rm(list = ls(all.names = TRUE))
seed = 123456789
set.seed(seed)
percent = 1
library(GeneViability)
library(car)
## Loading required package: carData
library(uwot)
## Loading required package: Matrix
library('ggRandomForests')
## Loading required package: randomForestSRC
##
## randomForestSRC 2.9.0
##
## Type rfsrc.news() to see new features, changes, and bug fixes.
##
##
## Attaching package: 'ggRandomForests'
## The following object is masked from 'package:randomForestSRC':
##
## partial.rfsrc
removeDuplicated = function(x, id = 'MGI id') {
dplt = duplicated(x[, id])
dpll = duplicated(x[, id], fromLast = TRUE)
message('Duplicated: ', length(unique(x[, id][dplt])))
return(x[!(dplt | dpll), ])
}
message('Machine name: ', paste(Sys.info()[1:5], collapse = ' | '))
## Machine name: Windows | 10 x64 | build 17134 | HAMED-W10L | x86-64
# http://rpubs.com/hamedhm/368692 <-- human+DMDD
# http://rpubs.com/hamedhm/372453 <-- human+DMDD+SHet - Before revising duplicates
# http://rpubs.com/hamedhm/374473 <-- human+DMDD+SHet - After revising duplicates
# http://rpubs.com/hamedhm/425419 <-- human+dmdd+Shet+436lines+GTEX+PLI - First version on 3/10/2018
#### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
# Loading data
# 1
load(file = 'All_Human_Cells_data.Rdata')
load(file = '436_lines_Human.Rdata')
load(file = 'DMDD.RData')
load(file = 'GTEX.Rdata')
load(file = 'PLI.Rdata')
load(file = 'Human shet scores from CASSA.Rdata')
load(file = 'DR7_viability.Rdata')
load(file = 'viabi_DR8.Rdata')
#### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
#### #### #### #### #### #### ####
# 1.1 removing unnecessary columns from the data
RemoveUnncessaryColumns = function(x, cnames) {
unnc = !grepl0(pattern = cnames,
x = colnames(x),
fixed = TRUE)
y = as.data.frame(x[, unnc])
return(y)
}
###########
overall_human_cell_lines = RemoveUnncessaryColumns(
x = overall_human_cell_lines,
cnames = c(
'Hart_A375_GeCKo_BF',
'Hart_HCT116_shRNA_BF',
"HGNC ID" ,
"Gene_symbol_Wang" ,
"Gene_symbol_Hart",
"Gene_symbol_Blomen" ,
"ENSEMBL_ID_Blomen" ,
"HGNC_id_source_Wang+Hart",
"HGNC_id_source_Blomen" ,
"Reference" ,
"Match type",
"Approved symbol" ,
"Approved symbol: unique/conflicting" ,
"Location",
"human_entrez_gene",
"human_ensembl_gene" ,
"hgnc_id" ,
"human_name",
"human_symbol" ,
"human_chr" ,
"human_assert_ids",
"mouse_entrez_gene" ,
"mouse_ensembl_gene",
"mouse_name" ,
"mouse_symbol" ,
"mouse_chr",
"mouse_assert_ids" ,
"score" ,
"support",
"Result_merge"
)
)
#### #### #### #### #### #### ####
DMDD_test = RemoveUnncessaryColumns(
DMDD_test,
c(
"Gene ID" ,
"Gene Name" ,
"Matching by ENSMUSG ID" ,
"Matching by GeneSymbol" ,
"Updated Gene ID" ,
"Updated GeneSymbol" ,
"Matched_by_MGI_ID" ,
"Gene" ,
"corrected_Viability" ,
"Primary_species" ,
"Ortholog_species" ,
"Primary_symbol" ,
"Ortholog_symbol" ,
"Primary_species_DBID" ,
"Ortholog_species_DBID" ,
"Primary_Ensembl_ID" ,
"Ortholog_Ensembl_ID" ,
"Primary_NCBI_Gene_ID" ,
"Ortholog_NCBI_Gene_ID" ,
"Score_Violeta" ,
"Assertion_source(s)"
)
)
#### #### #### #### #### #### ####
shet = RemoveUnncessaryColumns(
shet,
c(
"HGNC ID",
"Input_Cassa",
"Match type",
"Approved symbol",
"Approved name",
"Location",
"human_entrez_gene",
"human_ensembl_gene",
"hgnc_id",
"human_name",
"human_symbol",
"human_chr",
"human_assert_ids",
"mouse_entrez_gene",
"mouse_ensembl_gene",
"mouse_name",
"mouse_symbol",
"mouse_chr",
"mouse_assert_ids",
"score",
"support",
"Result_merge"
)
)
#### #### #### #### #### #### ####
human_cells_436 = RemoveUnncessaryColumns(
human_cells_436,
c(
'Gene',
'HGNC_ID',
'Approved_symbol',
'support_count',
'mouse_symbol',
'mouse_ensembl_gene',
'duplicate_flag',
'support_count'
)
)
# Human scores black list (outliers) by Violeta
human_cells_436 = human_cells_436[!human_cells_436$`MGI id` %in% c(
'MGI:1916008',
'MGI:1277162',
'MGI:1913882',
'MGI:1929520',
'MGI:1917761',
'MGI:1919669',
'MGI:2663979',
'MGI:107953',
'MGI:97753',
'MGI:1096367',
'MGI:1929092',
'MGI:1343044',
'MGI:98038',
'MGI:2655401',
'MGI:1913462',
'MGI:1351663',
'MGI:1342294'
), ]
#### #### #### #### #### #### ####
DR7_viability = RemoveUnncessaryColumns(
DR7_viability,
c(
"mouse_symbol",
"mouse_chr" ,
"homozygosity",
"hgnc_id",
"human_symbol",
"score"
)
)
#### #### #### #### #### #### ####
PLI = RemoveUnncessaryColumns(
PLI,
c(
"Approved_symbol",
"HGNC_ID" ,
"mouse_symbol" ,
"mouse_chr" ,
"support_count" ,
"duplicate_flag"
)
)
#### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
# Removing duplicated
overall_human_cell_lines = removeDuplicated(overall_human_cell_lines)
## Duplicated: 250
human_cells_436 = removeDuplicated(human_cells_436)
## Duplicated: 0
DMDD_test = removeDuplicated(DMDD_test)
## Duplicated: 13
GTEX = removeDuplicated(GTEX)
## Duplicated: 231
shet = removeDuplicated(shet)
## Duplicated: 138
PLI = removeDuplicated(PLI)
## Duplicated: 0
DR7_viability = removeDuplicated(DR7_viability)
## Duplicated: 0
viabi_DR8 = removeDuplicated(viabi_DR8)
## Duplicated: 149
#### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
# Exclusive dimension reduction (PCA or UMAP (TSNE))
pcaUmap0 = function(x,
main = 'PCA',
active = TRUE,
cname = 'V',
umap = TRUE,
ncomp = 10,
...) {
if (active) {
x2 = x
num.cols = sapply(x, is.numeric)
if (umap) {
message('Umap in progress ...')
newx = umap(X = x[, num.cols], n_components = ncomp , ...)
