cna bug and docs update

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
 
-<font color="red"><b>April 12th 2017</b>: New release (v0.3)</font>
-1. Download and unpack the [latest release (v0.3)](https://github.com/sigven/pcgr/releases/latest)
+<font color="red"><b>April 12th 2017</b>: New release (0.3.1)</font>
+
+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:
 

docs/_build/html/_sources/getting_started.rst.txt

@@ -42,18 +42,18 @@ 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) <https://github.com/sigven/pcgr/releases/latest>`__
+   (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``)
+      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 <https://hub.docker.com/r/sigven/pcgr/>`__ from DockerHub
+   0.3.1 <https://hub.docker.com/r/sigven/pcgr/>`__ 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:

docs/_build/html/getting_started.html

@@ -189,18 +189,18 @@ <h3>Python<a class="headerlink" href="#python" title="Permalink to this headline
 </div>
 <div class="section" id="download-pcgr">
 <h3>Download PCGR<a class="headerlink" href="#download-pcgr" title="Permalink to this headline">¶</a></h3>
-<p><strong>April 12th 2017</strong>: New release (v0.3)</p>
+<p><strong>April 14th 2017</strong>: New release (0.3.1)</p>
 <ul>
 <li><p class="first">Download and unpack the <a class="reference external" href="https://github.com/sigven/pcgr/releases/latest">latest release
-(v0.3)</a></p>
+(0.3.1)</a></p>
 </li>
 <li><p class="first">Download and unpack the data bundle (approx. 17Gb) in the PCGR
 directory</p>
 <ul class="simple">
 <li>Download <a class="reference external" href="https://drive.google.com/file/d/0B8aYD2TJ472mQjZOMmg4djZfT1k/">the latest data
 bundle</a>
 from Google Drive to <code class="docutils literal"><span class="pre">~/pcgr-X.X</span></code> (replace <em>X.X</em> with the
-version number, e.g. <code class="docutils literal"><span class="pre">~/pcgr-0.3</span></code>)</li>
+version number, e.g. <code class="docutils literal"><span class="pre">~/pcgr-0.3.1</span></code>)</li>
 <li>Decompress and untar the bundle, e.g. through the following Unix
 command:
 <code class="docutils literal"><span class="pre">gzip</span> <span class="pre">-dc</span> <span class="pre">pcgr.databundle.GRCh37.YYYYMMDD.tgz</span> <span class="pre">|</span> <span class="pre">tar</span> <span class="pre">xvf</span> <span class="pre">-</span></code></li>
@@ -209,10 +209,10 @@ <h3>Download PCGR<a class="headerlink" href="#download-pcgr" title="Permalink to
 have been produced</p>
 </li>
 <li><p class="first">Pull the <a class="reference external" href="https://hub.docker.com/r/sigven/pcgr/">PCGR Docker image -
-v0.3</a> from DockerHub
+0.3.1</a> from DockerHub
 (3.1Gb) :</p>
 <ul class="simple">
-<li><code class="docutils literal"><span class="pre">docker</span> <span class="pre">pull</span> <span class="pre">sigven/pcgr:0.3</span></code> (PCGR annotation engine)</li>
+<li><code class="docutils literal"><span class="pre">docker</span> <span class="pre">pull</span> <span class="pre">sigven/pcgr:0.3.1</span></code> (PCGR annotation engine)</li>
 </ul>
 </li>
 </ul>
@@ -272,7 +272,7 @@ <h2>Run test - generation of clinical report for a cancer genome<a class="header
 sequenced within TCGA. A report for a colorectal tumor case can be
 generated by running the following command in your terminal window:</p>
 <p><code class="docutils literal"><span class="pre">python</span> <span class="pre">pcgr.py</span> <span class="pre">--input_vcf</span> <span class="pre">examples/tumor_sample.COAD.vcf.gz</span> <span class="pre">--input_cna_segments</span></code>
-<code class="docutils literal"><span class="pre">examples/tumor_sample.COAD.cna.tsv</span> <span class="pre">~/pcgr-0.3</span> <span class="pre">~/pcgr-0.3/examples</span> <span class="pre">tumor_sample.COAD</span></code></p>
+<code class="docutils literal"><span class="pre">examples/tumor_sample.COAD.cna.tsv</span> <span class="pre">~/pcgr-0.3.1</span> <span class="pre">~/pcgr-0.3.1/examples</span> <span class="pre">tumor_sample.COAD</span></code></p>
 <p>This command will run the Docker-based PCGR workflow and produce the
 following output files in the <em>examples</em> folder:</p>
 <ol class="arabic simple">

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)</font>
+__April 14th 2017__: New release (0.3.1)</font>
 
-* 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:
 

docs/getting_started.rst

@@ -42,18 +42,18 @@ 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) <https://github.com/sigven/pcgr/releases/latest>`__
+   (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``)
+      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 <https://hub.docker.com/r/sigven/pcgr/>`__ from DockerHub
+   0.3.1 <https://hub.docker.com/r/sigven/pcgr/>`__ 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:

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

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 /

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)