Merge branch 'dev' into 607

.github/workflows/run_analysis.yml

@@ -55,7 +55,7 @@ jobs:
 
   run_analysis:
     name: Run Analysis
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-latest-m
     strategy:
       fail-fast: false
       matrix:
@@ -151,8 +151,8 @@ jobs:
             entrypoint: mutational-signatures/run_mutational_signatures.sh
             openpbta_testing: 1
 
-          #- name: Immune Deconvolution
-          #  entrypoint: immune-deconv/run-immune-deconv.sh
+          - name: Immune Deconvolution
+            entrypoint: immune-deconv/run-immune-deconv.sh
 
           #- name: EFO/MONDO annotation
           #  entrypoint: efo-mondo-mapping/run_search_and_qc.sh

README.md

@@ -9,6 +9,7 @@ To cite this work, please note the data release used in your work and cite the f
 1. OpenPBTA:
 Shapiro, J.A., Gaonkar, K.S., Spielman, S.J., Savonen, C.L., Bethell, C.J., Jin, R., Rathi, K.S., Zhu, Y., Egolf, L.E., Farrow, B.K., et al. (2023). OpenPBTA: The Open Pediatric Brain Tumor Atlas. Cell Genom., 100340. [10.1016/j.xgen.2023.100340](https://www.cell.com/cell-genomics/pdf/S2666-979X(23)00115-5.pdf).
 2. OpenPedCan:
+Geng, Z. et al. The Open Pediatric Cancer Project. bioRxiv (2024) [10.1101/2024.07.09.599086](https://www.biorxiv.org/content/10.1101/2024.07.09.599086v1)
 DOI for [all releases](https://zenodo.org/search?q=parent.id%3A6473912&f=allversions%3Atrue&l=list&p=1&s=10&sort=version): `10.5281/zenodo.6473912`. 
 
 The OpenPedCan analyses currently include the following datasets, described more fully below:

analyses/immune-deconv/01-immune-deconv.R

@@ -67,6 +67,9 @@ full_output <- plyr::dlply(.data = n_groups, .variables = "group", .fun = functi
   expr_mat_sub <- expr_mat %>%
     dplyr::select(kf_ids)
 
+  # remove features with all 0 values
+  expr_mat_sub <- expr_mat_sub[rowSums(expr_mat_sub) > 0,]
+
   # deconvolute using specified method
   print("Starting deconvolution...")
   deconv_output <- deconvolute(gene_expression = as.matrix(expr_mat_sub), 

analyses/immune-deconv/README.md

@@ -49,22 +49,6 @@ results/{deconv_method}_output.rds
 
 For `xCell`, the results in the rds file are predicted immune scores per cell type per input sample. These scores are not actual cell fractions but arbitrary scores representing enrichment of the cell types which can be compared across various cancer/gtex groups. The `quanTIseq` results, in contrast, provide an absolute score that can be interpreted as a cell fraction and the results in the rds file are the absolute scores per cell type per input sample. Depending on the user requirements, the output can also be used to create various visualizations. 
 
-#### 02-summary-plots.R 
-
-1. Input
-
-```
-results/{deconv_method}_output.rds
-```
-
-2. Function:
-
-This script creates heatmaps from predicted immune scores.
-
-3. Output
-
-* `plots/{deconv_method}_heatmap_by_group.pdf`: heatmap of average immune scores per cell type per cancer group or GTEx group.
-
 ### Running the analysis
 
 The following script will run the full analysis using either of the two methods of choice: `xCell` or `quanTIseq`. `xCell` is run by default, so to select `quanTIseq`, see code option in chunk below.

analyses/immune-deconv/run-immune-deconv.sh

@@ -23,25 +23,11 @@ Rscript --vanilla 01-immune-deconv.R \
 --deconv_method 'xcell' \
 --output_dir 'results'
 
