This notebook identifies samples to include in subset files for the purpose of molecularly subtyping embryonal tumors (AlexsLemonade/OpenPBTA-analysis#251).
This closed pull request is also relevant to the task at hand: [AlexsLemonade/OpenPBTA-analysis#401](https://github.com/AlexsLemonade/OpenPBTA-analysis/pull/401
We’ll use the subset files generated in 02-generate-subset-files.R to construct tables that summarize the data on the subtyping issue.
This notebook is intended to be run via the command line from the top directory of the repository as follows:
Rscript -e "rmarkdown::render('analyses/molecular-subtyping-embryonal/03-table-prep.Rmd', clean = TRUE)"library(tidyverse)Registered S3 method overwritten by 'dplyr':
method from
print.rowwise_df
[37m── [1mAttaching packages[22m ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──[39m
[37m[32m✔[37m [34mggplot2[37m 3.2.0 [32m✔[37m [34mpurrr [37m 0.3.2
[32m✔[37m [34mtibble [37m 2.1.3 [32m✔[37m [34mdplyr [37m 0.8.3
[32m✔[37m [34mtidyr [37m 0.8.3 [32m✔[37m [34mstringr[37m 1.4.0
[32m✔[37m [34mreadr [37m 1.3.1 [32m✔[37m [34mforcats[37m 0.4.0[39m
[37m── [1mConflicts[22m ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
[31m✖[37m [34mdplyr[37m::[32mfilter()[37m masks [34mstats[37m::filter()
[31m✖[37m [34mdplyr[37m::[32mlag()[37m masks [34mstats[37m::lag()[39m# TODO: consider moving this out of this notebook to use with ependymoma
# subtyping?
wrangle_fusions <- function(fusions_df, string_to_match, column_name) {
# This function takes the fusion summary binary matrix and returns a
# data frame that contains two columns: Kids_First_Biospecimen_ID and
# a column containing a comma-separated 'list' of relevant fusions.
# A "relevant fusion" is determined by passing the string_to_match argument
# to dplyr::matches. To specify that a gene symbol should be a 5' partner,
# you can pass "<gene symbol>--". The 2nd column name is determined by the
# column_name argument.
#
# Args:
# fusions_df: a fusion_summary data.frame from `fusion-summary`
# string_to_match: character; passed to dplyr::matches to select relevant
# columns
# column_name: the name of the relevant fusion column in the data.frame
# that is returned
#
# Returns: a data.frame with Kids_First_Biospecimen_ID and relevant fusions
# columns; biospecimens without any relevant fusions will have "None"
# in the second column
specific_fusions <- fusions_df %>%
# only include relevant fusions using string_to_match
select(Kids_First_Biospecimen_ID, matches(string_to_match)) %>%
reshape2::melt() %>%
# a value of 0 means that fusion is not present in that biospecimen
filter(value > 0) %>%
select(-value) %>%
group_by(Kids_First_Biospecimen_ID) %>%
# get the comma separated 'list' of relevant fusions
summarize(!!column_name := paste(sort(unique(variable)), collapse = ", "))
# if a sample had none of the relevant fusions, it's missing from
# specific_fusions -- let's add them back in with "None" in the second
# column
missing_ids <- setdiff(fusions_df$Kids_First_Biospecimen_ID,
specific_fusions$Kids_First_Biospecimen_ID)
specific_fusions <- specific_fusions %>%
bind_rows(data.frame(Kids_First_Biospecimen_ID = missing_ids)) %>%
replace(is.na(.), "None")
return(specific_fusions)
}subset_dir <- "subset-files"
results_dir <- "results"
data_dir <- file.path("..", "..", "data")Set up filenames.