} else{
message('pca in progress ...')
pca = prcomp(x[, num.cols], ...)
plot(pca, main = main)
abline(
v = ncomp,
lwd = 2,
lty = 2,
col = 4
)
newx = pca$x
}
newx = data.frame(newx[, 1:ncomp])
names(newx) = paste0(cname, 1:ncomp)
x2 = data.frame(x[, !num.cols], newx, check.names = FALSE)
} else{
x2 = x
}
return(x2)
}
#### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
# 2. Preprocess and dimension reduction
#### 2.1 DMDD
aM = initialization(data = DMDD_test,
speed = TRUE,
seed = seed)
##
## Response is not found and then set to NULL
##
##
## Input dataset : rows = 27640 | columns = 29
##
##
## Variable names (29):
## MGI id|4-somite stage|5-somite stage|6-somite stage|7-somite stage|8-somite stage|9-somite stage|10-somite stage|11-somite stage|12-somite stage|13-somite stage|14-somite stage|15-somite stage|16-somite stage|17-somite stage|18-somite stage|19-somite stage|20-somite stage|21-somite stage|22-somite stage|23-somite stage|24-somite stage|25-somite stage|26-somite stage|27-somite stage|28-somite stage|34-somite stage|35-somite stage|36-somite stage
## No duplicate found!
##
##
##
## Response is not included!
##
##
##
## Missing data more than 60 percent in rows (5911=21%) [removed]: 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8416|MGI:5610168|MGI:5610692|MGI:5610691|MGI:5589015|MGI:5611277|MGI:5611260|MGI:3647848|MGI:5610329|MGI:5610883|MGI:5611570|MGI:5611580|MGI:5611428|MGI:1917549|MGI:5610474|MGI:5611154|MGI:5611152|MGI:5011630|MGI:5610600|MGI:5610598|MGI:1920499|MGI:5611313|MGI:5611567|MGI:5611011|MGI:5610452|MGI:5610458|MGI:5610457|MGI:5611362|MGI:5611365|MGI:5610797|MGI:5610799|MGI:5610267|MGI:5610923|MGI:5610263|MGI:5610264|MGI:5610469|MGI:5611193|MGI:5610672|MGI:5611076|MGI:5610903|MGI:5010738|MGI:5611451|MGI:2443520|MGI:5011879|MGI:5610308|MGI:5611605|MGI:1924793|MGI:5610450|MGI:5610602|MGI:5610447|MGI:5610446|MGI:5611541|MGI:3647903|MGI:5611536|MGI:5610846|MGI:5611162|MGI:5591603|MGI:5610570|MGI:5611490|MGI:5610571|MGI:2444780|MGI:5610830|MGI:5610826|MGI:3642170|MGI:5610411|MGI:3644588|MGI:5611391|MGI:5611389|MGI:5611392|MGI:5611479|MGI:3780614|MGI:5611480|MGI:5611483|MGI:5610940|MGI:5610938|MGI:5611041|MGI:5611045|MGI:5610218|MGI:5610240|MGI:3780102|MGI:5610611|MGI:5611264|MGI:5610312|MGI:5610868|MGI:5610365|MGI:5610364|MGI:5610363|MGI:5610510|MGI:3647116|MGI:1923259|MGI:5611039|MGI:5611491|MGI:5611493|MGI:5611492|MGI:5593335|MGI:5590887|MGI:5594743|MGI:5611344|MGI:5610780|MGI:5610778|MGI:5611111|MGI:5610246|MGI:5610444|MGI:5611173|MGI:5611549|MGI:5593886|MGI:5611546|MGI:1924441|MGI:5611303|MGI:1925574|MGI:5313137|MGI:5610732|MGI:3648010|MGI:5610776|MGI:5610777|MGI:5610734|MGI:5610733|MGI:5590485|MGI:3704186|MGI:3780888|MGI:5610539|MGI:5611501|MGI:5611496|MGI:5010371|MGI:5610197|MGI:5611337|MGI:5595466|MGI:2444775|MGI:5610783|MGI:1298371|MGI:5610786|MGI:5610275|MGI:1922399|MGI:5610619|MGI:5610618|MGI:3641876|MGI:5611205|MGI:5611209|MGI:5610970|MGI:3645533|MGI:5611375|MGI:5611374|MGI:5610398|MGI:5610317|MGI:5610874|MGI:5610871|MGI:5610872|MGI:5610870|MGI:5610876|MGI:5611367|MGI:3648243|MGI:5611514|MGI:5610476|MGI:5610958|MGI:5010155|MGI:5610388|MGI:5610387|MGI:5611317|MGI:5593926|MGI:5611319|MGI:5610747|MGI:1891442|MGI:5610875|MGI:1917703|MGI:3644273|MGI:3780221|MGI:5610207|MGI:1920491|MGI:5611463|MGI:5611032|MGI:5011520|MGI:1298370|MGI:5611586|MGI:1918282|MGI:5610630|MGI:5610634|MGI:5662629|MGI:5611223|MGI:5588930|MGI:3704315|MGI:5610280|MGI:5610281|MGI:5596047|MGI:3780316|MGI:3643373|MGI:5610821|MGI:5611384|MGI:5610409|MGI:5610408|MGI:5610404|MGI:5610984|MGI:5611529|MGI:5611530|MGI:5611532|MGI:5611535|MGI:5611158|MGI:5610567|MGI:5610224|MGI:2444677|MGI:3780442|MGI:5610220|MGI:5610359|MGI:1922713|MGI:5611326|MGI:5611325|MGI:5611228|MGI:5611229|MGI:5610640|MGI:5610638|MGI:5610637|MGI:5610635|MGI:5610660|MGI:1924651|MGI:5610490|MGI:5610489|MGI:1917693|MGI:3030123|MGI:5610896|MGI:5611445|MGI:5611446|MGI:5594226|MGI:5610342|MGI:2443717|MGI:5611553|MGI:5611281|MGI:2444685|MGI:5610582|MGI:5610584|MGI:5010137|MGI:5611175|MGI:5610423|MGI:1916641|MGI:5611179|MGI:5610587|MGI:1920438|MGI:5590565|MGI:5610935|MGI:5610433|MGI:5610429|MGI:1925652|MGI:1920203|MGI:5610300|MGI:2444046|MGI:5610759|MGI:5010062|MGI:3708672|MGI:5592381|MGI:3780305|MGI:5611266|MGI:5594458|MGI:5610323|MGI:5610325|MGI:5610879|MGI:5610881|MGI:5611469|MGI:5611464|MGI:5610511|MGI:3648115|MGI:5610516|MGI:5611088|MGI:5611086|MGI:5611084|MGI:2447322|MGI:5610162|MGI:5610161|MGI:5610160|MGI:1920155|MGI:5610158|MGI:5610159|MGI:5610752|MGI:5610750|MGI:5610754|MGI:5610753|MGI:3781920|MGI:5610291|MGI:5610289|MGI:5611233|MGI:3645908|MGI:5611237|MGI:5610836|MGI:5610838|MGI:5610841|MGI:5590425|MGI:5611037|MGI:5610478|MGI:2442787|MGI:5610648|MGI:5610234|MGI:5610725|MGI:5610730|MGI:5611124|MGI:5611123|MGI:5611122|MGI:3642594|MGI:5611120|MGI:5610192|MGI:3525150|MGI:3646603|MGI:5588877|MGI:5610948|MGI:5595547|MGI:3644362|MGI:5589332|MGI:1917165|MGI:5610907|MGI:5610694|MGI:5012490|MGI:5611259|MGI:5611257|MGI:1921007|MGI:5611254|MGI:5611563|MGI:5610592|MGI:5610443