Last updated: 2021-07-05

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Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/

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/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/SingleCell_VariancePartition_SCT_Res0.1_minPCT0.2.rds ../output/SingleCell_VariancePartition_SCT_Res0.1_minPCT0.2.rds
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Fig4_VarPartSingleCellRes.0.1.png ../output/figs/Fig4_VarPartSingleCellRes.0.1.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/VarPart.ByCluster.Res0.1.rds ../output/VarPart.ByCluster.Res0.1.rds
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/VarPart.ByCluster.SCres.Plots.pdf ../output/pdfs/VarPart.ByCluster.SCres.Plots.pdf

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File Version Author Date Message
Rmd ee65585 KLRhodes 2021-07-05 wflow_publish("analysis/VarPartPlots_res0.1_SCT.Rmd")
Rmd 285dd16 KLRhodes 2021-07-05 wflow_git_commit(c("analysis/SingleCell_VariancePartitionByCluster_Harmony.Batchindividual_ClusterRes0.1_minPCT0.2.Rmd", 
library(variancePartition)
Loading required package: ggplot2
Loading required package: limma
Loading required package: foreach
Loading required package: scales
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB
The following object is masked from 'package:limma':

    plotMA
The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
    tapply, union, unique, unsplit, which, which.max, which.min
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Attaching package: 'variancePartition'
The following object is masked from 'package:limma':

    classifyTestsF
varpart<- readRDS("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/SingleCell_VariancePartition_SCT_Res0.1_minPCT0.2.rds")
vp<- sortCols(varpart)
plotPercentBars(vp[1:10,])

colnames(vp)<- c("Cluster", "Replicate", "Individual", "Residuals")
G<- plotVarPart(vp)
G

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Fig4_VarPartSingleCellRes.0.1.png", width=8, height=5, units= "in", res=1080)

G
  
dev.off()
#do the genes most effected by individual match what I did in limma?
vp<- vp[order(vp$Individual, decreasing=T),]
head(vp, 30)
             Cluster    Replicate Individual Residuals
EIF1AY   0.017179074 0.0201635369  0.7400354 0.2226220
DDX3Y    0.015241230 0.0394909986  0.5534403 0.3918274
USP9Y    0.030763674 0.0461448311  0.5046933 0.4183982
TYW3     0.026875179 0.0024270382  0.4348307 0.5358671
BEX3     0.075978797 0.0076296320  0.4105769 0.5058146
NDUFB11  0.021293031 0.0113526582  0.4034176 0.5639367
HIST1H1A 0.079550441 0.0476246651  0.3221543 0.5506706
HESX1    0.131045825 0.0282384687  0.3046679 0.5360478
PMEL     0.111753841 0.0117396317  0.2926008 0.5839057
IAH1     0.023797091 0.0041248269  0.2745594 0.6975187
TCEAL4   0.027975274 0.0114853312  0.2524410 0.7080984
MT1F     0.340521072 0.0291377620  0.2506635 0.3796776
TIMM17B  0.014399707 0.0169316047  0.2435549 0.7251138
CHCHD2   0.007111615 0.0024823218  0.2375171 0.7528890
MT-CO1   0.074241583 0.0695424993  0.2328243 0.6233916
GPR143   0.106052993 0.0281560616  0.2278210 0.6379699
PDHA1    0.074651885 0.0116003840  0.2270857 0.6866620
TCEAL5   0.024222672 0.0099600090  0.2234590 0.7423583
USP9X    0.093029122 0.1021567471  0.2158343 0.5889798
UXT      0.019959766 0.0241737193  0.2136661 0.7422004
TCEAL7   0.078318652 0.0002146677  0.2135347 0.7079320
BEX2     0.131248052 0.0093431646  0.2079642 0.6514446
TCEAL8   0.020584809 0.0575414556  0.2064651 0.7154087
EDNRB    0.129067801 0.0211735023  0.2057443 0.6440144
TCEAL9   0.161864688 0.0246674622  0.2003147 0.6131531
TIMP1    0.312132488 0.0027570009  0.1993260 0.4857845
AASS     0.194906768 0.0549847318  0.1990449 0.5510636
MAGEH1   0.075076508 0.0274232980  0.1973866 0.7001136
SMS      0.153756403 0.0748190831  0.1953155 0.5761090
FZD5     0.176126765 0.0089992715  0.1930138 0.6218601
#do the genes most effected by batch match what I did in limma?
vp<- vp[order(vp$Replicate, decreasing=T),]
head(vp, 30)
             Cluster Replicate   Individual Residuals
GIMAP8   0.360376220 0.4218973 0.0021894809 0.2155370
EEF1A1   0.043836198 0.3606096 0.0112124487 0.5843417
GIMAP1   0.489351939 0.3408792 0.0015224285 0.1682465
PPP1CB   0.020738221 0.2805619 0.0059202063 0.6927796
EIF3E    0.049771632 0.2801152 0.0383999920 0.6317132
KLHL6    0.483873526 0.2740501 0.0014882706 0.2405881
MT-ND1   0.066763486 0.2528231 0.1728168463 0.5075966
MYZAP    0.373913854 0.2495968 0.0015462969 0.3749430
OIT3     0.596744814 0.2471812 0.0012308036 0.1548432
MORF4L1  0.032661957 0.2457469 0.0020551649 0.7195360
MRPL42   0.056118836 0.2440401 0.0012990993 0.6985420
ADGRF5   0.650996569 0.2335237 0.0010136889 0.1144660
SCRT2    0.403213098 0.2256859 0.0007248324 0.3703762
ST18     0.421479228 0.2202838 0.0004949852 0.3577420
CAPZA1   0.008856970 0.2159039 0.0040780361 0.7711611
PSMA4    0.012767658 0.2144311 0.0098085331 0.7629928
TMED2    0.031739799 0.2124085 0.0020349108 0.7538168
RSL24D1  0.032166490 0.2112529 0.0004608102 0.7561198
SKIL     0.196438818 0.2099147 0.0036798866 0.5899666
CD163L1  0.395100043 0.2091684 0.0047735006 0.3909581
CHRNA1   0.521622484 0.2084190 0.0031347986 0.2668237
STARD8   0.328068949 0.2082135 0.0037620819 0.4599554
ITGA10   0.603951784 0.2025822 0.0009514104 0.1925146
NSG2     0.439203154 0.2017754 0.0003053412 0.3587162
MORF4L2  0.023526618 0.2000242 0.1814664754 0.5949827
SRP9     0.070302898 0.1987936 0.0825299589 0.6483735
TLR4     0.440699416 0.1939968 0.0023843038 0.3629195
TMEM167A 0.034668240 0.1902355 0.0153045963 0.7597916
PPP1CC   0.064483524 0.1902061 0.0026784432 0.7426319
ATP5MG   0.009518363 0.1892405 0.0032775246 0.7979636
summary(vp$Individual)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
0.000000 0.001460 0.003936 0.010267 0.009719 0.740035 
summary(vp$Replicate)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00381 0.01053 0.02102 0.02552 0.42190 
#genes for which individual contributes more to variance than batch?
vp.indgreaterthanbatch<- vp[vp$Individual>vp$Replicate,]