# full clinical file + fusions of interest file
histologies_file <- file.path(data_dir, "pbta-histologies.tsv")
# TODO: update when the new files with union of sample IDs get included in the
# in the data download (v14)
fusion_file <- file.path("..", "fusion-summary", "results",
"fusion_summary_embryonal_foi.tsv")
# copy number consensus file (analysis file)
cn_consensus_seg <- file.path("..", "copy_number_consensus_call", "results",
"pbta-cnv-consensus.seg.gz")
manta_file <- file.path(subset_dir, "embryonal_manta_sv.tsv")
polya_file <- file.path(subset_dir, "embryonal_zscored_exp.polya.rds")
stranded_file <- file.path(subset_dir, "embryonal_zscored_exp.stranded.rds")
# file that contains the biospecimen IDs that met criteria for subtyping
biospecimen_file <- file.path(results_dir,
"biospecimen_ids_embryonal_subtyping.tsv")
# annotated sex chromosomes copy number alterations
sex_chr_consensus <- file.path("..", "focal-cn-file-preparation", "results",
"consensus_seg_annotated_cn_x_and_y.tsv.gz")
sex_chr_cnvkit <- file.path("..", "focal-cn-file-preparation", "results",
"cnvkit_annotated_cn_x_and_y.tsv.gz")
sex_chr_controlfreec <- file.path("..", "focal-cn-file-preparation", "results",
"controlfreec_annotated_cn_x_and_y.tsv.gz")Read in files.
histologies_df <- read_tsv(histologies_file,
col_types = cols(
molecular_subtype = col_character()
))
# manta data for BCOR tandem duplication
manta_sv_df <- read_tsv(manta_file)
# all of the RNA data
polya_exp <- read_rds(polya_file)
stranded_exp <- read_rds(stranded_file)
fusions_df <- read_tsv(fusion_file)
# get biospecimen ids as a vector rather than data frame
biospecimen_ids <- read_tsv(biospecimen_file) %>%
pull(Kids_First_Biospecimen_ID)
# we'll filter to only the relevant biospecimens for the copy number alteration
# data that is committed to the repository
cn_consensus <- read_tsv(cn_consensus_seg) %>%
filter(ID %in% biospecimen_ids)
consensus_sex_chr <- read_tsv(sex_chr_consensus) %>%
filter(biospecimen_id %in% biospecimen_ids)
cnvkit_sex_chr <- read_tsv(sex_chr_cnvkit) %>%
filter(biospecimen_id %in% biospecimen_ids)
controlfreec_sex_chr <- read_tsv(sex_chr_controlfreec) %>%
filter(biospecimen_id %in% biospecimen_ids)output_all <- file.path(results_dir,
"embryonal_tumor_subtyping_relevant_data.tsv")Summarizing the salient points from AlexsLemonade/OpenPBTA-analysis#251:
ETMR, C19MC-altered: These tumors have focal amplification of the miRNA cluster on chr19 (denoted C19MC) and often have gene fusions involving TTYH1 and chr19 miRNA cluster genes
CNS HGNET-MN1: Contain gene fusions involving 5’ MN1. 3’ fusion partners can include BEND2 and CXXC5.
