Merge pull request #340 from PediatricOpenTargets/v12-post-release

v12 post data release analyses

analyses/cnv-frequencies/01-cnv-frequencies.py

@@ -221,8 +221,8 @@ def get_annotations(cnv_frequency_df, CNV_FILE):
      cnv_annot_freq_df = pd.read_csv(cnv_annot_freq_tsv, sep="\t", na_filter=False, dtype=str)
      #1 rename "Gene_Ensembl_Id" to "targetFromSourceId", "EFO" to "diseaseFromSourceMappedId"
      cnv_annot_freq_df.rename(columns={"Gene_Ensembl_ID": "targetFromSourceId", "EFO": "diseaseFromSourceMappedId"}, inplace=True)
-     #2 add "datatypeId" column  with value for every row set to "somatic_mutation"
-     cnv_annot_freq_df["datatypeId"] = "somatic_mutation"
+     #2 add "datatypeId" column  with value for every row set to "pediatric_cancer"
+     cnv_annot_freq_df["datatypeId"] = "pediatric_cancer"
      #3 add "chop_uuid" column - the uuid value for each row should be unique
      cnv_annot_freq_df["chop_uuid"] = [uuid.uuid4() for x in range(len(cnv_annot_freq_df))]
      #4 add "datasourceId" column with value for each row set to "chop_gene_level_cnv"

analyses/cnv-frequencies/README.md

@@ -43,7 +43,7 @@ For each `cancer_group_cohort` with `n_samples` >= 3, compute `Frequency_in_over
 
 
 #### New columns
-- `datatypeId` column with value for all rows set to `somatic_mutation`
+- `datatypeId` column with value for all rows set to `pediatric_cancer`
 - `chop_uuid` column with unique UUID for each row
 - `datasourceId` column with value for all rows set to `chop_gene_level_cnv`
 

analyses/cnv-frequencies/results/.gitignore

@@ -0,0 +1,2 @@
+gene-level-cnv-consensus-annotated-mut-freq.jsonl.gz
+gene-level-cnv-consensus-annotated-mut-freq.tsv.gz

analyses/efo-mondo-mapping/01-merge-auto-search-ols-terms.R

@@ -1,3 +1,8 @@
+# Author: Sangeeta Shukla (shuklas1@chop.edu)
+# Purpose: This scripts automates the seach to retrieve EFO, MONDO, and NCIT ontology codes for all cancer_groups found in the histologies.tsv file.
+# This reduces the manual work to only have to review the potential edge cases where the codes may not be perfect match for the cancer_groups.
+
+
 # Load required libraries
 
 suppressPackageStartupMessages({
@@ -66,11 +71,13 @@ map_prefill_auto <- dplyr::inner_join(map_prefill,efo_auto_prefill,by="cancer_gr
   dplyr::inner_join(.,ncit_auto_prefill,by="cancer_group") %>%
   rename(efo_code_prefill=efo_code.x, 
          mondo_code_prefill=mondo_code.x,
+         ncit_code_prefill=ncit_code.x,
          efo_code_auto=efo_code.y,
          mondo_code_auto=mondo_code.y,
-         ncit_code_auto=ncit_code) %>%
-  select(cancer_group, efo_code_prefill,mondo_code_prefill,efo_code_auto,
-         mondo_code_auto,ncit_code_auto,efo_OntoDesc,mondo_OntoDesc,ncit_OntoDesc) %>%
+         ncit_code_auto=ncit_code.y) %>%
+  select(cancer_group, efo_code_prefill, mondo_code_prefill, ncit_code_prefill,
+         efo_code_auto, mondo_code_auto, ncit_code_auto,
+         efo_OntoDesc,mondo_OntoDesc,ncit_OntoDesc) %>%
   mutate(efo_desc_match = case_when(tolower(cancer_group) == tolower(efo_OntoDesc) ~ "True",
                                     TRUE ~ as.character("False"))) %>%
   mutate(mondo_desc_match = case_when(tolower(cancer_group) == tolower(mondo_OntoDesc) ~ "True",
@@ -81,7 +88,8 @@ map_prefill_auto <- dplyr::inner_join(map_prefill,efo_auto_prefill,by="cancer_gr
                                     TRUE ~ as.character("False"))) %>%
   mutate(mondo_code_match = case_when(mondo_code_prefill == mondo_code_auto ~ "True",
                                       TRUE ~ as.character("False"))) %>%
-  mutate(ncit_code_match = "False")  #ncit_code_match is hard-coded "False" since it does not exist in prefill table
+  mutate(ncit_code_match = case_when(ncit_code_prefill == ncit_code_auto ~ "True",
+                                      TRUE ~ as.character("False")))
 
 
 

analyses/efo-mondo-mapping/02-qc_efo_mondo_map.Rmd

@@ -15,7 +15,7 @@ suppressPackageStartupMessages({
 
 ```{r read the two files}
 root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
-integrte_dir <- file.path(root_dir, 
+integrate_dir <- file.path(root_dir, 
                           "analyses", 
                           "molecular-subtyping-integrate",
                           "results")
@@ -24,7 +24,7 @@ results_dir <- file.path(root_dir,
                          "efo-mondo-mapping",
                          "results")
 
-histology_df <- readr::read_tsv(file.path(integrte_dir, "histologies.tsv"), guess_max=100000)
+histology_df <- readr::read_tsv(file.path(integrate_dir, "histologies.tsv"), guess_max=100000)
 efo_mondo_map <- readr::read_tsv(file.path(results_dir, "efo-mondo-map.tsv"))
 
