This is nearly entirely based on the code in notebook 09 and that in 11.
We have latent variable expression analysis data - Latent Variable Feather File
For this data we are also using any data for which there are gene variants (cNFs, pNFs, MPNSTs): - Exome-Seq variants - WGS Variants
Let’s see if there are any LVs that split based on gene variant. Because we’re having trouble scaling with the number of latent variables, I only look at variants that occur in less than 5% of the population. notice this is a difference from notebook #11.
wgs.vars=synTableQuery("SELECT Hugo_Symbol,Protein_position,specimenID,IMPACT,FILTER,ExAC_AF,gnomAD_AF FROM syn20551862")$asDataFrame()
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exome.vars=synTableQuery("SELECT Hugo_Symbol,Protein_position,specimenID,IMPACT,FILTER,ExAC_AF,gnomAD_AF FROM syn20554939")$asDataFrame()
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all.vars<-rbind(select(wgs.vars,'Hugo_Symbol','Protein_position','specimenID','IMPACT','gnomAD_AF'),
select(exome.vars,'Hugo_Symbol','Protein_position','specimenID','IMPACT','gnomAD_AF'))%>%
subset(gnomAD_AF<0.01)
#fn <- tempfile(pattern = "", fileext = ".feather")
#download.file('https://github.com/Sage-Bionetworks/nf-lv-viz/raw/master/data/filt_nf_mp_res.feather', destfile = fn)
mp_res<-synTableQuery("SELECT * FROM syn21046991")$asDataFrame()%>%
filter(isCellLine != "TRUE")%>%
select(latent_var,id,value,specimenID,tumorType,modelOf,diagnosis)
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For the purposes of this analysis we want to have only those samples wtih genomic data and only those latent variables that are highly variable.
samps<-intersect(mp_res$specimenID,all.vars$specimenID)
mp_res<-mp_res%>%
subset(specimenID%in%samps)%>%
group_by(latent_var) %>%
mutate(sd_value = sd(value)) %>%
filter(sd_value > 0.025) %>%
ungroup()
Let’s retrieve the LV data and evaluate any correlations between scores and tumor size or patient age
data.with.var<-mp_res%>%subset(specimenID%in%samps)%>%
left_join(all.vars,by='specimenID')
tab<-subset(data.with.var,!tumorType%in%c('Other','High Grade Glioma','Low Grade Glioma'))
top.genes=tab%>%group_by(tumorType)%>%
mutate(numSamps=n_distinct(specimenID))%>%
group_by(tumorType,Hugo_Symbol)%>%
mutate(numMutated=n_distinct(specimenID))%>%
ungroup()%>%
subset(numMutated>1)%>%
subset(numMutated<(numSamps-1))%>%
select(tumorType,Hugo_Symbol,numSamps,numMutated)%>%distinct()
gene.count=top.genes%>%group_by(tumorType)%>%mutate(numGenes=n_distinct(Hugo_Symbol))%>%select(tumorType,numGenes)%>%distinct()
DT::datatable(gene.count)
## Test significance of each gene/immune population
Now we can loop through every tumor type and gene
red.genes<-c("NF1","SUZ12","CDKN2A","EED")##for testing
vals<-tab%>%#subset(Hugo_Symbol%in%red.genes)%>%
mutate(mutated=ifelse(is.na(IMPACT),'WT','Mutated'))%>%
select(latent_var,tumorType,value,Hugo_Symbol,specimenID,mutated)%>%
distinct()%>%
spread(key=Hugo_Symbol,value='mutated',fill='WT')
counts<-vals%>%
gather(key=gene,value=status,-c(latent_var,tumorType,value,specimenID))%>%
select(latent_var,tumorType,value,gene,specimenID,status)%>%
group_by(latent_var,tumorType,gene)%>%
mutate(numVals=n_distinct(status))%>%
subset(numVals==2)%>%ungroup()
#so now we have only
with.sig<-counts%>%ungroup()%>%subset(gene%in%top.genes$Hugo_Symbol)%>%
group_by(latent_var,gene)%>%
mutate(pval=t.test(value~status)$p.value)%>%ungroup()%>%
group_by(latent_var)%>%
mutate(corP=p.adjust(pval))%>%ungroup()%>%
select(latent_var,gene,pval,corP)%>%distinct()
sig.vals<-subset(with.sig,corP<0.05)
DT::datatable(sig.vals)
Interesting! Some genes actually pass p-value correction. What do they look like? Here let’s write the messiest possible code to print.