CNS NB-FOXR2: Over-expression and/or gene fusions in FOXR2
CNS EFT-CIC: Alterations in CIC, commonly fused with NUTM1
colnames(fusions_df) [1] "Kids_First_Biospecimen_ID" "MACF1--TTYH1" "MN1--AC008667.1" "MN1--CXXC5" "MN1--PATZ1" "MN1--PSD2"
[7] "NPAS3--AC245884.4/TTYH1" "TTYH1--DPRX/RNU6-698P" "TTYH1--MIR512-2/MIR1323" "TTYH1--MIR515-2/MIR519C" "ZC4H2--AL627224.1/FOXR2" "ZC4H2--FOXR2"
[13] "CIC--NUTM1" "MN1--BEND2" ttyh1_fusions <- wrangle_fusions(fusions_df, "TTYH1--", "TTYH1_fusions")binding character and factor vector, coercing into character vectormn1_fusions <- wrangle_fusions(fusions_df, "MN1--", "MN1_fusions") binding character and factor vector, coercing into character vectorfoxr2_fusions <- wrangle_fusions(fusions_df, "FOXR2", "FOXR2_fusions")binding character and factor vector, coercing into character vectorcic_fusions <- wrangle_fusions(fusions_df, "CIC--", "CIC_fusions")binding character and factor vector, coercing into character vectorfusions_summary_df <- list(ttyh1_fusions,
mn1_fusions,
foxr2_fusions,
cic_fusions) %>%
purrr::reduce(dplyr::inner_join, by = "Kids_First_Biospecimen_ID")
# remove data.frame we no longer need
rm(ttyh1_fusions, mn1_fusions, foxr2_fusions, cic_fusions, fusions_df)head(fusions_summary_df %>% arrange(desc(TTYH1_fusions)), n = 10)Summarizing salient points from AlexsLemonade/OpenPBTA-analysis#251:
ETMR, C19MC-altered: These tumors have high expression of LIN28A and serves as a biomarker for ETMRs
CNS NB-FOXR2: Over-expression and/or gene fusions in FOXR2
ETMR, NOS: These tumors have high expression of LIN28A and serves as a biomarker for ETMRs, but do not show focal amplification of C19MC.
exp_genes_of_interest <- c("LIN28A", "FOXR2")
# set up poly-A data
polya_exp <- polya_exp[, exp_genes_of_interest] %>%
as.data.frame() %>%
tibble::rownames_to_column("Kids_First_Biospecimen_ID")
# set up stranded data
stranded_exp <- stranded_exp[, exp_genes_of_interest] %>%
as.data.frame() %>%
tibble::rownames_to_column("Kids_First_Biospecimen_ID")
# bind together both datasets, but keep track of what selection strategy was
# used
exp_df <- bind_rows("polya" = polya_exp,
"stranded" = stranded_exp,
.id = "exp_dataset") %>%
select(-exp_dataset, everything())
# remove data.frame we no longer need
rm(polya_exp, stranded_exp, exp_genes_of_interest)rna_df <- exp_df %>%
left_join(fusions_summary_df) %>%
select(-exp_dataset, everything())Joining, by = "Kids_First_Biospecimen_ID"# remove data.frame we no longer need
rm(exp_df)head(rna_df, n = 10)We’re interested in focal amplification of C19MC (miRNA cluster on chr19) for the ETMR, C19MC-altered subtype.
chr19_cn_df <- cn_consensus %>%
filter(chrom == "chr19",
seg.mean > 0)
chr19_cn_dfThese are not within the ranges from @sjspielman provided here:
## From UCSC Genome Brower, these are positions corresponding to 19q13.41 (C19MC region)
c19mc_region <- "19q13.41"
c19mc_start <- 51400001
c19mc_end <- 53600000We may have to revisit these coordinates. For now, let’s see how this lines up with the RNA-seq data.
The copy numbers in all of these cases are high - we’ll simplify the column name to chr19_amplification.
# get a data.frame of the relevant WGS biospecimen_ids
wgs_ids <- intersect(histologies_df %>%
filter(experimental_strategy == "WGS") %>%
pull(Kids_First_Biospecimen_ID),
biospecimen_ids) %>%
as.data.frame(stringsAsFactors = FALSE) %>%
rename(Kids_First_Biospecimen_ID = ".")
chr19_cn_df <- chr19_cn_df %>%
mutate(chr19_amplification = "Yes") %>%
select(ID, chr19_amplification) %>%
right_join(wgs_ids, by = c("ID" = "Kids_First_Biospecimen_ID")) %>%
replace(is.na(.), "No")
# we no longer need the consensus file
rm(cn_consensus)CNS HGNET-BCOR
- CNS high-grade neuroepithelial tumor with BCOR alteration
- Tumors have internal tandem duplication of BCOR
The definition of internal tandem duplication from Rustagi et al. BMC Bioinformatics. 2016.:
Detection of tandem duplication within coding exons, referred to as internal tandem duplication (ITD)
The annotated files from the focal-cn-file-preparation module use exons to check for overlaps. So, we can try to supplement the Manta calls with the annotated sex chromosome files.
