python script __pcgr.py__ in t The _examples_ folder contain sample files from TCGA. A report for a colorectal tumor case can be generated through the following command: -`python pcgr.py --input_vcf tumor_sample.COAD.vcf.gz --input_cna_segments tumor_sample.COAD.cna.tsv ~/pcgr-0.3 ~/pcgr-0.3/examples tumor_sample.COAD` +`python pcgr.py --input_vcf tumor_sample.COAD.vcf.gz --input_cna_segments tumor_sample.COAD.cna.tsv ~/pcgr-0.3.1 ~/pcgr-0.3.1/examples tumor_sample.COAD` This command will run the Docker-based PCGR workflow and produce the following output files in the _examples_ folder: diff --git a/docs/_build/doctrees/environment.pickle b/docs/_build/doctrees/environment.pickle index 1c389d7f16d2247d8dcb2501d51d3742f18d9896..b362ed944bcf210acf161dae74b4f7396f738a64 100644 GIT binary patch delta 6526 zcmZ{oc|cU<8pdG;WB?sB2N4j(gCMA(C@?6fAPOpqXiZ@?*}xn;bA%brnVHh+XjWKO zwtZRIZu@47w(oVje^l3{ZmaG0wwGo5V#|G>_sk4u?)Cojz2`gc?|r}LIqS?hxIOjY z_SF08Z5xK9q@;wRk^X2zt5k+pY}BS2+O%?Iq$fpj8Cq4jGRmvud6m&#CEu$Qc%w?8 zSDUUBbvmhTQIN`t%Zg+5GX^$l)rM9>)rU*hDv~la+T~M9lnK$U&Kzp6TCR&k0>Ra* z!V#Yyk^P#RCDa+(%!Gtlhm}w-iYJaL(;8^W*@iX;OQK<`%>9bv{pQk|b)w4VI;xR7 zHyPTzMCaymk3HZIYxBJxhdq$5Fwi43i>t3VFJz;I3 zc@!3jD{al$i%CJ7p)H}V;woFyh^4e*6T9@JM*@*vS=6M3vrm@wa3B=aJZ&jrXWG83 zP?suqN5Wc{A-1IFE$%WD%0xZ3{&j}tFITz@PexzJAMKUH@r)WaG#MF_wCmxBq4ijH zbocig+G_D``lJmCoyLHnspTGLMDFYF^+n_kpYH1mYirsSsogU)zPCwV9oBl=J=uPl zJnOy)H1@T7GW?-#7}tXBo~(dh4n_h!0a@2V?R5I1U62qvGDbA&8MeAN)U_%S>W=6# z9ku>lR}9h8(%gp9D28UcGe;`!wjoMmTdG)+U2CsY++rYmT83MxQyP^f@kI8V)W%(h zvdjn9465MT81M+`v@kFu#zLi4Kt>h z+B4Azyi5EtW1K6OW+ZtO*W{Ts;*S{xj?us*bwo^Vi7TIx0uwQ%7P5f&#WV&LkXpp# zSSvY>NwmPy#fXqPo=M3{mWZ61Suv?-1b%|hY9>2NX+~1ZxMrfbvu28;9Dt;bPdcU* z3{0XiaqcE#`NUN}g;P*^B%_s9bSf)F53D>55lX9=oNgs&h%L3XF{Nk(zFK@)JJDG~ zGg4a1HEvPtE_c)ckkl$tI@&dp6SHVcD6D6N@nsu0g^PPOqjRk2Tt?9YD>iZpW||nC zXGNRY40-^aj|ejhm~634mfshi+jL^Cq8m}}a^({-heB>*IK0h?LM ziFO(jW|pzS__7_Gf|=!vuCSs>`8? 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Download PCGR ^^^^^^^^^^^^^ -**April 12th 2017**: New release (v0.3) +**April 14th 2017**: New release (0.3.1) - Download and unpack the `latest release - (v0.3) `__ + (0.3.1) `__ - Download and unpack the data bundle (approx. 17Gb) in the PCGR directory @@ -53,7 +53,7 @@ Download PCGR - Download `the latest data bundle `__ from Google Drive to ``~/pcgr-X.X`` (replace *X.X* with the - version number, e.g. ``~/pcgr-0.3``) + version number, e.g. ``~/pcgr-0.3.1``) - Decompress and untar the bundle, e.g. through the following Unix command: ``gzip -dc pcgr.databundle.GRCh37.YYYYMMDD.tgz | tar xvf -`` @@ -62,10 +62,10 @@ Download PCGR have been produced - Pull the `PCGR Docker image - - v0.3 `__ from DockerHub + 0.3.1 `__ from DockerHub (3.1Gb) : - - ``docker pull sigven/pcgr:0.3`` (PCGR annotation engine) + - ``docker pull sigven/pcgr:0.3.1`` (PCGR annotation engine) Run test - generation of clinical report for a cancer genome ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -125,7 +125,7 @@ sequenced within TCGA. A report for a colorectal tumor case can be generated by running the following command in your terminal window: ``python pcgr.py --input_vcf examples/tumor_sample.COAD.vcf.gz --input_cna_segments`` -``examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3 ~/pcgr-0.3/examples tumor_sample.COAD`` +``examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3.1 ~/pcgr-0.3.1/examples tumor_sample.COAD`` This command will run the Docker-based PCGR workflow and produce the following output files in the *examples* folder: diff --git a/docs/_build/html/getting_started.html b/docs/_build/html/getting_started.html index 929ef471..3aa6e24b 100644 --- a/docs/_build/html/getting_started.html +++ b/docs/_build/html/getting_started.html @@ -189,10 +189,10 @@