-# generate heatmaps of average normalized immune scores per cancer or gtex group
-echo "Create summary plots xCell"
-Rscript --vanilla 02-summary-plots.R \
---deconv_output 'results/xcell_output.rds' \
---output_dir 'plots'
-
 ### quanTIseq
 # generate deconvolution output
-echo "Deconvolution quanTIseq..."
-Rscript --vanilla 01-immune-deconv.R \
---expr_mat '../../data/gene-expression-rsem-tpm-collapsed.rds' \
---clin_file '../../data/histologies.tsv' \
---deconv_method 'quantiseq' \
---output_dir 'results'
-
-# generate heatmaps of average normalized immune scores per cancer or gtex group
-echo "Create summary plots quanTIseq"
-Rscript --vanilla 02-summary-plots.R \
---deconv_output 'results/quantiseq_output.rds' \
---output_dir 'plots'
-
-
+#echo "Deconvolution quanTIseq..."
+#Rscript --vanilla 01-immune-deconv.R \
+#--expr_mat '../../data/gene-expression-rsem-tpm-collapsed.rds' \
+#--clin_file '../../data/histologies.tsv' \
+#--deconv_method 'quantiseq' \
+#--output_dir 'results'

analyses/immune-deconv/util/colors.yaml

@@ -3,7 +3,10 @@ cohort:
   'GTEx': '#7CAE00'
   'PBTA': '#C77CFF'
   'TARGET': '#00BFC4'
-
+  'DGD': '#FF61CC'
+  'Maris': '#619CFF'
+  'PPTC': '#CD9600'
+
 sample_type:
    'Normal' : '#7CAE00'
    'Tumor' : 'brown1'

analyses/molecular-subtyping-PB/01-molecular-subtype-pineoblastoma.Rmd

@@ -46,7 +46,10 @@ pb_meta <- histo %>%
     dkfz_v12_methylation_subclass == "PIN_RB" ~ "PB, RB1",
     dkfz_v12_methylation_subclass == "PB_GRP1A" ~ "PB, Group 1A",
     dkfz_v12_methylation_subclass == "PB_GRP1B" ~ "PB, Group 1B",
-    dkfz_v12_methylation_subclass == "PB_GRP2" ~ "PB, Group 2")) 
+    dkfz_v12_methylation_subclass == "PB_GRP2" ~ "PB, Group 2",
+    dkfz_v12_methylation_subclass == "PPTID_A" ~ "PB, PPTID"
+    ))
+
 
 ```
 
@@ -62,19 +65,27 @@ pb_meta <- histo %>%
 ## PB_GRP2: pineoblastoma, miRNA processing-altered 2 (PB, Group 2)
 ## PIN_RB: pineoblastoma, RB1-altered (PB, RB1)
 
+## Additional subtypes added on 07/22/2024
+## Edited By: Sangeeta Shukla
+## PPTID_A: Pineal Parenchymal Tumor of Intermediate Differentiation Type A
+
+
 ```{r}
 
 pb_methy_with_subtype <- pb_meta %>% 
   filter(experimental_strategy == "Methylation" & dkfz_v12_methylation_subclass_score >= 0.8) %>% 
   mutate(molecular_subtype = case_when(molecular_subtype_methyl == "PB, MYC/FOXR2" ~ "PB, MYC/FOXR2", 
                                        molecular_subtype_methyl == "PB, RB1" ~ "PB, RB1",
                                        molecular_subtype_methyl %in% c("PB, Group 1A", "PB, Group 1B") ~ "PB, Group 1",
-                                       molecular_subtype_methyl == "PB, Group 2" ~ "PB, Group 2"))
+                                       molecular_subtype_methyl == "PB, Group 2" ~ "PB, Group 2",
+                                       molecular_subtype_methyl == "PPTID" ~ "PB, PPTID"
+                                       ))
   
 
 pb_methy_without_subtype <- pb_meta %>% 
   filter(experimental_strategy == "Methylation" & 
-           !(Kids_First_Biospecimen_ID %in% pb_methy_with_subtype$Kids_First_Biospecimen_ID))
+           !(Kids_First_Biospecimen_ID %in% pb_methy_with_subtype$Kids_First_Biospecimen_ID)) %>%
+  mutate(molecular_subtype_methyl = NA_character_)
 
 pb_methyl_df <- pb_methy_with_subtype %>% 
   bind_rows(pb_methy_without_subtype) %>% 