# only include rows from Manta that make reference to BCOR, pass all filters
# https://github.com/Illumina/manta/blob/75b5c38d4fcd2f6961197b28a41eb61856f2d976/docs/userGuide/README.md#vcf-format-fields
# and are recorded as tandem duplication events
bcor_sv_df <- manta_sv_df %>%
filter(str_detect(Gene.name, "BCOR"),
FILTER == "PASS",
ALT == "<DUP:TANDEM>")
# take a look at the genes
bcor_sv_df %>%
head(n = 2) %>%
pull(Gene.name)[1] "ABCB7/ACE2/ACOT9/ADGRG2/AKAP4/ALAS2/AMELX/AMER1/ANOS1/AP1S2/APEX2/APOO/APOOL/AR/ARAF/ARHGAP6/ARHGEF9/ARHGEF9-IT1/ARR3/ARX/ASB11/ASB12/ASB9/ATP6AP2/ATP7A/ATRX/ATXN3L/AWAT1/AWAT2/BCLAF3/BCOR/BEND2/BMP15/BMX/BRDTP1/BRWD3/CA5B/CA5BP1/CA5BP1-CA5B/CACNA1F/CASK/CBLL2/CCDC120/CCDC22/CCNB3/CDK16/CDKL5/CDX4/CENPVL1/CENPVL2/CENPVL3/CFAP47/CFP/CHIC1/CHM/CHMP1B2P/CHST7/CITED1/CLCN4/CLCN5/CLDN34/CLTRN/CNKSR2/COX7B/CPXCR1/CTPS2/CXCR3/CXXC1P1/CXorf21/CXorf38/CXorf49/CXorf49B/CXorf58/CXorf65/CXorf67/CYBB/CYLC1/CYSLTR1/DACH2/DCAF8L1/DCAF8L2/DDX3X/DDX53/DGAT2L6/DGKK/DIAPH2/DIAPH2-AS1/DIPK2B/DLG3/DLG3-AS1/DMD/DMRTC1/DMRTC1B/DUSP21/DYNLT3/EBP/EDA/EDA2R/EFHC2/EFNB1/EGFL6/EIF1AX/EIF1AX-AS1/EIF2S3/ELK1/ERAS/ERCC6L/FAAH2/FAM104B/FAM120C/FAM133A/FAM155B/FAM156A/FAM156B/FAM226A/FAM226B/FAM236A/FAM236B/FAM236C/FAM236D/FAM47A/FAM47B/FAM47C/FAM9A/FAM9B/FAM9C/FANCB/FGD1/FGF16/FLICR/FLJ44635/FOXO4/FOXP3/FOXR2/FRMD8P1/FRMPD4/FTH1P18/FTHL17/FTSJ1/FTX/FUNDC1/GAGE1/GAGE10/GAGE12B/GAGE12C/GAGE12D/GAGE12E/GAGE12F/GAGE12G/GAGE12H/GAGE12I/GAGE12J/GAGE13/GAGE2A/GAGE2B/GAGE2C/GAGE2D/GAGE2E/GAGE4/GAGE5/GAGE6/GAGE7/GAGE8/GATA1/GCNA/GDPD2/GEMIN8/GJB1/GK/GLOD5/GLRA2/GNL3L/GPKOW/GPM6B/GPR143/GPR173/GPR174/GPR34/GPR82/GRIPAP1/GRPR/GS1-594A7.3/GS1-600G8.3/GSPT2/HCCS/HDAC6/HDAC8/HDX/HEPH/HMGN5/HSD17B10/HUWE1/HYPM/IGBP1/IL1RAPL1/IL2RG/INE1/INE2/INGX/IQSEC2/ITGB1BP2/ITIH6/ITM2A/JADE3/JPX/KANTR/KCND1/KDM5C/KDM6A/KIF4A/KLF8/KLHL15/KLHL34/KLHL4/KRBOX4/LANCL3/LAS1L/LINC00269/LINC00891/LINC01186/LINC01203/LINC01204/LINC01278/LINC01281/LINC01282/LINC01284/LINC01456/LINC01496/LINC01545/LINC01560/LINC02154/LINC02595/LINC02601/LOC100129291/LOC100132741/LOC100132831/LOC100421746/LOC101059915/LOC101927476/LOC101927635/LOC101928128/LOC101928201/LOC101928389/LOC101928627/LOC105373156/LOC392452/LOC729609/LPAR4/MAGEB1/MAGEB10/MAGEB16/MAGEB17/MAGEB18/MAGEB2/MAGEB3/MAGEB4/MAGEB5/MAGEB6/MAGED1/MAGED2/MAGED4/MAGED4B/MAGEE1/MAGEE2/MAGEH1/MAGIX/MAGT1/MAOA/MAOB/MAP2K4P1/MAP3K15/MAP7D2/MBTPS2/MED12/MED14/MED14OS/MID1/MID1IP1/MID1IP1-AS1/MIR1321/MIR1468/MIR1587/MIR188/MIR221/MIR222/MIR223/MIR23C/MIR325/MIR325HG/MIR361/MIR362/MIR374A/MIR374B/MIR374C/MIR384/MIR3915/MIR3937/MIR421/MIR4328/MIR4454/MIR4536-1/MIR4536-2/MIR4666B/MIR4767/MIR4768/MIR4769/MIR4770/MIR500A/MIR500B/MIR501/MIR502/MIR532/MIR545/MIR548AJ2/MIR548AM/MIR548AX/MIR548F5/MIR548I4/MIR548M/MIR6086/MIR6134/MIR651/MIR660/MIR676/MIR6857/MIR6894/MIR6895/MIR8088/MIR98/MIRLET7F2/MOSPD2/MPC1L/MSL3/MSN/MTMR8/MTRNR2L10/NAP1L2/NAP1L3/NAP1L6/NBDY/NDP/NDP-AS1/NDUFB11/NEXMIF/NHS/NHS-AS1/NHSL2/NLGN3/NLGN4X/NLRP2B/NONO/NR0B1/NUDT10/NUDT11/NYX/OFD1/OGT/OPHN1/OTC/OTUD5/OTUD6A/P2RY10/P2RY4/PABPC1L2A/PABPC1L2B/PABPC1L2B-AS1/PABPC5/PABPC5-AS1/PAGE1/PAGE2/PAGE2B/PAGE3/PAGE4/PAGE5/PBDC1/PCDH11X/PCSK1N/PCYT1B/PCYT1B-AS1/PDHA1/PDK3/PDZD11/PFKFB1/PGAM4/PGK1/PHEX/PHEX-AS1/PHF8/PHKA1/PHKA1-AS1/PHKA2/PHKA2-AS1/PIGA/PIM2/PIN4/PINCR/PIR/PIR-FIGF/PJA1/PLP2/PNPLA4/POF1B/POLA1/PORCN/POU3F4/PPEF1/PPEF1-AS1/PPP1R2C/PPP1R3F/PPP4R3C/PQBP1/PRAF2/PRDX4/PRICKLE3/PRPS2/PRRG1/PTCHD1/PTCHD1-AS/PUDP/RAB41/RAB9A/RAI2/RBBP7/RBM10/RBM3/REPS2/RGN/RIBC1/RLIM/RP2/RPA4/RPGR/RPS26P11/RPS4X/RPS6KA3/RPS6KA6/RRAGB/RS1/RTL3/RTL5/S100G/SAT1/SATL1/SCARNA23/SCARNA9L/SCML1/SCML2/SH3BGRL/SH3KBP1/SHROOM2/SHROOM4/SLC16A2/SLC35A2/SLC38A5/SLC7A3/SLC9A7/SMC1A/SMPX/SMS/SNORA109/SNORA11/SNORA11C/SNORA11D/SNORA11E/SNORA11G/SNORD3E/SNX12/SPACA5/SPACA5B/SPANXN5/SPIN2A/SPIN2B/SPIN3/SPIN4/SRPX/SSX1/SSX2/SSX2B/SSX3/SSX4/SSX4B/SSX5/SSX6P/SSX7/SSX8P/SSX9P/STARD8/STS/SUPT20HL1/SUPT20HL2/SUV39H1/SYAP1/SYN1/SYP/SYP-AS1/SYTL5/TAB3/TAB3-AS1/TAF1/TAF9B/TBC1D25/TBL1X/TBX22/TCEANC/TENT5D/TEX11/TFE3/TGIF2LX/TIMM17B/TIMP1/TLR7/TLR8/TLR8-AS1/TMEM47/TMSB4X/TRAPPC2/TRO/TSIX/TSPAN7/TSPYL2/TSR2/TTC3P1/TXLNG/UBA1/UBE2