for(ct in unique(sig.vals$latent_var)){
tplot<-sig.vals[which(sig.vals$latent_var==ct),]
if(nrow(tplot)==0)
next
print(tplot)
p<-counts%>%
subset(latent_var==ct)%>%
subset(gene%in%tplot$gene)%>%
ggplot(aes(x=gene,y=value,col=status))+
geom_boxplot(outlier.shape=NA)+
geom_point(position=position_jitterdodge(),aes(group=status))+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
ggtitle(paste(ct,'scores'))
# if(method=='cibersort')
# p<-p+scale_y_log10()
print(p)
}
## # A tibble: 28 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 24,PID_DELTANP63PATHWAY AASDH 0.0000000955 0.0129
## 2 24,PID_DELTANP63PATHWAY ALDH4A1 0.0000000955 0.0129
## 3 24,PID_DELTANP63PATHWAY ASB6 0.0000000955 0.0129
## 4 24,PID_DELTANP63PATHWAY ASPSCR1 0.0000000955 0.0129
## 5 24,PID_DELTANP63PATHWAY CAPN14 0.0000000955 0.0129
## 6 24,PID_DELTANP63PATHWAY CCDC13 0.0000000955 0.0129
## 7 24,PID_DELTANP63PATHWAY CEP57L1 0.0000000955 0.0129
## 8 24,PID_DELTANP63PATHWAY CTNS 0.0000000955 0.0129
## 9 24,PID_DELTANP63PATHWAY DDIT3 0.0000000955 0.0129
## 10 24,PID_DELTANP63PATHWAY DKFZP434O1614 0.0000000955 0.0129
## # … with 18 more rows
## # A tibble: 7 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 804 ABCC11 3.02e-10 0.0000408
## 2 LV 804 ATG2A 3.58e- 9 0.000483
## 3 LV 804 CARD10 7.39e-11 0.00000996
## 4 LV 804 EPSTI1 3.02e-10 0.0000408
## 5 LV 804 L3HYPDH 2.49e-11 0.00000336
## 6 LV 804 SCGN 2.49e-11 0.00000336
## 7 LV 804 WWC3 2.27e-11 0.00000307
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 937,PID_HIF1_TFPATHWAY AC078925.1 0.00000000201 0.000272
## 2 937,PID_HIF1_TFPATHWAY ZDHHC11B 0.00000000201 0.000272
## # A tibble: 6 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 891 ADAMTS3 0.000000350 0.0472
## 2 LV 891 PLA1A 0.000000350 0.0472
## 3 LV 891 RASSF7 0.0000000892 0.0120
## 4 LV 891 UNC45B 0.0000000892 0.0120
## 5 LV 891 VCX3B 0.00000000501 0.000676
## 6 LV 891 ZNF335 0.000000350 0.0472
## # A tibble: 10 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 117 AP5M1 0.000000268 0.0362
## 2 LV 117 BRICD5 0.000000268 0.0362
## 3 LV 117 CYP4B1 0.000000268 0.0362
## 4 LV 117 GSDMC 0.000000268 0.0362
## 5 LV 117 MT1H 0.000000268 0.0362
## 6 LV 117 MUC6 0.000000268 0.0362
## 7 LV 117 OR4C5 0.000000268 0.0362
## 8 LV 117 OR5M11 0.000000268 0.0362
## 9 LV 117 SLC25A21 0.000000268 0.0362
## 10 LV 117 WDR52 0.000000268 0.0362
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 418 APC 0.0000000102 0.00138
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 588 APC 0.00000000370 0.000500
## # A tibble: 4 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 72 APC 0.0000000516 0.00697
## 2 LV 72 EIF2S3L 0.000000131 0.0177
## 3 LV 72 HNRNPDL 0.0000000767 0.0103
## 4 LV 72 MYCT1 0.000000131 0.0177
## # A tibble: 13 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 17,SVM NK cells resting ARHGAP29 0.0000000246 0.00332
## 2 17,SVM NK cells resting ATRX 0.0000000246 0.00332
## 3 17,SVM NK cells resting C3 0.0000000246 0.00332
## 4 17,SVM NK cells resting CCSER2 0.0000000246 0.00332
## 5 17,SVM NK cells resting CMIP 0.0000000246 0.00332
## 6 17,SVM NK cells resting IGIP 0.0000000246 0.00332
## 7 17,SVM NK cells resting NCF2 0.0000000246 0.00332
## 8 17,SVM NK cells resting RP1-241P17.4 0.0000000246 0.00332
## 9 17,SVM NK cells resting SAP30L 0.0000000246 0.00332
## 10 17,SVM NK cells resting SEMA3A 0.0000000246 0.00332
## 11 17,SVM NK cells resting SLC24A5 0.0000000246 0.00332
## 12 17,SVM NK cells resting SLC3A2 0.0000000246 0.00332
## 13 17,SVM NK cells resting SMCHD1 0.0000000246 0.00332
## # A tibble: 15 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 663 ARHGAP29 5.87e- 8 0.00791
## 2 LV 663 ATRX 5.87e- 8 0.00791
## 3 LV 663 C3 5.87e- 8 0.00791
## 4 LV 663 CCSER2 5.87e- 8 0.00791
## 5 LV 663 CMIP 5.87e- 8 0.00791
## 6 LV 663 IGHV3-38 6.04e-12 0.000000815
## 7 LV 663 IGIP 5.87e- 8 0.00791
## 8 LV 663 ITM2B 6.04e-12 0.000000815
## 9 LV 663 NCF2 5.87e- 8 0.00791
## 10 LV 663 RP1-241P17.4 5.87e- 8 0.00791
## 11 LV 663 SAP30L 5.87e- 8 0.00791
## 12 LV 663 SEMA3A 5.87e- 8 0.00791
## 13 LV 663 SLC24A5 5.87e- 8 0.00791
## 14 LV 663 SLC3A2 5.87e- 8 0.00791
## 15 LV 663 SMCHD1 5.87e- 8 0.00791
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 861 C9orf129 0.000000206 0.0278