|MGI:3649088|MGI:5610440|MGI:5610439|MGI:5611001|MGI:1920357|MGI:5611000|MGI:5610794|MGI:5611353|MGI:5611360|MGI:5611358|MGI:3646099|MGI:5611184|MGI:5611183|MGI:5610424|MGI:1922147|MGI:5610254|MGI:5610255|MGI:5610251|MGI:5610252|MGI:5611506|MGI:3646388|MGI:5588898|MGI:5611503|MGI:1925848|MGI:2443553|MGI:3641917|MGI:5611107|MGI:5610525|MGI:5610526|MGI:5610712|MGI:5610710|MGI:5610716|MGI:5610714|MGI:5610180|MGI:5610368|MGI:5610370|MGI:2681880|MGI:5611333|MGI:5012274|MGI:5610930|MGI:5611048|MGI:5610627|MGI:1925283|MGI:5611212|MGI:5611215|MGI:1923142|MGI:2447824|MGI:5610817|MGI:5610815|MGI:5610816|MGI:5611382|MGI:5012357|MGI:5610975|MGI:5010714|MGI:3647094|MGI:5610685|MGI:5611268|MGI:5610203|MGI:5610200|MGI:5611140|MGI:1298369|MGI:5610549|MGI:5610551|MGI:5611511|MGI:3642357|MGI:3646806|MGI:5611309|MGI:5610738|MGI:5611187|MGI:5611192|MGI:5611238|MGI:5434106|MGI:5611240|MGI:5611239|MGI:5610295|MGI:1920315|MGI:1925171|MGI:5611596|MGI:5610654|MGI:5610483|MGI:5610889|MGI:5010883|MGI:5610891|MGI:5611438|MGI:5611439|MGI:3642542|MGI:5592301|MGI:5611294|MGI:5611290|MGI:5011737|MGI:5610339|MGI:3642807|MGI:5610965|MGI:5610963|MGI:5593153|MGI:5610391|MGI:5610389|MGI:5610564|MGI:5610561|MGI:1920600|MGI:3779614|MGI:5663279|MGI:3782793|MGI:5562763|MGI:5663392|MGI:5663147|MGI:3642056|MGI:5662757|MGI:5662838|MGI:1918174|MGI:1925506|MGI:5663298|MGI:5663282|MGI:4887388|MGI:5662916|MGI:2685413|MGI:1924248|MGI:5662678|MGI:5663911|MGI:5649069|MGI:5663076|MGI:5663246|MGI:5663951|MGI:5663738|MGI:3781382|MGI:5621296|MGI:3645990|MGI:5662914|MGI:5663513|MGI:3646905|MGI:5663036|MGI:5589182|MGI:5663083|MGI:1921047|MGI:5662621|MGI:2140937|MGI:5663210|MGI:3639287|MGI:5690700|MGI:5439414|MGI:6096119|MGI:5621297|MGI:5663828|MGI:5012028|MGI:5011333|MGI:5690746|MGI:5663245|MGI:5663002|MGI:5662756|MGI:1922320|MGI:5662618|MGI:5663035|MGI:3648206|MGI:5663157|MGI:2444788|MGI:2442445|MGI:3645184|MGI:5663865|MGI:3642120|MGI:5662681|MGI:5663524|MGI:5663534|MGI:5662788|MGI:5663848|MGI:5663295|MGI:1919269|MGI:5663644|MGI:1925479|MGI:5663844|MGI:5663855|MGI:5663142|MGI:1924178|MGI:5663160|MGI:5562779|MGI:5663139|MGI:1919710|MGI:5663045|MGI:5594874|MGI:5011289|MGI:1916674|MGI:1922635|MGI:5663573|MGI:5012116|MGI:1917213|MGI:5663126|MGI:1922233|MGI:6096115|MGI:5663789|MGI:3644419|MGI:5663683|MGI:5663684|MGI:1922739|MGI:5663813|MGI:5662813|MGI:5690847|MGI:5662623|MGI:5662619|MGI:5662942|MGI:5623075|MGI:5663250|MGI:5595070|MGI:5663502|MGI:5663343|MGI:5594666|MGI:5663866|MGI:5662824|MGI:97986|MGI:5562754|MGI:5663902|MGI:5662607|MGI:5011751|MGI:5663378|MGI:3781322|MGI:2444617|MGI:5011351|MGI:5663490|MGI:5453154|MGI:3646277|MGI:5663721|MGI:1918479|MGI:5663875|MGI:3779442|MGI:5621298|MGI:5625090|MGI:1925070|MGI:5663286|MGI:5662570|MGI:5663228|MGI:5663581|MGI:5690850|MGI:5663088|MGI:5648986|MGI:5622925|MGI:5663470|MGI:5663118|MGI:5663620|MGI:5663665|MGI:5662980|MGI:1920712|MGI:3642767|MGI:5589772|MGI:5663833|MGI:6096147|MGI:5663888|MGI:1924319|MGI:5589024|MGI:5663453|MGI:1920829|MGI:5663954|MGI:5663381|MGI:5663693|MGI:5623002|MGI:5662740|MGI:5662784|MGI:3643358|MGI:5663611|MGI:5662688|MGI:5595694|MGI:5663285|MGI:5662652|MGI:5562728|MGI:5663538|MGI:4950384|MGI:5452674|MGI:5662944|MGI:5663905|MGI:6096185|MGI:3647070|MGI:5663438|MGI:5662777|MGI:5662751|MGI:5663109|MGI:5662742|MGI:3646382|MGI:5663882|MGI:5663337|MGI:5662808|MGI:3645661|MGI:5662726|MGI:5663161|MGI:5621447|MGI:5663132|MGI:5662728|MGI:5662673|MGI:5663119|MGI:2429943|MGI:5690801|MGI:5662573|MGI:2444377|MGI:5662974|MGI:5663627|MGI:5663656|MGI:3779890|MGI:5662572|MGI:3647044|MGI:5593407|MGI:5690869|MGI:1922633|MGI:3648119|MGI:3647155|MGI:5662881|MGI:5663424|MGI:5690800|MGI:5663108|MGI:5662819|MGI:5663799|MGI:5663388|MGI:5662725|MGI:5663501|MGI:5663323|MGI:5592155|MGI:5663075|MGI:5590207|MGI:5662752|MGI:5663409|MGI:5591317|MGI:5662617|MGI:5663271|MGI:5663642|MGI:5663026|MGI:1923648|MGI:5663154|MGI:1922357|MGI:5663112|MGI:5662934|MGI:5590465|MGI:3779561|MGI:5663257|MGI:5663374|MGI:5591040|MGI:5662724|MGI:5662860|MGI:5662735|MGI:5663659|MGI:5662577|MGI:5662580|MGI:3643936|MGI:5662956|MGI:1924313|MGI:5663110|MGI:1914904|MGI:1918422|MGI:5662593|MGI:5562785|MGI:3647478|MGI:5663267|MGI:5662562|MGI:5663744|MGI:4358947|MGI:1922619|MGI:5663143|MGI:3642597|MGI:3647442|MGI:5594224|MGI:5010141|MGI:5011167|MGI:5011170|MGI:3028058|MGI:5663663|MGI:5662763|MGI:5663393|MGI:5663106|MGI:5663603|MGI:5663978|MGI:5690848|MGI:5011624|MGI:5662820|MGI:3781037|MGI:5663645|MGI:3642756|MGI:5593303|MGI:3648302|MGI:5662654|MGI:1925396|MGI:5663284|MGI:5663655|MGI:5662616|MGI:5452590|MGI:5662917|MGI:2443043|MGI:5010132|MGI:5593492|MGI:5663676|MGI:3647344|MGI:5662906|MGI:5663397|MGI:5663681|MGI:5662930|MGI:1925823|MGI:2441710|MGI:5663018|MGI:5662746|MGI:5663548|MGI:5662734|MGI:5663130|MGI:1920587|MGI:5663356|MGI:3780383|MGI:5663528|MGI:5579857|MGI:5010136|MGI:3643020|MGI:6096195|MGI:5690714|MGI:2444172|MGI:3647327|MGI:5663105|MGI:5663133|MGI:5690854|MGI:5662581|MGI:5663156|MGI:5663760|MGI:5663697|MGI:1924718|MGI:5663963|MGI:5663029|MGI:5662941|MGI:3605803|MGI:3643978|MGI:5663841|MGI:5663209|MGI:5662859|MGI:3782710|MGI:5011015|MGI:5662834|MGI:5662775|MGI:1925434|MGI:1925432|MGI:5690753|MGI:5010122|MGI:5663872|MGI:5562782|MGI:5663567|MGI:5690743|MGI:5663814|MGI:5663621|MGI:5663124|MGI:6096165|MGI:3644940|MGI:5663764|MGI:5663070|MGI:1922593|MGI:5562784|MGI:5663706|MGI:5690732|MGI:5663864|MGI:5434364|MGI:5663231|MGI:5662979|MGI:5663249|MGI:5663563|MGI:5663425|MGI:1925898|MGI:5663960|MGI:5663701|MGI:5663601|MGI:5588970|MGI:5592506|MGI:3649091|MGI:5663857|MGI:3629628|MGI:5663044|MGI:5593245|MGI:5592209|MGI:5662627|MGI:3780458|MGI:5663711|MGI:3643731|MGI:5662655|MGI:1918288|MGI:5690770|MGI:5663129|MGI:5663811|MGI:5663569|MGI:5663114|MGI:3642648|MGI:1923034|MGI:5662615|MGI:5663467|MGI:5562727|MGI:1923204|MGI:5663863|MGI:5662575|MGI:3648644|MGI:3643011|MGI:5663131|MGI:5663492|MGI:1925995|MGI:5593298|MGI:5690692|MGI:5663080|MGI:5663128|MGI:3644057|MGI:5663699|MGI:5662576|MGI:5562787|MGI:5662765|MGI:5594025|MGI:5663396|MGI:5663155|MGI:5663071|MGI:3643077|MGI:5434111|MGI:5663862|MGI:5623233|MGI:6096129|MGI:5595710|MGI:3624349|MGI:1917958|MGI:3642065|MGI:5663420|MGI:5662583|MGI:5663332|MGI:5662677|MGI:1918440|MGI:5663658|MGI:5662578|MGI:5662832|MGI:5010636|MGI:5589460|MGI:5663383|MGI:5663783|MGI:1925148|MGI:3780646|MGI:5662579|MGI:3643001|MGI:5663817|MGI:6096151|MGI:2444463|MGI:1918432|MGI:5662908|MGI:5562772|MGI:5562771|MGI:5645797|MGI:1918897|MGI:6096137|MGI:5662959|MGI:5663669|MGI:5562729|MGI:5690757|MGI:5663315|MGI:5663240|MGI:5663887|MGI:3644601|MGI:3648967|MGI:5663472|MGI:5663481|MGI:5623040|MGI:5663559|MGI:5663541|MGI:5662745|MGI:3619322|MGI:3780791|MGI:6096171|MGI:5595707|MGI:5625046|MGI:5662976
##
##
##
##
## [if possible] Missing imputation in progress ...
## Threshold: 0.6
## Cluster size 21729 broken into 348 21381
## Done cluster 348
## Cluster size 21381 broken into 19239 2142
## Cluster size 19239 broken into 4124 15115
## Cluster size 4124 broken into 1498 2626
## Done cluster 1498
## Cluster size 2626 broken into 1189 1437
## Done cluster 1189
## Done cluster 1437
## Done cluster 2626
## Done cluster 4124
## Cluster size 15115 broken into 11519 3596
## Cluster size 11519 broken into 2755 8764
## Cluster size 2755 broken into 1540 1215
## Cluster size 1540 broken into 1066 474
## Done cluster 1066
## Done cluster 474
## Done cluster 1540
## Done cluster 1215
## Done cluster 2755
## Cluster size 8764 broken into 6405 2359
## Cluster size 6405 broken into 2196 4209
## Cluster size 2196 broken into 479 1717
## Done cluster 479
## Cluster size 1717 broken into 409 1308
## Done cluster 409
## Done cluster 1308
## Done cluster 1717
## Done cluster 2196
## Cluster size 4209 broken into 1582 2627
## Cluster size 1582 broken into 283 1299
## Done cluster 283
## Done cluster 1299
## Done cluster 1582
## Cluster size 2627 broken into 846 1781
## Done cluster 846
## Cluster size 1781 broken into 547 1234
## Done cluster 547
## Done cluster 1234
## Done cluster 1781
## Done cluster 2627
## Done cluster 4209
## Done cluster 6405
## Cluster size 2359 broken into 689 1670
## Done cluster 689
## Cluster size 1670 broken into 1186 484
## Done cluster 1186
## Done cluster 484
## Done cluster 1670
## Done cluster 2359
## Done cluster 8764
## Done cluster 11519
## Cluster size 3596 broken into 1621 1975
## Cluster size 1621 broken into 1172 449
## Done cluster 1172
## Done cluster 449
## Done cluster 1621
## Cluster size 1975 broken into 1073 902
## Done cluster 1073
## Done cluster 902
## Done cluster 1975
## Done cluster 3596
## Done cluster 15115
## Done cluster 19239
## Cluster size 2142 broken into 1607 535
## Cluster size 1607 broken into 606 1001
## Done cluster 606
## Done cluster 1001
## Done cluster 1607
## Done cluster 535
## Done cluster 2142
## Done cluster 21381
##
##
##
## Final dataset : rows = 21729 | columns = 28
##
##
##
## Variable names :
## length = 28
##
##
## list: 4-somite stage|5-somite stage|6-somite stage|7-somite stage|8-somite stage|9-somite stage|10-somite stage|11-somite stage|12-somite stage|13-somite stage|14-somite stage|15-somite stage|16-somite stage|17-somite stage|18-somite stage|19-somite stage|20-somite stage|21-somite stage|22-somite stage|23-somite stage|24-somite stage|25-somite stage|26-somite stage|27-somite stage|28-somite stage|34-somite stage|35-somite stage|36-somite stage
# plot00(DR7_viability = DR7_viability,
# data = aM,
# main = 'DMDD')
bM = tranSamp(
object = aM,
percent = percent,
FUN = function(x) {
r = apply(x, 1, function(y) {
# Scaling has no meaning here
y2 = log(log(y + 1))
y = c(0, diff(y2))
})
return(r)
},
speed = TRUE,
seed = seed
)
##
## Total observations: 21729. Selected samples: 21729 [Shuffled]
##
##########
bM$rdata = bM$rdata[, -1] # Remove the column of zero HAMED (diff consequence)
# PCA orUMAP
bM$rdata = pcaUmap0(
bM$rdata,
main = 'DMDD',
active = FALSE,
cname = 'DMDD',
ncomp = ncol(bM$rdata)
)
##########
#### #### #### #### #### #### #### #### #### #### #### #### ####
### 2.2 SHET
aS = initialization(data = shet,
speed = TRUE,
seed = seed)