DNL/UBE2E4P/UBQLN2/UPRT/UQCRBP1/USP11/USP27X/USP27X-AS1/USP51/USP9X/UXT/UXT-AS1/VCX/VCX2/VCX3A/VCX3B/VEGFD/VENTXP1/VSIG4/WAS/WDR13/WDR45/WNK3/WWC3/XAGE1A/XAGE1B/XAGE2/XAGE3/XAGE5/XIST/XK/YIPF6/YY2/ZC3H12B/ZC4H2/ZCCHC13/ZDHHC15/ZFX/ZFX-AS1/ZMYM3/ZNF157/ZNF182/ZNF41/ZNF630/ZNF630-AS1/ZNF674/ZNF674-AS1/ZNF711/ZNF81/ZRSR2/ZXDA/ZXDB"
[2] "ACE2/ACOT9/ADGRG2/AMELX/ANOS1/AP1S2/APOO/ARHGAP6/ARX/ASB11/ASB9/ATP6AP2/ATXN3L/BCLAF3/BCOR/BEND2/BMX/CA5B/CA5BP1/CA5BP1-CA5B/CASK/CBLL2/CDKL5/CFAP47/CLCN4/CLDN34/CLTRN/CNKSR2/CTPS2/CXorf21/CXorf38/CXorf58/CYBB/DCAF8L1/DCAF8L2/DDX3X/DDX53/DIPK2B/DMD/DUSP21/DYNLT3/EFHC2/EGFL6/EIF1AX/EIF1AX-AS1/EIF2S3/FAM47A/FAM47B/FAM47C/FAM9A/FAM9B/FAM9C/FANCB/FRMPD4/FTH1P18/FTHL17/FUNDC1/GEMIN8/GK/GLRA2/GPM6B/GPR143/GPR34/GPR82/GRPR/GS1-594A7.3/GS1-600G8.3/HCCS/HYPM/IL1RAPL1/INE2/KDM6A/KLHL15/KLHL34/KRBOX4/LANCL3/LINC01186/LINC01203/LINC01204/LINC01281/LINC01282/LINC01456/LINC02154/LINC02595/LINC02601/LOC100132831/LOC101927476/LOC101928389/LOC101928627/LOC392452/LOC729609/MAGEB1/MAGEB10/MAGEB16/MAGEB17/MAGEB18/MAGEB2/MAGEB3/MAGEB4/MAGEB5/MAGEB6/MAOA/MAOB/MAP3K15/MAP7D2/MBTPS2/MED14/MED14OS/MID1/MID1IP1/MID1IP1-AS1/MIR1587/MIR221/MIR222/MIR23C/MIR3915/MIR3937/MIR4666B/MIR4767/MIR4768/MIR548AJ2/MIR548AM/MIR548AX/MIR548F5/MIR6086/MIR6134/MIR651/MOSPD2/MPC1L/MSL3/NDP/NDP-AS1/NHS/NHS-AS1/NR0B1/NYX/OFD1/OTC/PCYT1B/PCYT1B-AS1/PDHA1/PDK3/PHEX/PHEX-AS1/PHKA2/PHKA2-AS1/PIGA/PINCR/PIR/PIR-FIGF/PNPLA4/POLA1/PPEF1/PPEF1-AS1/PPP1R2C/PPP4R3C/PRDX4/PRPS2/PRRG1/PTCHD1/PTCHD1-AS/PUDP/RAB9A/RAI2/RBBP7/REPS2/RPGR/RPS6KA3/RS1/S100G/SAT1/SCARNA23/SCARNA9L/SCML1/SCML2/SH3KBP1/SHROOM2/SMPX/SMS/SRPX/STS/SUPT20HL1/SUPT20HL2/SYAP1/SYTL5/TAB3/TAB3-AS1/TBL1X/TCEANC/TLR7/TLR8/TLR8-AS1/TMEM47/TMSB4X/TRAPPC2/TSPAN7/TXLNG/UBE2E4P/USP9X/VCX/VCX2/VCX3B/VEGFD/VENTXP1/WWC3/XK/YY2/ZFX/ZFX-AS1/ZNF674/ZNF674-AS1/ZRSR2" Not necessarily specific to BCOR alterations.
consensus_sex_chr %>% filter(gene_symbol == "BCOR") cnvkit_sex_chr %>% filter(gene_symbol == "BCOR") controlfreec_sex_chr %>% filter(gene_symbol == "BCOR")This is not conclusive.