Python

Download PCGRΒΆ

-

April 12th 2017: New release (v0.3)

+

April 14th 2017: New release (0.3.1)

  • Download and unpack the latest release -(v0.3)

    +(0.3.1)

  • Download and unpack the data bundle (approx. 17Gb) in the PCGR directory

    @@ -200,7 +200,7 @@

    Download PCGRthe latest data bundle from Google Drive to ~/pcgr-X.X (replace X.X with the -version number, e.g. ~/pcgr-0.3)

    • +version number, e.g. ~/pcgr-0.3.1)
  • Decompress and untar the bundle, e.g. through the following Unix command: gzip -dc pcgr.databundle.GRCh37.YYYYMMDD.tgz | tar xvf -
    • @@ -209,10 +209,10 @@

    Download PCGRPull the PCGR Docker image - -v0.3 from DockerHub +0.3.1 from DockerHub (3.1Gb) :

      -
    • docker pull sigven/pcgr:0.3 (PCGR annotation engine)
      • +
    • docker pull sigven/pcgr:0.3.1 (PCGR annotation engine)

@@ -272,7 +272,7 @@

Run test - generation of clinical report for a cancer genomepython pcgr.py --input_vcf examples/tumor_sample.COAD.vcf.gz --input_cna_segments -examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3 ~/pcgr-0.3/examples tumor_sample.COAD

+examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3.1 ~/pcgr-0.3.1/examples tumor_sample.COAD

This command will run the Docker-based PCGR workflow and produce the following output files in the examples folder:

    diff --git a/docs/_build/html/searchindex.js b/docs/_build/html/searchindex.js index efe27231..713fb367 100644 --- a/docs/_build/html/searchindex.js +++ b/docs/_build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({docnames:["about","annotation_resources","getting_started","index","output"],envversion:50,filenames:["about.rst","annotation_resources.rst","getting_started.rst","index.rst","output.rst"],objects:{},objnames:{},objtypes:{},terms:{"1000g":4,"1000genom":1,"12th":2,"140453136a":1,"16gb":[],"17gb":2,"17th":[],"1gb":2,"1kg":[],"2016_03":[],"2016_09":1,"2020plu":2,"28th":[],"2gb":[],"5gb":2,"5th":1,"8th":1,"abstract":1,"case":[1,2,4],"class":4,"default":2,"function":[2,3,4],"import":[0,2,4],"new":2,"public":4,"short":[1,4],CDS:4,EAS:4,For:[1,2,4],IDs:4,POS:[],SAS:4,The:[0,1,2,4],There:0,These:[],_strong:[],abber:3,aberr:[0,2,4],about:3,abov:4,accept:4,acceptor:4,access:[0,4],accompani:2,accord:[2,4],acid:4,acquir:0,across:4,action:4,actual:1,ada:[],adapt:4,add:4,adddit:4,addit:[0,4],adenoma:1,adjust:4,advanc:2,af_norm:[],af_tumor:[],affect:4,affected_spl:[],affili:0,afr:4,afr_af_1kg:4,afr_af_exac:4,afr_af_gnomad:4,african:4,after:4,aggreg:4,aid:0,algorithm:4,align:4,all:[0,1],allel:4,allele_num:4,alon:0,alreadi:[1,2],also:1,alt:4,alter:[2,4],altern:4,american:4,amino:4,amino_acid:4,among:[],amplif:2,amr:4,amr_af_1kg:4,amr_af_exac:4,amr_af_gnomad:4,analys:2,analysi:0,analyz:4,ani:[2,4],annot:[0,2,3],annotation_resourc:[],antineoplast:[1,4],antineoplastic_drug_interact:4,antineoplastic_drugs_dgidb:4,appli:4,applic:[0,4],appri:4,approv:4,approx:2,april:[1,2],argument:2,asian:4,assembl:4,assign:2,associ:2,attach:[0,4],aug:[],b147:1,base:[2,3,4],basic:[0,2,3,4],been:[0,1,2,4],below:4,benign:[],best:4,betweeen:1,bgzip:4,bind:4,biocomput:4,bioconductor:4,biologi:0,biomark:[0,1,2],biotyp:4,block:4,block_substitut:[],bm_citat:4,bm_clinical_signific:4,bm_disease_nam:4,bm_drug_nam:4,bm_evidence_direct:4,bm_evidence_level:4,bm_evidence_typ:4,bm_rate:4,bool:[],boost:4,both:[0,4],braf:1,breast:4,browser:4,build:4,bundl:[0,1,2],cadd:4,call:2,call_confid:[],caller:4,can:[0,2,4],cancer:4,cancer_census_germlin:4,cancer_census_somat:4,cancer_mutation_hotspot:4,cancer_typ:4,cancerhotspot:4,candid:4,canon:4,cap:4,caption:[],care:0,carri:4,catalog:[1,4],catalogu:1,categori:4,caus:4,causal:4,cbmdb:1,cbmdb_id:4,ccd:4,cdna:4,cdna_posit:4,cds:[],cds_chang:4,cds_end_nf:4,cds_posit:4,cds_start_nf:4,cell:4,cell_typ:4,cellular:[],cencu:1,censu:4,challeng:0,chang:[2,4],check:2,chr1:4,chr7:1,chr:4,chrom:4,chrome:4,chromosom:4,citat:[],cite:4,civic:[1,4],civic_id:4,civic_id_2:4,classif:4,clin:[],clin_sig:4,clinic:[0,3],clinvar:[1,4],clinvar_msid:4,clinvar_pmid:4,clinvar_sig:4,clinvar_variant_origin:4,cluster:4,cna:2,cna_seg:[2,4],cnminor:[],cntotal:[],cnv:[],coad:2,code:3,codon:[1,4],codon_numb:4,cohort:1,collect:1,colorect:[2,4],column:4,com:[],come:1,command:2,common:4,complet:[2,4],complex:0,composit:4,comprehens:0,compress:4,comput:2,concern:3,confer:1,confid:[],confirmed_somat:1,consensu:4,consequ:3,consid:4,consortium:[0,1,4],constitut:4,contain:[0,2,4],content:[3,4],context:0,contribut:[2,4],convent:4,coordin:4,copi:[0,2,3],correctli:4,correspond:1,cosmic:[1,4],cosmic_cancer_type_al:4,cosmic_cancer_type_gw:4,cosmic_codon_count_gw:4,cosmic_codon_frac_gw:4,cosmic_consequ:4,cosmic_count_gw:4,cosmic_drug_resist:4,cosmic_fathmm_pr:4,cosmic_mutation_id:4,cosmic_sample_sourc:4,cosmic_site_histolog:4,cosmic_vartyp:4,count:4,cover:4,cpu:2,creat:[],criteria:1,csq:4,curat:[1,4],current:4,damag:[],data:[0,2,3,4],databas:3,databundl:2,dataset:3,date:0,dbnsfp:[1,4],dbnsfp_consensus_lr:4,dbnsfp_consensus_svm:4,dbsnp:[1,4],dbsnp_mappingstatu:4,dbsnp_submiss:4,dbsnp_valid:4,dbsnpbuildid:4,dbsnprsid:4,dec:1,decompos:4,decompress:2,deconstructsig:4,dedic:[],defin:4,delet:[2,4],delin:4,denot:4,depend:0,depth:4,deriv:4,describ:4,descript:4,determin:[],develop:[0,4],dgidb:1,diagnosi:2,diagnost:[0,4],differ:[2,4],direct:[],directori:2,discov:1,diseas:4,distanc:4,distribut:4,dna:4,doc:[],docker:3,dockerhub:2,docm:1,docm_diseas:4,docm_pmid:4,document:[],domain:[3,4],done:4,donor:4,download:[],downstream:2,dp_normal:[],dp_tumor:[],drive:2,driver:[1,4],drug:[1,2,4],dure:2,each:4,eas_af_1kg:4,eas_af_exac:4,eas_af_gnomad:4,east:4,effect:[0,3],effect_predict:4,either:[1,4],emerg:4,end:4,engin:2,ensembl:[0,4],ensembl_gene_id:4,ensembl_transcript_id:4,ensp:4,entrez:4,entrez_id:4,error:2,estim:2,etc:[1,4],etiolog:[2,4],eur:4,eur_af_1kg:4,european:4,event:4,evid:[2,4],exac:[1,4],exampl:[2,4],exist:[2,4],existing_vari:4,exit:2,exom:[1,4],exon:[1,4],experi:4,experienc:4,experiment:4,expert:0,explor:4,extend:0,facet:[],factor:4,fail:2,fall:4,fals:2,famili:1,fathmm:4,fathmm_mkl:4,fda:1,featur:[3,4],feature_typ:4,feb:[],februai:1,februari:1,figur:0,file:[2,4],fin:[],fin_af_exac:4,fin_af_gnomad:4,find:[0,4],finnish:4,firefox:4,first:4,flag:[2,4],flag_pick_allel:4,flank:4,flexibl:0,focu:[],folder:2,follow:[1,2,4],forc:2,force_overwrit:2,fork:2,form:0,format:0,found:[1,2,4],four:4,frac:[],fraction:[],fraction_mut:4,frameshift:4,frequenc:3,from:[0,1,2,4],g12:1,gain:4,gencod:[1,4],gencode_tag:4,gencode_transcript_typ:4,gencode_v19:4,gene:[0,2,3],gene_biotyp:4,gen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ol:[0,2],toolbar:2,total:4,trait:4,transcript:[2,4],transcript_end:4,transcript_overlap_perc:4,transcript_start:4,transvar:1,treatment:4,trembl:4,treshold:2,trial:[1,4],trust:4,tsgene:[1,4],tsgene_oncogen:4,tsl:4,tsv:2,tumor:[0,1,2,4],tumor_sampl:2,tumor_suppressor:4,tumor_typ:4,tumorigenesi:4,two:[2,4],type:[2,4],unambigu:1,unannot:4,underli:[2,4],uniform:4,uniparc:4,uniprot:[1,4],uniprot_featur:4,uniprot_id:4,uniprotkb:4,uniqu:4,univers:0,unix:2,unpack:2,untar:2,upcom:4,upon:[],upper:4,uri:4,usag:2,use:[2,3],used:[1,2],user:4,using:[0,2,4],util:3,v15:[],v19:[1,4],v22:[],v23:1,v30:[],v31:1,v600e:1,v78:[],v80:1,v85:1,valid:4,valu:2,variabl:4,variant:[0,2,3],variant_class:4,variat:4,variou:4,vartyp:[],vcf:2,vcf_sample_id:4,vcfanno:[0,2],vcfbreakmulti:4,vcflib:4,vcftool:4,vector:4,vep:[0,1,2],vep_all_consequ:4,veri:2,version:[2,4],view:4,virtual:2,weak:[],weak_mutect:[],weak_strelka:[],weight:4,what:3,whenev:1,where:4,whether:4,which:[0,1,2,4],why:3,wide:[1,4],window:2,within:[1,2,4],work:4,workflow:[0,2,4],working_directori:[],wtsi:4,xvf:2,you:2,your:2,yyyymmdd:2},titles:["About","Annotation 