##
## Response is not found and then set to NULL
##
##
## Input dataset : rows = 14160 | columns = 2
##
##
## Variable names (2):
## s_het|MGI id
## No duplicate found!
##
##
##
## Response is not included!
##
##
##
## [if possible] missing data removed!
##
##
##
## Final dataset : rows = 14160 | columns = 1
##
##
##
## Variable names :
## length = 1
##
##
## list: s_het
# plot00(DR7_viability = DR7_viability,
# data = aS,
# main = 'S-HET')
bS = tranSamp(
object = aS,
percent = percent,
speed = TRUE,
FUN = function(y) {
r = scale(y, center = min(y), scale = max(y) - min(y))
return(r)
},
seed = seed
)
##
## Total observations: 14160. Selected samples: 14160 [Shuffled]
##
hist(bS$rdata$s_het, main = 'Shet')
#### #### #### #### #### #### #### #### #### #### #### #### ####
### 2.3 Humn scores
aH = initialization(
data = overall_human_cell_lines,
response = NULL,
speed = TRUE,
seed = seed
)
##
## Response is not found and then set to NULL
##
##
## Input dataset : rows = 16641 | columns = 12
##
##
## Variable names (12):
## Wang_KBM7_CS|Wang_K562_CS|Wang_Jiyoye_CS|Wang_Raji_CS|Hart_HCT116_BF|Hart_HeLa_BF|Hart_GBM_BF|Hart_RPE1_BF|Hart_DLD1_BF|Blomen_HAP1_Qval|Blomen_KBM7_Qval|MGI id
## No duplicate found!
##
##
##
## Response is not included!
##
##
##
## Missing data more than 60 percent in rows (611=4%) [removed]: MGI:3032636|MGI:3649436|MGI:1921304|MGI:1914897|MGI:2448283|MGI:3645096|MGI:5141896|MGI:5141924|MGI:3845075|MGI:1922386|MGI:3031127|MGI:2447313|MGI:2681879|MGI:1935216|MGI:1935218|MGI:1935170|MGI:1935173|MGI:106660|MGI:3030006|MGI:3031084|MGI:3030578|MGI:2179203|MGI:2681304|MGI:3652059|MGI:3031137|MGI:1196223|MGI:5141967|MGI:5011982|MGI:3030025|MGI:95317|MGI:3045308|MGI:3612701|MGI:1919091|MGI:5510732|MGI:3030287|MGI:2150982|MGI:2681880|MGI:1298371|MGI:1298369|MGI:1935226|MGI:1935169|MGI:894324|MGI:104812|MGI:3030138|MGI:2151908|MGI:1333748|MGI:1913518|MGI:2441753|MGI:1915273|MGI:3608324|MGI:3030015|MGI:3783006|MGI:1914913|MGI:3648770|MGI:3643379|MGI:3030548|MGI:1333751|MGI:109315|MGI:3030210|MGI:3030269|MGI:1298406|MGI:97891|MGI:2448270|MGI:98821|MGI:98898|MGI:2148931|MGI:3031094|MGI:3030336|MGI:3030866|MGI:3030933|MGI:3030509|MGI:3030390|MGI:3030386|MGI:3030492|MGI:3030827|MGI:3030566|MGI:109565|MGI:2443220|MGI:2385905|MGI:1345162|MGI:88543|MGI:5516029|MGI:3030648|MGI:1923993|MGI:3712484|MGI:1924672|MGI:98484|MGI:96103|MGI:5604098|MGI:1923364|MGI:97279|MGI:3030990|MGI:1915443|MGI:1914498|MGI:98105|MGI:101766|MGI:98239|MGI:1919558|MGI:109626|MGI:98970|MGI:1095403|MGI:1352750|MGI:1333820|MGI:3030547|MGI:3031258|MGI:1891697|MGI:1917264|MGI:1925507|MGI:1923429|MGI:2678374|MGI:88501|MGI:2389572|MGI:1277959|MGI:3039592|MGI:3031090|MGI:3030624|MGI:1329027|MGI:1925719|MGI:99502|MGI:3030271|MGI:3031303|MGI:3030007|MGI:97520|MGI:1915851|MGI:103291|MGI:1195456|MGI:1347520|MGI:1347521|MGI:1929455|MGI:98085|MGI:1351628|MGI:98280|MGI:1917233|MGI:1315203|MGI:1315205|MGI:109242|MGI:1927450|MGI:2152345|MGI:2155445|MGI:95484|MGI:101835|MGI:105044|MGI:1347077|MGI:3031089|MGI:3031027|MGI:3030927|MGI:2146430|MGI:104562|MGI:2441980|MGI:1335089|MGI:1344360|MGI:3647180|MGI:1335073|MGI:1098266|MGI:1914546|MGI:2443657|MGI:2651811|MGI:2386853|MGI:88437|MGI:1916465|MGI:2442566|MGI:1919402|MGI:2685834|MGI:1918190|MGI:94865|MGI:108519|MGI:1341168|MGI:3057108|MGI:1915364|MGI:1919837|MGI:109173|MGI:1931466|MGI:3030029|MGI:3030616|MGI:101833|MGI:95752|MGI:1929713|MGI:95815|MGI:1921547|MGI:99909|MGI:96828|MGI:1101355|MGI:109452|MGI:96912|MGI:1343489|MGI:96080|MGI:102699|MGI:87853|MGI:3712328|MGI:97402|MGI:105368|MGI:2177473|MGI:1313142|MGI:1339975|MGI:1277958|MGI:98090|MGI:88121|MGI:97584|MGI:109585|MGI:97631|MGI:2387203|MGI:108295|MGI:97898|MGI:1338824|MGI:97914|MGI:1341105|MGI:1277953|MGI:1339939|MGI:1278340|MGI:1336189|MGI:98233|MGI:1932339|MGI:1891831|MGI:1097164|MGI:1859682|MGI:105090|MGI:1315202|MGI:108075|MGI:109490|MGI:105370|MGI:102690|MGI:1098658|MGI:1928487|MGI:1916333|MGI:1196624|MGI:98779|MGI:98848|MGI:1921362|MGI:1353665|MGI:106922|MGI:98933|MGI:1926479|MGI:1918846|MGI:1927657|MGI:1890646|MGI:88235|MGI:1917912|MGI:1888978|MGI:2155888|MGI:108176|MGI:1920230|MGI:1916969|MGI:1930948|MGI:2137224|MGI