Kids_First_Participant_ID and sample_ididentifiers_df <- histologies_df %>%
select(Kids_First_Participant_ID,
sample_id,
Kids_First_Biospecimen_ID)
# add IDs to chromosome 19 data
chr19_cn_df <- identifiers_df %>%
inner_join(chr19_cn_df, by = c("Kids_First_Biospecimen_ID" = "ID"))
# add IDs to RNA data
rna_df <- identifiers_df %>%
inner_join(rna_df)Joining, by = "Kids_First_Biospecimen_ID"molecular_data_df <- full_join(chr19_cn_df, rna_df,
by = c("Kids_First_Participant_ID",
"sample_id")) %>%
rename(Kids_First_Biospecimen_ID_DNA = Kids_First_Biospecimen_ID.x,
Kids_First_Biospecimen_ID_RNA = Kids_First_Biospecimen_ID.y)The following were mentioned in AlexsLemonade/OpenPBTA-analysis#251:
CNS HGNET-BCOR: Median age of diagnosis less than 10 years
CNS HGNET-MN1: Predominantly female patients.
CNS Embryonal, NOS: Tumors previously called PNET that do not fit into other groups above.
Granted, we don’t currently have enough information to look specifically at CNS HGNET-BCOR and therefore perhaps can not classify any tumor as CNS Embryonal, NOS.
relevant_clinical_data <- histologies_df %>%
filter(Kids_First_Biospecimen_ID %in% biospecimen_ids) %>%
select(Kids_First_Participant_ID,
sample_id,
age_at_diagnosis_days,
germline_sex_estimate,
disease_type_old,
disease_type_new) %>%
distinct() %>%
# convert age to years
mutate(age_at_diagnosis_yrs =
floor(as.numeric(age_at_diagnosis_days) / 365)) %>%
select(-age_at_diagnosis_days)
all_data_df <- inner_join(relevant_clinical_data,
molecular_data_df)Joining, by = c("Kids_First_Participant_ID", "sample_id")Write to file.
all_data_df %>%
select(Kids_First_Participant_ID,
sample_id,
Kids_First_Biospecimen_ID_DNA,
Kids_First_Biospecimen_ID_RNA,
age_at_diagnosis_yrs,
germline_sex_estimate,
disease_type_old,
disease_type_new,
LIN28A,
TTYH1_fusions,
chr19_amplification,
FOXR2,
FOXR2_fusions,
MN1_fusions,
CIC_fusions) %>%
write_tsv(output_all)The differences between ETMR, C19MC-altered and ETMR, NOS is a difference in C19MC amplification. We’ll filter on LIN28A overexpression, as that is a feature of both.