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diff --git a/docs/getting_started.md b/docs/getting_started.md index 9399eae5..3e800218 100644 --- a/docs/getting_started.md +++ b/docs/getting_started.md @@ -23,18 +23,18 @@ An installation of Python (version 2.7.13) is required to run PCGR. Check that P #### Download PCGR -__April 12th 2017__: New release (v0.3) +__April 14th 2017__: New release (0.3.1) -* Download and unpack the [latest release (v0.3)](https://github.com/sigven/pcgr/releases/latest) +* Download and unpack the [latest release (0.3.1)](https://github.com/sigven/pcgr/releases/latest) * Download and unpack the data bundle (approx. 17Gb) in the PCGR directory - * Download [the latest data bundle](https://drive.google.com/file/d/0B8aYD2TJ472mQjZOMmg4djZfT1k/) from Google Drive to `~/pcgr-X.X` (replace _X.X_ with the version number, e.g. `~/pcgr-0.3`) + * Download [the latest data bundle](https://drive.google.com/file/d/0B8aYD2TJ472mQjZOMmg4djZfT1k/) from Google Drive to `~/pcgr-X.X` (replace _X.X_ with the version number, e.g. `~/pcgr-0.3.1`) * Decompress and untar the bundle, e.g. through the following Unix command: `gzip -dc pcgr.databundle.GRCh37.YYYYMMDD.tgz | tar xvf -` A _data/_ folder within the _pcgr-X.X_ software folder should now have been produced -* Pull the [PCGR Docker image - v0.3](https://hub.docker.com/r/sigven/pcgr/) from DockerHub (3.1Gb) : - * `docker pull sigven/pcgr:0.3` (PCGR annotation engine) +* Pull the [PCGR Docker image - 0.3.1](https://hub.docker.com/r/sigven/pcgr/) from DockerHub (3.1Gb) : + * `docker pull sigven/pcgr:0.3.1` (PCGR annotation engine) ### Run test - generation of clinical report for a cancer genome @@ -90,7 +90,7 @@ A tumor sample report is generated by calling the Python script __pcgr.py__, whi The _examples_ folder contain input files from two tumor samples sequenced within TCGA. A report for a colorectal tumor case can be generated by running the following command in your terminal window: `python pcgr.py --input_vcf examples/tumor_sample.COAD.vcf.gz --input_cna_segments ` -`examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3 ~/pcgr-0.3/examples tumor_sample.COAD` +`examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3.1 ~/pcgr-0.3.1/examples tumor_sample.COAD` This command will run the Docker-based PCGR workflow and produce the following output files in the _examples_ folder: diff --git a/docs/getting_started.rst b/docs/getting_started.rst index 4faca462..35ddf1c0 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -42,10 +42,10 @@ terminal window. Download PCGR ^^^^^^^^^^^^^ -**April 12th 2017**: New release (v0.3) +**April 14th 2017**: New release (0.3.1) - Download and unpack the `latest release - (v0.3) `__ + (0.3.1) `__ - Download and unpack the data bundle (approx. 