dim(vp.indgreaterthanbatch) #vp greater than batch for 5266 out of 11356
[1] 3098    4
head(vp.indgreaterthanbatch, 20)
             Cluster  Replicate Individual Residuals
ATP6AP2  0.004918418 0.11830660 0.18082884 0.6959461
PRKY     0.078775919 0.11153476 0.12191813 0.6877712
TNFAIP6  0.051354787 0.10320712 0.14028757 0.7051505
USP9X    0.093029122 0.10215675 0.21583431 0.5889798
USP44    0.365469207 0.09761112 0.11848474 0.4184349
LIN28A   0.198065758 0.08656516 0.10239114 0.6129779
SMC1A    0.047301943 0.08433810 0.09059742 0.7777625
MT1M     0.105977798 0.08024246 0.10238324 0.7113965
SLC38A2  0.024121018 0.07941953 0.08424119 0.8122183
CD70     0.039476345 0.07872565 0.08749644 0.7943016
TOMM20   0.064513971 0.07556030 0.11936872 0.7405570
NR6A1    0.043893033 0.07510756 0.16180868 0.7191907
SMS      0.153756403 0.07481908 0.19531552 0.5761090
CCK      0.034740304 0.07411645 0.08105861 0.8100846
TMEM132C 0.067781901 0.07309619 0.09534396 0.7637779
ARHGAP8  0.072135472 0.07219112 0.08150912 0.7741643
TSTD1    0.270485439 0.07188931 0.07694232 0.5806829
MT-CYB   0.047934904 0.07176506 0.08498571 0.7953143
TSPAN6   0.075833396 0.07154123 0.10542160 0.7472038
RAB4A    0.029308323 0.07104102 0.10424411 0.7954065

Plots for Variance Partition by cluster (Res 0.1, SCT assay)

varpart.byclust<- readRDS("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/VarPart.ByCluster.Res0.1.rds")
vp.bar.list<- NULL

for (i in 1:length(varpart.byclust)){
  v<- varpart.byclust[[i]]
  vp<- sortCols(v)
  vp.bar.list[[i]]<-plotVarPart(vp, main= paste0("Percent variance explained by batch and individual in cluster", (i-1)))
}
  
vp.bar.list
[[1]]


[[2]]


[[3]]


[[4]]


[[5]]


[[6]]