Recall that we z-scored these expression values before subsetting the matrix so they are in the context of all samples for a given selection strategy.
all_data_df %>%
# 3 standard deviations above the mean
filter(LIN28A > 3) %>%
select(ends_with("ID"),
starts_with("disease"),
LIN28A,
TTYH1_fusions,
chr19_amplification)For some samples that have a TTYH1 fusion, we do not have DNA data to check for C19MC amplification.
- CNS high-grade neuroepithelial tumor with MN1 alteration
- Likely previously diagnosed as PNET.
- Contain gene fusions involving 5’ MN1. 3’ fusion partners can include BEND2 and CXXC5.
- Predominantly female patients.
all_data_df %>%
# 3 standard deviations above the mean
filter(MN1_fusions != "None") %>%
select(ends_with("ID"),
starts_with("disease"),
germline_sex_estimate,
MN1_fusions)No samples fit this criteria. There were other samples that did contain MN1 fusions.
mn1_fusions <- fusions_summary_df %>%
filter(MN1_fusions != "None")
mn1_fusionshistologies_df %>%
filter(Kids_First_Biospecimen_ID %in% pull(mn1_fusions,
Kids_First_Biospecimen_ID)) %>%
select(Kids_First_Participant_ID,
sample_id,
Kids_First_Biospecimen_ID,
disease_type_old,
disease_type_new,
germline_sex_estimate)They are classified as Ependymoma and Low-grade astrocytic tumor.
- CNS high-grade neuroepithelial tumor with BCOR alteration
- Tumors have internal tandem duplication of BCOR
- Median age of diagnosis less than 10 years
We have not included SV data, yet.
- Central nervous system (CNS) neuroblastoma with FOXR2 activation
- Over-expression and/or gene fusions in FOXR2
all_data_df %>%
filter(FOXR2_fusions != "None" | FOXR2 > 3) %>%
select(ends_with("ID"),
starts_with("disease"),
FOXR2,
FOXR2_fusions)
- CNS Ewing sarcoma family tumor with CIC alteration
- Alterations in CIC, commonly fused with NUTM1
all_data_df %>%
filter(CIC_fusions != "None")
- CNS Embryonal tumor, not otherwise specified
- Tumors previously called PNET that do not fit into other groups above.
We do not have CNS HGNET-BCOR information currently.
sessionInfo()R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 9 (stretch)
Matrix products: default
BLAS/LAPACK: /usr/lib/libopenblasp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C LC_PAPER=en_US.UTF-8
[8] LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3 purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 tibble_2.1.3 ggplot2_3.2.0 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.1 plyr_1.8.4 cellranger_1.1.0 pillar_1.4.2 compiler_3.6.0 tools_3.6.0 jsonlite_1.6 lubridate_1.7.4 nlme_3.1-140 gtable_0.3.0 lattice_0.20-38
[12] pkgconfig_2.0.2 rlang_0.4.0 cli_1.1.0 rstudioapi_0.10 haven_2.1.1 xfun_0.8 withr_2.1.2 xml2_1.2.0 httr_1.4.0 knitr_1.23 generics_0.0.2
[23] hms_0.4.2 grid_3.6.0 tidyselect_0.2.5 glue_1.3.1 R6_2.4.0 readxl_1.3.1 reshape2_1.4.3 modelr_0.1.4 magrittr_1.5 backports_1.1.4 scales_1.0.0
[34] rvest_0.3.4 assertthat_0.2.1 colorspace_1.4-1 stringi_1.4.3 lazyeval_0.2.2 munsell_0.5.0 broom_0.5.2 crayon_1.3.4