 ```

analyses/efo-mondo-mapping/02-qc_efo_mondo_map.nb.html

@@ -1783,9 +1783,9 @@ <h2>Load libraries</h2>
 <h2>Read efo-mondo-map.tsv and histologies file</h2>
 <!-- rnb-text-end -->
 <!-- rnb-chunk-begin -->
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+<!-- rnb-source-begin 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 -->
 <pre class="r"><code>root_dir &lt;- rprojroot::find_root(rprojroot::has_dir(&quot;.git&quot;))
-integrte_dir &lt;- file.path(root_dir, 
+integrate_dir &lt;- file.path(root_dir, 
                           &quot;analyses&quot;, 
                           &quot;molecular-subtyping-integrate&quot;,
                           &quot;results&quot;)
@@ -1794,22 +1794,28 @@ <h2>Read efo-mondo-map.tsv and histologies file</h2>
                          &quot;efo-mondo-mapping&quot;,
                          &quot;results&quot;)
 
-histology_df &lt;- readr::read_tsv(file.path(integrte_dir, &quot;histologies.tsv&quot;), guess_max=100000)</code></pre>
+histology_df &lt;- readr::read_tsv(file.path(integrate_dir, &quot;histologies.tsv&quot;), guess_max=100000)</code></pre>
 <!-- rnb-source-end -->
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 <pre><code>Parsed with column specification:
 cols(
   .default = col_character(),
+  age_at_diagnosis_days = col_double(),
   OS_days = col_double(),
+  EFS_days = col_double(),
   age_last_update_days = col_double(),
-  cns_methylation_class_score = col_double(),
-  cns_methylation_subclass_score = col_double(),
-  RF_purity_ABSOLUTE = col_double(),
-  RF_purity_ESTIMATE = col_double(),
-  RF_purity_LUMP = col_double(),
   normal_fraction = col_double(),
   tumor_fraction = col_double(),
-  tumor_ploidy = col_double()
+  tumor_ploidy = col_double(),
+  age_at_event_days = col_double(),
+  age_at_chemo_start = col_double(),
+  age_at_radiation_start = col_double(),
+  tumor_fraction_RFpurify_ABSOLUTE = col_double(),
+  tumor_fraction_RFpurify_ESTIMATE = col_double(),
+  tumor_fraction_LUMP = col_double(),
+  dkfz_v11_methylation_subclass_score = col_double(),
+  dkfz_v12_methylation_subclass_score = col_double(),
+  dkfz_v12_methylation_mgmt_estimated = col_double()
 )</code></pre>
 <!-- rnb-message-end -->
 <!-- rnb-message-begin eyJkYXRhIjoiU2VlIHNwZWMoLi4uKSBmb3IgZnVsbCBjb2x1bW4gc3BlY2lmaWNhdGlvbnMuXG4ifQ== -->
@@ -1848,21 +1854,26 @@ <h2>Check to make sure all cancer groups in histologies are in efo mondo map fil
 
 all(cancer_group_effective %in% efo_mondo_map$cancer_group)</code></pre>
 <!-- rnb-source-end -->
-<!-- rnb-output-begin eyJkYXRhIjoiWzFdIFRSVUVcbiJ9 -->
-<pre><code>[1] TRUE</code></pre>
+<!-- rnb-output-begin eyJkYXRhIjoiWzFdIEZBTFNFXG4ifQ== -->
+<pre><code>[1] FALSE</code></pre>
 <!-- rnb-output-end -->
 <!-- rnb-source-begin eyJkYXRhIjoiY2FuY2VyX2dyb3VwX2VmZmVjdGl2ZVshY2FuY2VyX2dyb3VwX2VmZmVjdGl2ZSAlaW4lIGVmb19tb25kb19tYXAkY2FuY2VyX2dyb3VwXSAifQ== -->
 <pre class="r"><code>cancer_group_effective[!cancer_group_effective %in% efo_mondo_map$cancer_group] </code></pre>
 <!-- rnb-source-end -->
-<!-- rnb-output-begin eyJkYXRhIjoiY2hhcmFjdGVyKDApXG4ifQ== -->
-<pre><code>character(0)</code></pre>
+<!-- rnb-output-begin eyJkYXRhIjoiWzFdIFwiU3VwcmF0ZW50b3JpYWwgZXBlbmR5bW9tYVwiIFwiU3BpbmFsIGVwZW5keW1vbWFcIiAgICAgICAgXG4ifQ== -->
+<pre><code>[1] &quot;Supratentorial ependymoma&quot; &quot;Spinal ependymoma&quot;        </code></pre>
+<!-- rnb-output-end -->
+<!-- rnb-output-begin eyJkYXRhIjpbIlN1cHJhdGVudG9yaWFsIGVwZW5keW1vbWFcbiIsIlNwaW5hbCBlcGVuZHltb21hXG4iXX0= -->
+<pre><code>Supratentorial ependymoma
+
+Spinal ependymoma</code></pre>
 <!-- rnb-output-end -->
 <!-- rnb-chunk-end -->
 <!-- rnb-text-begin -->
 <!-- rnb-text-end -->
 </div>
 
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