analyses/molecular-subtyping-PB/01-molecular-subtype-pineoblastoma.html

@@ -360,13 +360,13 @@ <h4 class="date">2023-12-06</h4>
 <div id="load-library" class="section level2">
 <h2>load library</h2>
 <pre class="r"><code>library(tidyverse)</code></pre>
-<pre><code>## ── Attaching core tidyverse packages ─────────────────────────────────────────── tidyverse 2.0.0 ──
-## ✔ dplyr     1.1.1     ✔ readr     2.1.4
-## ✔ forcats   1.0.0     ✔ stringr   1.5.0
-## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
-## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
-## ✔ purrr     1.0.1     
-## ── Conflicts ───────────────────────────────────────────────────────────── tidyverse_conflicts() ──
+<pre><code>## ── Attaching core tidyverse packages ──────────────────────────────────── tidyverse 2.0.0 ──
+## ✔ dplyr     1.1.4     ✔ readr     2.1.5
+## ✔ forcats   1.0.0     ✔ stringr   1.5.1
+## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
+## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
+## ✔ purrr     1.0.2     
+## ── Conflicts ────────────────────────────────────────────────────── tidyverse_conflicts() ──
 ## ✖ dplyr::filter() masks stats::filter()
 ## ✖ dplyr::lag()    masks stats::lag()
 ## ℹ Use the conflicted package (&lt;http://conflicted.r-lib.org/&gt;) to force all conflicts to become errors</code></pre>
@@ -385,7 +385,7 @@ <h2>set directories</h2>
 <h2>read files</h2>
 <pre class="r"><code>histo &lt;- readr::read_tsv(file.path(data_dir, &quot;histologies-base.tsv&quot;))</code></pre>
 <pre><code>## Rows: 47895 Columns: 64
-## ── Column specification ───────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────────────────
 ## Delimiter: &quot;\t&quot;
 ## chr (41): Kids_First_Participant_ID, Kids_First_Biospecimen_ID, sample_id, a...
 ## dbl (21): cell_line_passage, OS_days, EFS_days, age_at_diagnosis_days, age_a...
@@ -409,7 +409,9 @@ <h2>select PB samples from histologies</h2>
     dkfz_v12_methylation_subclass == &quot;PIN_RB&quot; ~ &quot;PB, RB1&quot;,
     dkfz_v12_methylation_subclass == &quot;PB_GRP1A&quot; ~ &quot;PB, Group 1A&quot;,
     dkfz_v12_methylation_subclass == &quot;PB_GRP1B&quot; ~ &quot;PB, Group 1B&quot;,
-    dkfz_v12_methylation_subclass == &quot;PB_GRP2&quot; ~ &quot;PB, Group 2&quot;)) </code></pre>
+    dkfz_v12_methylation_subclass == &quot;PB_GRP2&quot; ~ &quot;PB, Group 2&quot;,
+    dkfz_v12_methylation_subclass == &quot;PPTID_A&quot; ~ &quot;PB, PPTID&quot;
+    ))</code></pre>
 </div>
 <div id="there-are-four-subtypes" class="section level2">
 <h2>there are four subtypes:</h2>
@@ -442,17 +444,30 @@ <h2>PB_GRP2: pineoblastoma, miRNA processing-altered 2 (PB, Group
 </div>
 <div id="pin_rb-pineoblastoma-rb1-altered-pb-rb1" class="section level2">
 <h2>PIN_RB: pineoblastoma, RB1-altered (PB, RB1)</h2>
+</div>
+<div id="additional-subtypes-added-on-07222024" class="section level2">
+<h2>Additional subtypes added on 07/22/2024</h2>
+</div>
+<div id="edited-by-sangeeta-shukla" class="section level2">
+<h2>Edited By: Sangeeta Shukla</h2>
+</div>
+<div id="pptid_a-pineal-parenchymal-tumor-of-intermediate-differentiation-type-a" class="section level2">
+<h2>PPTID_A: Pineal Parenchymal Tumor of Intermediate Differentiation
+Type A</h2>
 <pre class="r"><code>pb_methy_with_subtype &lt;- pb_meta %&gt;% 
   filter(experimental_strategy == &quot;Methylation&quot; &amp; dkfz_v12_methylation_subclass_score &gt;= 0.8) %&gt;% 
   mutate(molecular_subtype = case_when(molecular_subtype_methyl == &quot;PB, MYC/FOXR2&quot; ~ &quot;PB, MYC/FOXR2&quot;, 
                                        molecular_subtype_methyl == &quot;PB, RB1&quot; ~ &quot;PB, RB1&quot;,
                                        molecular_subtype_methyl %in% c(&quot;PB, Group 1A&quot;, &quot;PB, Group 1B&quot;) ~ &quot;PB, Group 1&quot;,
-                                       molecular_subtype_methyl == &quot;PB, Group 2&quot; ~ &quot;PB, Group 2&quot;))
+                                       molecular_subtype_methyl == &quot;PB, Group 2&quot; ~ &quot;PB, Group 2&quot;,
+                                       molecular_subtype_methyl == &quot;PPTID&quot; ~ &quot;PB, PPTID&quot;
+                                       ))
 