## # A tibble: 3 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 720,PID_FANCONI_PATHWAY CBR1 0.0000000934 0.0126
## 2 720,PID_FANCONI_PATHWAY PCMTD1 0.000000153 0.0207
## 3 720,PID_FANCONI_PATHWAY SPATA31A3 0.0000000934 0.0126
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 407 CEP170 0.000000207 0.0279
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 615 CNKSR2 0.00000000104 0.000140
## # A tibble: 3 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 88 EIF2S3L 0.0000000704 0.00950
## 2 LV 88 MYCT1 0.0000000704 0.00950
## 3 LV 88 SCAMP3 0.0000000598 0.00807
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 666 ESRRA 0.000000141 0.0190
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 839 ESRRA 0.000000219 0.0295
## # A tibble: 3 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 979 FAM160A2 0.0000000997 0.0135
## 2 LV 979 RALBP1 0.0000000997 0.0135
## 3 LV 979 SERPINB6 0.000000241 0.0326
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 4,REACTOME_NEURONAL_SYSTEM FAM185A 0.00000000426 0.000575
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 827,KEGG_B_CELL_RECEPTOR_SIGNALING_PATHWAY FAM185A 1.34e-10 0.0000180
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 100 FAM185A 0.000000178 0.0241
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 315 FAM185A 5.62e-10 0.0000758
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 331 FAM185A 5.40e-10 0.0000728
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 484 FAM185A 0.000000135 0.0182
## 2 LV 484 PPP1R13B 0.000000147 0.0199
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 529 FAM185A 0.0000000456 0.00615
## # A tibble: 3 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 9 FAM185A 0.0000000536 0.00723
## 2 LV 9 PHF3 0.0000000669 0.00903
## 3 LV 9 SCAMP3 0.000000117 0.0158
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 517,REACTOME_SIGNALING_BY_EGFR_IN_CANCER GOLGA8S 0.0000000141 0.00190
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 120 GOLGA8T 5.24e-11 0.00000706
## 2 LV 120 TRBV11-1 1.57e- 7 0.0212
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 821 GOLGA8T 0.0000000709 0.00957
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 969 GPANK1 0.000000189 0.0255
## 2 LV 969 VCX3B 0.000000363 0.0490
## # A tibble: 3 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 326 GXYLT1 0.000000115 0.0155
## 2 LV 326 LRRC45 0.000000113 0.0153
## 3 LV 326 VCX3B 0.000000142 0.0192
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 870 HIVEP1 0.000000228 0.0308
## # A tibble: 3 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 754 HPS5 0.000000228 0.0307
## 2 LV 754 MYOM3 0.000000296 0.0399
## 3 LV 754 PNPLA1 0.000000296 0.0399
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 743,MIPS_55S_RIBOSOME_MITOCHONDRIAL IGHV3-38 0.0000000368 0.00497
## 2 743,MIPS_55S_RIBOSOME_MITOCHONDRIAL ITM2B 0.0000000368 0.00497
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 82,PID_RAC1_PATHWAY KRT18 0.00000000310 0.000418
## 2 82,PID_RAC1_PATHWAY TRBV11-1 0.000000122 0.0164
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 186 KRTAP2-2 0.0000000628 0.00847
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 904 KRTAP2-2 0.000000314 0.0423
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 767,SVM B cells naive LRCH4 0.000000345 0.0465
## 2 767,SVM B cells naive TPSD1 0.0000000251 0.00338
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 13,REACTOME_GLUCOSE_METABOLISM LRRC45 0.0000000279 0.00377
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 557,SVM Dendritic cells resting LRRC45 0.00000000836 0.00113
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 637,REACTOME_METABOLISM_OF_LIPIDS_AND_LIPOPROTE… LRRC45 1.48e-7 0.0199