17Gb) in the PCGR directory @@ -53,7 +53,7 @@ Download PCGR - Download `the latest data bundle `__ from Google Drive to ``~/pcgr-X.X`` (replace *X.X* with the - version number, e.g. ``~/pcgr-0.3``) + version number, e.g. ``~/pcgr-0.3.1``) - Decompress and untar the bundle, e.g. through the following Unix command: ``gzip -dc pcgr.databundle.GRCh37.YYYYMMDD.tgz | tar xvf -`` @@ -62,10 +62,10 @@ Download PCGR have been produced - Pull the `PCGR Docker image - - v0.3 `__ from DockerHub + 0.3.1 `__ from DockerHub (3.1Gb) : - - ``docker pull sigven/pcgr:0.3`` (PCGR annotation engine) + - ``docker pull sigven/pcgr:0.3.1`` (PCGR annotation engine) Run test - generation of clinical report for a cancer genome ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -125,7 +125,7 @@ sequenced within TCGA. A report for a colorectal tumor case can be generated by running the following command in your terminal window: ``python pcgr.py --input_vcf examples/tumor_sample.COAD.vcf.gz --input_cna_segments`` -``examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3 ~/pcgr-0.3/examples tumor_sample.COAD`` +``examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3.1 ~/pcgr-0.3.1/examples tumor_sample.COAD`` This command will run the Docker-based PCGR workflow and produce the following output files in the *examples* folder: diff --git a/pcgr.py b/pcgr.py index 6e7ad23d..7b1167e7 100755 --- a/pcgr.py +++ b/pcgr.py @@ -18,12 +18,12 @@ def __main__(): parser.add_argument('--num_vcfanno_processes', dest = "num_vcfanno_processes", default=4, type=int, help='Number of processes used during vcfanno annotation') parser.add_argument('--num_vep_forks', dest = "num_vep_forks", default=4, type=int, help='Number of forks (--forks option in VEP) used during VEP annotation') parser.add_argument('--force_overwrite', action = "store_true", help='By default, the script will fail with an error if any output file already exists. You can force the overwrite of existing result files by using this flag') - parser.add_argument('--version', action='version', version='%(prog)s 0.3') - parser.add_argument('pcgr_dir',help='PCGR base directory with accompanying data directory, e.g. ~/pcgr-0.3') + parser.add_argument('--version', action='version', version='%(prog)s 0.3.1') + parser.add_argument('pcgr_dir',help='PCGR base directory with accompanying data directory, e.g. ~/pcgr-0.3.1') parser.add_argument('output_dir',help='Output directory') parser.add_argument('sample_id',help="Tumor sample/cancer genome identifier - prefix for output files") - docker_image_version = 'sigven/pcgr:0.3' + docker_image_version = 'sigven/pcgr:0.3.1' args = parser.parse_args() overwrite = 0 diff --git a/src/Dockerfile b/src/Dockerfile index 601812a5..abcf74e5 100755 --- a/src/Dockerfile +++ b/src/Dockerfile @@ -15,7 +15,7 @@ ENV