 
 pb_methy_without_subtype &lt;- pb_meta %&gt;% 
   filter(experimental_strategy == &quot;Methylation&quot; &amp; 
-           !(Kids_First_Biospecimen_ID %in% pb_methy_with_subtype$Kids_First_Biospecimen_ID))
+           !(Kids_First_Biospecimen_ID %in% pb_methy_with_subtype$Kids_First_Biospecimen_ID)) %&gt;%
+  mutate(molecular_subtype_methyl = NA_character_)
 
 pb_methyl_df &lt;- pb_methy_with_subtype %&gt;% 
   bind_rows(pb_methy_without_subtype) %&gt;% 
@@ -478,13 +493,13 @@ <h2>assign the molecular subtypes to all samples based on the
 <div id="session-info" class="section level2">
 <h2>session info</h2>
 <pre class="r"><code>sessionInfo()</code></pre>
-<pre><code>## R version 4.2.3 (2023-03-15)
-## Platform: x86_64-pc-linux-gnu (64-bit)
-## Running under: Ubuntu 22.04.2 LTS
+<pre><code>## R version 4.4.0 (2024-04-24)
+## Platform: x86_64-pc-linux-gnu
+## Running under: Ubuntu 22.04.4 LTS
 ## 
 ## Matrix products: default
-## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
-## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so
+## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
+## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
 ## 
 ## locale:
 ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
@@ -494,26 +509,29 @@ <h2>session info</h2>
 ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
 ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
 ## 
+## time zone: Etc/UTC
+## tzcode source: system (glibc)
+## 
 ## attached base packages:
 ## [1] stats     graphics  grDevices utils     datasets  methods   base     
 ## 
 ## other attached packages:
-##  [1] lubridate_1.9.2 forcats_1.0.0   stringr_1.5.0   dplyr_1.1.1    
-##  [5] purrr_1.0.1     readr_2.1.4     tidyr_1.3.0     tibble_3.2.1   
-##  [9] ggplot2_3.4.2   tidyverse_2.0.0
+##  [1] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
+##  [5] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
+##  [9] ggplot2_3.5.1   tidyverse_2.0.0
 ## 
 ## loaded via a namespace (and not attached):
-##  [1] bslib_0.4.2      compiler_4.2.3   pillar_1.9.0     jquerylib_0.1.4 
-##  [5] tools_4.2.3      bit_4.0.5        digest_0.6.31    timechange_0.2.0
-##  [9] jsonlite_1.8.4   evaluate_0.20    lifecycle_1.0.3  gtable_0.3.3    
-## [13] pkgconfig_2.0.3  rlang_1.1.0      cli_3.6.1        parallel_4.2.3  
-## [17] yaml_2.3.7       xfun_0.38        fastmap_1.1.1    withr_2.5.0     
-## [21] knitr_1.42       generics_0.1.3   vctrs_0.6.2      sass_0.4.5      
-## [25] hms_1.1.3        bit64_4.0.5      rprojroot_2.0.3  grid_4.2.3      
-## [29] tidyselect_1.2.0 glue_1.6.2       R6_2.5.1         fansi_1.0.4     
-## [33] vroom_1.6.1      rmarkdown_2.21   tzdb_0.3.0       magrittr_2.0.3  
-## [37] scales_1.2.1     htmltools_0.5.5  colorspace_2.1-0 utf8_1.2.3      
-## [41] stringi_1.7.12   munsell_0.5.0    cachem_1.0.7     crayon_1.5.2</code></pre>
+##  [1] bit_4.0.5         gtable_0.3.5      jsonlite_1.8.8    crayon_1.5.2     
+##  [5] compiler_4.4.0    tidyselect_1.2.1  parallel_4.4.0    jquerylib_0.1.4  
+##  [9] scales_1.3.0      yaml_2.3.8        fastmap_1.2.0     R6_2.5.1         
+## [13] generics_0.1.3    knitr_1.46        rprojroot_2.0.4   munsell_0.5.1    
+## [17] bslib_0.7.0       pillar_1.9.0      tzdb_0.4.0        rlang_1.1.3      
+## [21] utf8_1.2.4        stringi_1.8.4     cachem_1.1.0      xfun_0.44        
+## [25] sass_0.4.9        bit64_4.0.5       timechange_0.3.0  cli_3.6.2        
+## [29] withr_3.0.0       magrittr_2.0.3    digest_0.6.35     grid_4.4.0       
+## [33] vroom_1.6.5       hms_1.1.3         lifecycle_1.0.4   vctrs_0.6.5      
+## [37] evaluate_0.23     glue_1.7.0        fansi_1.0.6       colorspace_2.1-0 
+## [41] rmarkdown_2.27    tools_4.4.0       pkgconfig_2.0.3   htmltools_0.5.8.1</code></pre>
 </div>
 