[[7]]

pdf(file = "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/VarPart.ByCluster.SCres.Plots.pdf")

vp.bar.list
[[1]]

[[2]]

[[3]]

[[4]]

[[5]]

[[6]]

[[7]]
dev.off()
png 
  2 
sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
[1] C

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] variancePartition_1.16.1 Biobase_2.46.0           BiocGenerics_0.32.0     
[4] scales_1.1.1             foreach_1.5.0            limma_3.42.2            
[7] ggplot2_3.3.3            workflowr_1.6.2         

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6          lattice_0.20-38     prettyunits_1.1.1  
 [4] gtools_3.8.2        rprojroot_2.0.2     digest_0.6.27      
 [7] plyr_1.8.6          R6_2.5.0            evaluate_0.14      
[10] highr_0.8           pillar_1.4.7        gplots_3.0.4       
[13] rlang_0.4.10        progress_1.2.2      minqa_1.2.4        
[16] gdata_2.18.0        whisker_0.4         nloptr_1.2.2.2     
[19] Matrix_1.2-18       rmarkdown_2.3       labeling_0.4.2     
[22] splines_3.6.1       BiocParallel_1.20.1 lme4_1.1-23        
[25] statmod_1.4.34      stringr_1.4.0       munsell_0.5.0      
[28] compiler_3.6.1      httpuv_1.5.4        xfun_0.16          
[31] pkgconfig_2.0.3     htmltools_0.5.0     tidyselect_1.1.0   
[34] tibble_3.0.4        codetools_0.2-16    crayon_1.3.4       
[37] dplyr_1.0.2         withr_2.4.2         later_1.1.0.1      
[40] MASS_7.3-51.4       bitops_1.0-6        grid_3.6.1         
[43] nlme_3.1-140        gtable_0.3.0        lifecycle_0.2.0    
[46] git2r_0.26.1        magrittr_2.0.1      KernSmooth_2.23-15 
[49] stringi_1.5.3       farver_2.0.3        reshape2_1.4.4     
[52] fs_1.4.2            promises_1.1.1      doParallel_1.0.15  
[55] colorRamps_2.3      ellipsis_0.3.1      generics_0.1.0     
[58] vctrs_0.3.6         boot_1.3-23         iterators_1.0.12   
[61] tools_3.6.1         glue_1.4.2          purrr_0.3.4        
[64] hms_0.5.3           pbkrtest_0.4-8.6    yaml_2.2.1         
[67] colorspace_2.0-0    caTools_1.18.0      knitr_1.29         

sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
[1] C

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] variancePartition_1.16.1 Biobase_2.46.0           BiocGenerics_0.32.0     
[4] scales_1.1.1             foreach_1.5.0            limma_3.42.2            
[7] ggplot2_3.3.3            workflowr_1.6.2         

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6          lattice_0.20-38     prettyunits_1.1.1  
 [4] gtools_3.8.2        rprojroot_2.0.2     digest_0.6.27      
 [7] plyr_1.8.6          R6_2.5.0            evaluate_0.14      
[10] highr_0.8           pillar_1.4.7        gplots_3.0.4       
[13] rlang_0.4.10        progress_1.2.2      minqa_1.2.4        
[16] gdata_2.18.0        whisker_0.4         nloptr_1.2.2.2     
[19] Matrix_1.2-18       rmarkdown_2.3       labeling_0.4.2     
[22] splines_3.6.1       BiocParallel_1.20.1 lme4_1.1-23        
[25] statmod_1.4.34      stringr_1.4.0       munsell_0.5.0      
[28] compiler_3.6.1      httpuv_1.5.4        xfun_0.16          
[31] pkgconfig_2.0.3     htmltools_0.5.0     tidyselect_1.1.0   
[34] tibble_3.0.4        codetools_0.2-16    crayon_1.3.4       
[37] dplyr_1.0.2         withr_2.4.2         later_1.1.0.1      
[40] MASS_7.3-51.4       bitops_1.0-6        grid_3.6.1         
[43] nlme_3.1-140        gtable_0.3.0        lifecycle_0.2.0    
[46] git2r_0.26.1        magrittr_2.0.1      KernSmooth_2.23-15 
[49] stringi_1.5.3       farver_2.0.3        reshape2_1.4.4     
[52] fs_1.4.2            promises_1.1.1      doParallel_1.0.15  
[55] colorRamps_2.3      ellipsis_0.3.1      generics_0.1.0     
[58] vctrs_0.3.6         boot_1.3-23         iterators_1.0.12   
[61] tools_3.6.1         glue_1.4.2          purrr_0.3.4        
[64] hms_0.5.3           pbkrtest_0.4-8.6    yaml_2.2.1         
[67] colorspace_2.0-0    caTools_1.18.0      knitr_1.29