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 879,REACTOME_HEPARAN_SULFATE_HEPARIN_HS_GAG_ME… LRRC… 2.07e-8 0.00280
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 975,SVM T cells CD4 memory resting LRRC45 0.0000000720 0.00971
## 2 975,SVM T cells CD4 memory resting TRBV11-1 0.0000000674 0.00910
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 479 LRRC45 0.0000000122 0.00164
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 504 LRRC45 0.0000000613 0.00827
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 745 LRRC45 0.00000000226 0.000304
## 2 LV 745 TRBV11-1 0.000000149 0.0201
## # A tibble: 2 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 884 LRRC45 0.0000000990 0.0134
## 2 LV 884 OR10G2 0.0000000911 0.0123
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 674 LRRIQ1 0.0000000372 0.00502
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 54 NAPA 0.00000000203 0.000274
## # A tibble: 3 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 840,MIPS_39S_RIBOSOMAL_SUBUNIT_MITOCHONDRIAL PHF3 5.35e-10 0.0000722
## 2 840,MIPS_39S_RIBOSOMAL_SUBUNIT_MITOCHONDRIAL RPL10L 1.15e- 8 0.00155
## 3 840,MIPS_39S_RIBOSOMAL_SUBUNIT_MITOCHONDRIAL SCAMP3 7.65e- 8 0.0103
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 671,REACTOME_COLLAGEN_FORMATION PRDM2 0.0000000200 0.00270
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 595 PURA 0.0000000224 0.00302
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 163 RAB44 0.000000224 0.0303
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 865,PID_FANCONI_PATHWAY RGPD8 0.0000000398 0.00537
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 742 SCAMP3 5.78e-14 0.00000000780
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 16 TBC1D3B 1.60e-11 0.00000216
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 192 TRBV11-1 0.000000347 0.0468
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 245 TRBV11-1 0.000000201 0.0272
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 282 TRBV11-1 0.0000000103 0.00139
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 423 TRBV11-1 0.00000000796 0.00107
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 465 TRBV11-1 0.000000131 0.0177
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 672 TRBV11-1 0.000000123 0.0166
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 940 TRBV11-1 0.00000000971 0.00131
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 42,DMAP_HSC1 VCX3B 0.000000138 0.0187
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 66,REACTOME_METABOLISM_OF_LIPIDS_AND_LIPOPROTE… VCX3B 3.79e-8 0.00511
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 70,MIPS_PA700_20S_PA28_COMPLEX VCX3B 0.000000272 0.0367
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 149 VCX3B 0.000000251 0.0339
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 571 VCX3B 0.0000000251 0.00339
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 629 VCX3B 2.02e-11 0.00000272
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 634 VCX3B 0.0000000188 0.00254
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 676 VCX3B 0.00000000985 0.00133
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 760 VCX3B 0.0000000284 0.00383
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 851 VCX3B 0.0000000291 0.00393
## # A tibble: 1 x 4
## latent_var gene pval corP
## <chr> <chr> <dbl> <dbl>
## 1 LV 976 VCX3B 0.00000000814 0.00110
#}
At first glance it seems that a lot of these are separating out cNFs (i.e. mast cell signaling) from other types. However, I’m getting the same error I get in notebook number 11, so am unsure about how to proceed.
#this is a failed attempt to group by tumor type
#with.sig<-counts%>%ungroup()%>%subset(gene%in%top.genes$Hugo_Symbol)%>%
# group_by(latent_var,tumorType,gene)%>%
# mutate(pval=t.test(value~status)$p.value)%>%
# ungroup()%>%
# group_by(latent_var)%>%
# mutate(corP=p.adjust(pval))%>%ungroup()%>%
# select(latent_var,tumorType,gene,pval,corP)%>%distinct()
#sig.vals<-subset(with.sig,corP<0.05)
#DT::datatable(sig.vals)