PACKAGE_BIO="tabix samtools libhts1 bedtools" ENV PACKAGE_DEV="perl debconf-utils build-essential gfortran python-dev python-pip gcc-multilib autoconf zlib1g-dev git libncurses5-dev libblas-dev liblapack-dev cpanminus libcurl4-gnutls-dev libssh2-1-dev libxml2-dev vim libssl-dev openssl libcairo2-dev" ENV PYTHON_MODULES="numpy cython scipy transvar bx-python pyvcf cyvcf cyvcf2 biopython crossmap pandas" -ENV R_BASE_VERSION 3.3.2 +ENV R_BASE_VERSION 3.3.3 USER root WORKDIR / diff --git a/src/R/pcgrr2/R/utils.R b/src/R/pcgrr2/R/utils.R index 6fee795c..1291b1e8 100644 --- a/src/R/pcgrr2/R/utils.R +++ b/src/R/pcgrr2/R/utils.R @@ -311,8 +311,8 @@ cna_segment_annotation <- function(cna_file, logR_threshold_amplification, logR_ names(civic_cna_biomarkers) <- toupper(names(civic_cna_biomarkers)) civic_cna_biomarkers <- dplyr::rename(civic_cna_biomarkers, GENE = GENESYMBOL, CNA_TYPE = CIVIC_CONSEQUENCE, DESCRIPTION = EVIDENCE_DESCRIPTION, CITATION = PUBMED_HTML_LINK) - cna_biomarkers <- NULL - cna_biomarker_segments <- NULL + cna_biomarkers <- data.frame() + cna_biomarker_segments <- data.frame() if(!is.null(tsgene_homozygous_deletion)){ if(nrow(tsgene_homozygous_deletion) > 0){ civic_biomarker_hits1 <- dplyr::inner_join(tsgene_homozygous_deletion, civic_cna_biomarkers, by=c("GENE","CNA_TYPE")) @@ -327,9 +327,11 @@ cna_segment_annotation <- function(cna_file, logR_threshold_amplification, logR_ } if(!is.null(cna_biomarkers)){ - cna_biomarkers <- cna_biomarkers[c("CHROMOSOME","GENE","CNA_TYPE","EVIDENCE_LEVEL","CLINICAL_SIGNIFICANCE","EVIDENCE_TYPE","DESCRIPTION","DISEASE_NAME","EVIDENCE_DIRECTION","DRUG_NAMES","CITATION","RATING","GENE_NAME","CANCER_CENSUS_SOMATIC","KEGG_PATHWAY","ANTINEOPLASTIC_DRUG_INTERACTIONS","SEGMENT_LENGTH", "SEGMENT","LogR")] - cna_biomarkers <- cna_biomarkers %>% dplyr::arrange(EVIDENCE_LEVEL,RATING) - cna_biomarker_segments <- dplyr::select(cna_biomarkers, SEGMENT, LogR) %>% dplyr::distinct() + if(nrow(cna_biomarkers) > 0){ + cna_biomarkers <- cna_biomarkers[c("CHROMOSOME","GENE","CNA_TYPE","EVIDENCE_LEVEL","CLINICAL_SIGNIFICANCE","EVIDENCE_TYPE","DESCRIPTION","DISEASE_NAME","EVIDENCE_DIRECTION","DRUG_NAMES","CITATION","RATING","GENE_NAME","CANCER_CENSUS_SOMATIC","KEGG_PATHWAY","ANTINEOPLASTIC_DRUG_INTERACTIONS","SEGMENT_LENGTH", "SEGMENT","LogR")] + cna_biomarkers <- cna_biomarkers %>% dplyr::arrange(EVIDENCE_LEVEL,RATING) + cna_biomarker_segments <- dplyr::select(cna_biomarkers, SEGMENT, LogR) %>% dplyr::distinct() + } } cna_data <- list(ranked_segments = cna_segments_filtered, oncogene_amplified = oncogene_amplified, tsgene_homozygous_deletion = tsgene_homozygous_deletion,cna_df_for_print = df_print_sorted, cna_biomarkers = cna_biomarkers, cna_biomarker_segments = cna_biomarker_segments) diff --git a/src/R/pcgrr2_0.1.0.tar.gz b/src/R/pcgrr2_0.1.0.tar.gz index e7a20197a5fb61562347e2d778713b441e2b09b3..e14a4d291cb29ed830c7594a94151f038d4fe872 100644 GIT binary patch delta 18459 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