 

analyses/molecular-subtyping-integrate/results/cancer_group_table_for_README.tsv

@@ -37,6 +37,7 @@ Congenital Mesoblastic Nephroma	Other tumor
 Cranial fasciitis	NA
 Cyst	Other tumor
 Dermoid cyst	NA
+Diffuse intrinsic pontine glioma	Diffuse midline glioma
 Dysembryoplastic neuroepithelial tumor (DNET)	Dysembryoplastic neuroepithelial tumor
 Dysembryoplastic neuroepithelial tumor (DNET);Dysplasia/Gliosis;Ganglioglioma	NA
 Dysembryoplastic neuroepithelial tumor (DNET);Ganglioglioma	Dysembryoplastic neuroepithelial tumor

analyses/molecular-subtyping-integrate/results/harmonized_diagnosis_cancer_group_table.tsv

@@ -69,6 +69,7 @@ DGD	Diffuse leptomeningeal glioneuronal tumor,  To be classified	Diffuse leptome
 PBTA	Diffuse midline glioma, H3 K28-altered	Diffuse midline glioma	Yes	No
 PBTA	Diffuse midline glioma, H3 K28-mutant	Diffuse midline glioma	Yes	No
 PBTA	Diffuse midline glioma, H3 K28-altered (EGFR mutant)	Diffuse midline glioma	Yes	No
+PBTA	Diffuse intrinsic pontine glioma	Diffuse midline glioma	No	Yes
 PBTA	Dysembryoplastic neuroepithelial tumor (DNET)	Dysembryoplastic neuroepithelial tumor	No	Yes
 PBTA	Dysembryoplastic neuroepithelial tumor (DNET);Ganglioglioma	Dysembryoplastic neuroepithelial tumor	No	Yes
 DGD	EBV-Positive Diffuse Large B-Cell Lymphoma, Not Otherwise Specified	EBV-Positive Diffuse Large B-Cell Lymphoma	Yes	No

analyses/molecular-subtyping-integrate/results/pediatric_cancer_groups.tsv

@@ -89,6 +89,7 @@ Diffuse midline glioma	Initial CNS Tumor	501
 Diffuse midline glioma	Progressive	55
 Diffuse midline glioma	Progressive Disease Post-Mortem	12
 Diffuse midline glioma	Recurrence	18
+Diffuse midline glioma	NA	1
 Dysembryoplastic neuroepithelial tumor	Initial CNS Tumor	128
 Dysembryoplastic neuroepithelial tumor	Primary Tumor	4
 Dysembryoplastic neuroepithelial tumor	Progressive	22

analyses/molecular-subtyping-pathology/03-incorporate-clinical-feedback.Rmd

@@ -12,16 +12,16 @@ those to the clinical subtypes
 
 Adding information from the original [issue](https://github.com/AlexsLemonade/OpenPBTA-analysis/issues/751) added by @jharenza 
 
-**UPDATE 2020-10-05** 
+**UPDATE 2020-10-05** Update 2024-08-15: PT_V1HNAC2Q and PT_FFHQ1B9R are the same patient and this needs to be updated upstream
 Below is a double-reviewed (by Cassie Kline and myself) table of histone mutations found in the specified patient tumor per the TGEN genomic reports. 
-
 Kids_First_Participant_ID | H3 Status | tumor_descriptor
 -- | -- | --
 PT_M23Q0DC3 | H3F3A K28M | Initial CNS Tumor
 PT_9GKVQ9QS | H3F3A K28M | Initial CNS Tumor
 PT_KBFM551M | H3 WT | Initial CNS Tumor
 PT_KBFM551M | no report | Progressive   Disease Post-Mortem
 PT_V1HNAC2Q | H3F3A K28M | Initial CNS Tumor
+PT_FFHQ1B9R | H3F3A K28M | Initial CNS Tumor
 PT_NK8A49X5 | H3F3A K28M | Initial CNS Tumor
 PT_NK8A49X5 | H3F3A K28M | Progressive
 PT_KZ56XHJT | H3F3A K28M | Initial CNS Tumor