:3030568|MGI:1098236|MGI:3030945|MGI:107824|MGI:1354708|MGI:2385255|MGI:894320|MGI:1913903|MGI:1929093|MGI:88354|MGI:88356|MGI:1927340|MGI:2176375|MGI:109632|MGI:1203500|MGI:2441996|MGI:1194499|MGI:1918984|MGI:1915110|MGI:1923772|MGI:3057273|MGI:1336194|MGI:1349470|MGI:1927753|MGI:1915956|MGI:107265|MGI:1919006|MGI:2673307|MGI:1098622|MGI:1914008|MGI:1921462|MGI:99946|MGI:88512|MGI:1346052|MGI:107461|MGI:2685830|MGI:1920970|MGI:1353448|MGI:2682306|MGI:2139628|MGI:1914870|MGI:1925500|MGI:1919100|MGI:3576487|MGI:94859|MGI:94866|MGI:1346328|MGI:104773|MGI:1924555|MGI:94896|MGI:1922863|MGI:1354949|MGI:107231|MGI:1344351|MGI:94910|MGI:2442366|MGI:1201387|MGI:1888902|MGI:1914688|MGI:103221|MGI:1329037|MGI:95301|MGI:101762|MGI:87963|MGI:1916789|MGI:109637|MGI:108050|MGI:1097695|MGI:99481|MGI:88381|MGI:109609|MGI:101790|MGI:95482|MGI:109622|MGI:2670976|MGI:95516|MGI:1298387|MGI:87991|MGI:108476|MGI:1349449|MGI:95678|MGI:107504|MGI:1347344|MGI:98483|MGI:95791|MGI:1096320|MGI:1922762|MGI:1261831|MGI:1918041|MGI:96067|MGI:96062|MGI:96108|MGI:2146636|MGI:104853|MGI:1345142|MGI:1916510|MGI:88054|MGI:1343166|MGI:99916|MGI:96789|MGI:2442252|MGI:97175|MGI:97043|MGI:96969|MGI:104532|MGI:88075|MGI:97052|MGI:105941|MGI:1858257|MGI:88321|MGI:101786|MGI:1100535|MGI:104563|MGI:109201|MGI:97298|MGI:109349|MGI:97355|MGI:97360|MGI:97362|MGI:97372|MGI:97373|MGI:1098765|MGI:1332235|MGI:1100492|MGI:1921393|MGI:1328337|MGI:1339656|MGI:97475|MGI:1341793|MGI:1926119|MGI:108028|MGI:1098283|MGI:97552|MGI:1099818|MGI:2155399|MGI:1858200|MGI:109182|MGI:97783|MGI:1097152|MGI:97845|MGI:2180784|MGI:97912|MGI:1924705|MGI:1915525|MGI:105922|MGI:98084|MGI:98227|MGI:102809|MGI:98254|MGI:98310|MGI:104295|MGI:2443098|MGI:1341903|MGI:1919649|MGI:103241|MGI:1933174|MGI:98344|MGI:98384|MGI:1202875|MGI:98389|MGI:1922646|MGI:103078|MGI:102760|MGI:98510|MGI:98506|MGI:109573|MGI:105992|MGI:109587|MGI:98813|MGI:87881|MGI:894652|MGI:104710|MGI:1922708|MGI:1917076|MGI:88233|MGI:2386681|MGI:1336193|MGI:103032|MGI:1351318|MGI:2151886|MGI:2664387|MGI:1931787|MGI:2182269|MGI:1913480|MGI:1927138|MGI:1934943|MGI:3030569|MGI:1919680|MGI:2183559|MGI:1931457|MGI:1341902|MGI:2389142|MGI:1915265|MGI:3036258|MGI:1349450|MGI:1339969|MGI:1917851|MGI:3045348|MGI:2388804|MGI:1860835|MGI:1927185|MGI:1346069|MGI:2442792|MGI:1921846|MGI:1097716|MGI:2384914|MGI:1201414|MGI:2686922|MGI:1921303|MGI:1342293|MGI:1861901|MGI:2182965|MGI:2384409|MGI:2445289|MGI:1921385|MGI:1289172|MGI:1915092|MGI:1914502|MGI:1913787|MGI:2442836|MGI:1859284|MGI:1270128|MGI:1922664|MGI:107812|MGI:1921677|MGI:106038|MGI:1915690|MGI:1926004|MGI:1916987|MGI:104615|MGI:104650|MGI:2141165|MGI:1344385|MGI:88479|MGI:2146616|MGI:2135874|MGI:891996|MGI:1934028|MGI:99675|MGI:1202878|MGI:2443882|MGI:1926193|MGI:2385269|MGI:2151071|MGI:88541|MGI:1261768|MGI:2148149|MGI:1339968|MGI:1917030|MGI:1925764|MGI:1929538|MGI:1921276|MGI:3603816|MGI:1914209|MGI:95612|MGI:1888986|MGI:105393|MGI:1925139|MGI:1919782|MGI:893587|MGI:1914858|MGI:1921701|MGI:1914736|MGI:1913411|MGI:1913305|MGI:1914974|MGI:2656976|MGI:2144271|MGI:1343297|MGI:1917535|MGI:1194993|MGI:1925808|MGI:95321|MGI:105120|MGI:2152297|MGI:106572|MGI:1914273|MGI:1891435|MGI:107822|MGI:1098684|MGI:107180|MGI:95407|MGI:2443139|MGI:95491|MGI:2137612|MGI:1350921|MGI:95554|MGI:1916776|MGI:108088|MGI:95611|MGI:1270854|MGI:1096345|MGI:99844|MGI:1202394|MGI:95718|MGI:95797|MGI:2441758|MGI:95819|MGI:95833|MGI:95852|MGI:106590|MGI:2680765|MGI:96079|MGI:1315197|MGI:2670962|MGI:1336200|MGI:107619|MGI:108482|MGI:1330808|MGI:96397|MGI:102851|MGI:1346336|MGI:1891066|MGI:88064|MGI:88067|MGI:894762|MGI:895068|MGI:101789|MGI:88070|MGI:1929940|MGI:2444672|MGI:892970|MGI:1306776|MGI:99436|MGI:96925|MGI:105380|MGI:2147351|MGI:96973|MGI:2384966|MGI:96982|MGI:96990|MGI:1353466|MGI:95495|MGI:106926|MGI:106612|MGI:1351625|MGI:1298366|MGI:1915289|MGI:97361|MGI:109185|MGI:1344424|MGI:104749|MGI:1914328|MGI:99462|MGI:1891457|MGI:1349767|MGI:97744|MGI:1100846|MGI:2685870|MGI:104968|MGI:894680|MGI:1270863|MGI:103294|MGI:1206586|MGI:1347006|MGI:97821|MGI:99425|MGI:1316678|MGI:1913929|MGI:97847|MGI:97876|MGI:97877|MGI:97879|MGI:1929473|MGI:2180585|MGI:98270|MGI:1203522
##
##
##
##
## [if possible] Missing imputation in progress ...
## Threshold: 0.6
## Cluster size 16030 broken into 14692 1338
## Cluster size 14692 broken into 7860 6832
## Cluster size 7860 broken into 4569 3291
## Cluster size 4569 broken into 1546 3023
## Cluster size 1546 broken into 713 833
## Done cluster 713
## Done cluster 833
## Done cluster 1546
## Cluster size 3023 broken into 1709 1314
## Cluster size 1709 broken into 896 813
## Done cluster 896
## Done cluster 813
## Done cluster 1709
## Done cluster 1314
## Done cluster 3023
## Done cluster 4569
## Cluster size 3291 broken into 2163 1128
## Cluster size 2163 broken into 1262 901
## Done cluster 1262
## Done cluster 901
## Done cluster 2163
## Done cluster 1128
## Done cluster 3291
## Done cluster 7860
## Cluster size 6832 broken into 4224 2608
## Cluster size 4224 broken into 2462 1762
## Cluster size 2462 broken into 1084 1378
## Done cluster 1084
## Done cluster 1378
## Done cluster 2462
## Cluster size 1762 broken into 962 800
## Done cluster 962
## Done cluster 800
## Done cluster 1762
## Done cluster 4224
## Cluster size 2608 broken into 1704 904
## Cluster size 1704 broken into 1434 270
## Done cluster 1434
## Done cluster 270
## Done cluster 1704
## Done cluster 904
## Done cluster 2608
## Done cluster 6832
## Done cluster 14692
## Done cluster 1338
##
##
##
## Final dataset : rows = 16030 | columns = 11
##
##
##
## Variable names :
## length = 11
##
##
## list: Wang_KBM7_CS|Wang_K562_CS|Wang_Jiyoye_CS|Wang_Raji_CS|Hart_HCT116_BF|Hart_HeLa_BF|Hart_GBM_BF|Hart_RPE1_BF|Hart_DLD1_BF|Blomen_HAP1_Qval|Blomen_KBM7_Qval
# plot00(DR7_viability = DR7_viability,
# data = aH,
# main = 'Human scores')
bH = tranSamp(
object = aH,
percent = percent,
FUN = function(x) {
r = apply(x, 2, function(y) {
scale(y, center = min(y), scale = max(y) - min(y))
})
return(r)
},
speed = TRUE,
seed = seed
)
##
## Total observations: 16030. Selected samples: 16030 [Shuffled]
##
# Umap or PCA
bH$rdata = pcaUmap0(
bH$rdata,
main = 'Hscores',
cname = 'HScore',
umap = FALSE,
ncomp = 6
)
## pca in progress ...
#### #### #### #### #### #### #### #### #### #### #### #### ####
### 436 human cell lines
aH436 = initialization(
data = human_cells_436,
response = NULL,
speed = TRUE,
seed = seed
)
##
## Response is not found and then set to NULL
##
##
## Input dataset : rows = 15753 | columns = 437
##
##
## Variable names (437):
## MGI id|127399_SOFT_TISSUE|143B_BONE|253J_URINARY_TRACT|42MGBA_CENTRAL_NERVOUS_SYSTEM|5637_URINARY_TRACT|59M_OVARY|639V_URINARY_TRACT|647V_URINARY_TRACT|697_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|769P_KIDNEY|786O_KIDNEY|8305C_THYROID|8MGBA_CENTRAL_NERVOUS_SYSTEM|A2058_SKIN|A2780_OVARY|A3KAW_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|A549_LUNG|ABC1_LUNG|AGS_STOMACH|ASPC1_PANCREAS|AU565_BREAST|BC3C_URINARY_TRACT|BFTC905_URINARY_TRACT|BFTC909_KIDNEY|BHY_UPPER_AERODIGESTIVE_TRACT|BICR16_UPPER_AERODIGESTIVE_TRACT|BICR22_UPPER_AERODIGESTIVE_TRACT|BICR31_UPPER_AERODIGESTIVE_TRACT|BICR56_UPPER_AERODIGESTIVE_TRACT|BICR6_UPPER_AERODIGESTIVE_TRACT|BIN67_OVARY|BT549_BREAST|C2BBE1_LARGE_INTESTINE|C32_SKIN|CAKI1_KIDNEY|CAKI2_KIDNEY|CAL27_UPPER_AERODIGESTIVE_TRACT|CAL29_URINARY_TRACT|CAL33_UPPER_AERODIGESTIVE_TRACT|CAL51_BREAST|CAL78_BONE|CALU6_LUNG|CAMA1_BREAST|CAOV3_OVARY|CAS1_CENTRAL_NERVOUS_SYSTEM|CCFSTTG1_CENTRAL_NERVOUS_SYSTEM|CCK81_LARGE_INTESTINE|CFPAC1_PANCREAS|CH157MN_CENTRAL_NERVOUS_SYSTEM|CHAGOK1_LUNG|CHLA15_AUTONOMIC_GANGLIA|CHLA266_SOFT_TISSUE|CHP212_AUTONOMIC_GANGLIA|CJM_SKIN|CL40_LARGE_INTESTINE|CME1_SOFT_TISSUE|COGAR359_SOFT_TISSUE|COGE352_BONE|COGN278_AUTONOMIC_GANGLIA|COGN305_AUTONOMIC_GANGLIA|COLO201_LARGE_INTESTINE|COLO678_LARGE_INTESTINE|COLO679_SKIN|COLO792_SKIN|COLO800_SKIN|CORL279_LUNG|CORL47_LUNG|COV318_OVARY|COV362_OVARY|COV434_OVARY|COV504_OVARY|COV644_OVARY|CW9019_SOFT_TISSUE|D283MED_CENTRAL_NERVOUS_SYSTEM|D341MED_CENTRAL_NERVOUS_SYSTEM|D425_CENTRAL_NERVOUS_SYSTEM|D458_CENTRAL_NERVOUS_SYSTEM|DANG_PANCREAS|DAOY_CENTRAL_NERVOUS_SYSTEM|DB_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|DETROIT562_UPPER_AERODIGESTIVE_TRACT|DKMG_CENTRAL_NERVOUS_SYSTEM|DLD1_LARGE_INTESTINE|DU4475_BREAST|EFE184_ENDOMETRIUM|EFM19_BREAST|EFO21_OVARY|EFO27_OVARY|EJM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|EKVX_LUNG|EN_ENDOMETRIUM|EPLC272H_LUNG|ES2_OVARY|ESS1_ENDOMETRIUM|F5_CENTRAL_NERVOUS_SYSTEM|FADU_UPPER_AERODIGESTIVE_TRACT|FU97_STOMACH|G292CLONEA141B1_BONE|GAMG_CENTRAL_NERVOUS_SYSTEM|GB1_CENTRAL_NERVOUS_SYSTEM|GCIY_STOMACH|GCT_SOFT_TISSUE|GI1_CENTRAL_NERVOUS_SYSTEM|GSS_STOMACH|GSU_STOMACH|H4_CENTRAL_NERVOUS_SYSTEM|HARA_LUNG|HCC1143_BREAST|HCC1359_LUNG|HCC1395_BREAST|HCC1419_BREAST|HCC1428_BREAST|HCC15_LUNG|HCC1806_BREAST|HCC1937_BREAST|HCC1954_BREAST|HCC202_BREAST|HCC56_LARGE_INTESTINE|HCC827_LUNG|HCC95_LUNG|HEC151_ENDOMETRIUM|HEC1A_ENDOMETRIUM|HEC1B_ENDOMETRIUM|HEC251_ENDOMETRIUM|HEC50B_ENDOMETRIUM|HEC59_ENDOMETRIUM|HEC6_ENDOMETRIUM|HEYA8_OVARY|HGC27_STOMACH|HLF_LIVER|HMC18_BREAST|HOP62_LUNG|HS578T_BREAST|HS683_CENTRAL_NERVOUS_SYSTEM|HS695T_SKIN|HS729_SOFT_TISSUE|HS766T_PANCREAS|HS944T_SKIN|HSC2_UPPER_AERODIGESTIVE_TRACT|HSC3_UPPER_AERODIGESTIVE_TRACT|HT1080_SOFT_TISSUE|HT115_LARGE_INTESTINE|HT1197_URINARY_TRACT|HT1376_URINARY_TRACT|HT144_SKIN|HT29_LARGE_INTESTINE|HT55_LARGE_INTESTINE|HUH1_LIVER|HUH6_LIVER|HUH7_LIVER|HUPT3_PANCREAS|IGR1_SKIN|IGR39_SKIN|IMR32_AUTONOMIC_GANGLIA|INA6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|IPC298_SKIN|ISTMES1_PLEURA|ISTMES2_PLEURA|JHH1_LIVER|JHH4_LIVER|JHH5_LIVER|JHH7_LIVER|JHOC5_OVARY|JHOM1_OVARY|JHOS2_OVARY|JHOS4_OVARY|JHUEM1_ENDOMETRIUM|JIMT1_BREAST|JJN3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|JMSU1_URINARY_TRACT|JR_SOFT_TISSUE|K029AX_SKIN|KALS1_CENTRAL_NERVOUS_SYSTEM|KARPAS299_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|KARPAS422_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|KE39_STOMACH|KELLY_AUTONOMIC_GANGLIA|KIJK_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|KLE_ENDOMETRIUM|KM12_LARGE_INTESTINE|KMBC2_URINARY_TRACT|KMRC1_KIDNEY|KMRC20_KIDNEY|KMS11_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|KMS20_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|KMS26_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|KMS27_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|KMS34_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|KNS42_CENTRAL_NERVOUS_SYSTEM|KNS60_CENTRAL_NERVOUS_SYSTEM|KNS62_LUNG|KNS81_CENTRAL_NERVOUS_SYSTEM|KP2_PANCREAS|KP3_PANCREAS|KP4_PANCREAS|KPL1_BREAST|KPNYN_AUTONOMIC_GANGLIA|KS1_CENTRAL_NERVOUS_SYSTEM|KU1919_URINARY_TRACT|KURAMOCHI_OVARY|KYSE180_OESOPHAGUS|KYSE270_OESOPHAGUS|KYSE30_OESOPHAGUS|KYSE410_OESOPHAGUS|KYSE450_OESOPHAGUS|KYSE510_OESOPHAGUS|KYSE70_OESOPHAGUS|L82_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|LCLC103H_LUNG|LI7_LIVER|LK2_LUNG|LMSU_STOMACH|LN18_CENTRAL_NERVOUS_SYSTEM|LN235_CENTRAL_NERVOUS_SYSTEM|LN319_CENTRAL_NERVOUS_SYSTEM|LN382_CENTRAL_NERVOUS_SYSTEM|LN443_CENTRAL_NERVOUS_SYSTEM|LNZ308_CENTRAL_NERVOUS_SYSTEM|LOVO_LARGE_INTESTINE|LP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|LS1034_LARGE_INTESTINE|LS180_LARGE_INTESTINE|LS513_LARGE_INTESTINE|LU99_LUNG|LUDLU1_LUNG|LXF289_LUNG|M059K_CENTRAL_NERVOUS_SYSTEM|MAC2A_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|MALME3M_SKIN|MCAS_OVARY|MDAMB157_BREAST|MDAMB231_BREAST|MDAMB415_BREAST|MDAMB435S_SKIN|MDAMB436_BREAST|MDAMB453_BREAST|MDAMB468_BREAST|MDST8_LARGE_INTESTINE|MELHO_SKIN|MELJUSO_SKIN|MFE319_ENDOMETRIUM|MHHNB11_AUTONOMIC_GANGLIA|MIAPACA2_PANCREAS|MKN45_STOMACH|MM1S_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|MOLM13_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|MORCPR_LUNG|MPP89_PLEURA|MSTO211H_PLEURA|MV411_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|MYLA_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|NALM6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|NB1_AUTONOMIC_GANGLIA|NB4_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|NCIH1299_LUNG|NCIH1437_LUNG|NCIH1568_LUNG|NCIH1573_LUNG|NCIH1581_LUNG|NCIH1648_LUNG|NCIH1650_LUNG|NCIH1666_LUNG|NCIH1693_LUNG|NCIH1703_LUNG|NCIH1792_LUNG|NCIH1944_LUNG|NCIH2023_LUNG|NCIH2030_LUNG|NCIH2052_PLEURA|NCIH2087_LUNG|NCIH2110_LUNG|NCIH2122_LUNG|NCIH2126_LUNG|NCIH2170_LUNG|NCIH2172_LUNG|NCIH2291_LUNG|NCIH23_LUNG|NCIH2452_PLEURA|NCIH322_LUNG|NCIH441_LUNG|NCIH460_LUNG|NCIH520_LUNG|NCIH716_LARGE_INTESTINE|NCIH747_LARGE_INTESTINE|NCIH838_LUNG|NCIN87_STOMACH|NH6_AUTONOMIC_GANGLIA|NOMO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|NUGC3_STOMACH|OAW28_OVARY|OCIAML2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|OCIAML3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|OE21_OESOPHAGUS|OE33_OESOPHAGUS|ONCODG1_OVARY|ONS76_CENTRAL_NERVOUS_SYSTEM|OSRC2_KIDNEY|OUMS23_LARGE_INTESTINE|OV7_OVARY|OV90_OVARY|OVCAR5_OVARY|OVCAR8_OVARY|OVISE_OVARY|OVK18_OVARY|OVMANA_OVARY|OVTOKO_OVARY|P31FUJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|PANC0203_PANCREAS|PANC0403_PANCREAS|PANC1005_PANCREAS|PATU8988S_PANCREAS|PC14_LUNG|PECAPJ34CLONEC12_UPPER_AERODIGESTIVE_TRACT|PECAPJ41CLONED2_UPPER_AERODIGESTIVE_TRACT|PEDS005TPFAD_KIDNEY|PEDS015T_SOFT_TISSUE|PF382_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|PK1_PANCREAS|PK45H_PANCREAS|PLCPRF5_LIVER|PSN1_PANCREAS|RCC10RGB_KIDNEY|RD_SOFT_TISSUE|REH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|RERFLCAD1_LUNG|RERFLCAI_LUNG|RH30_SOFT_TISSUE|RKN_SOFT_TISSUE|RKO_LARGE_INTESTINE|RMUGS_OVARY|RPMI7951_SKIN|RPMI8226_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|RT11284_URINARY_TRACT|RT112_URINARY_TRACT|RT4_URINARY_TRACT|RVH421_SKIN|S117_SOFT_TISSUE|SAOS2_BONE|SCABER_URINARY_TRACT|SCS214_SOFT_TISSUE|SEM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|SF172_CENTRAL_NERVOUS_SYSTEM|SF295_CENTRAL_NERVOUS_SYSTEM|SF767_CENTRAL_NERVOUS_SYSTEM|SH10TC_STOMACH|SIHA_CERVIX|SIMA_AUTONOMIC_GANGLIA|SJSA1_BONE|SKBR3_BREAST|SKHEP1_LIVER|SKMEL24_SKIN|SKMEL30_SKIN|SKMES1_LUNG|SKNAS_AUTONOMIC_GANGLIA|SKNBE2_AUTONOMIC_GANGLIA|SKNDZ_AUTONOMIC_GANGLIA|SKNFI_AUTONOMIC_GANGLIA|SKNMC_BONE|SKOV3_OVARY|SLR20_KIDNEY|SLR23_KIDNEY|SLR26_KIDNEY|SMSCTR_SOFT_TISSUE|SMZ1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|SNGM_ENDOMETRIUM|SNU1105_CENTRAL_NERVOUS_SYSTEM|SNU182_LIVER|SNU1_STOMACH|SNU201_CENTRAL_NERVOUS_SYSTEM|SNU213_PANCREAS|SNU349_KIDNEY|SNU398_LIVER|SNU410_PANCREAS|SNU449_LIVER|SNU503_LARGE_INTESTINE|SNU685_ENDOMETRIUM|SNU840_OVARY|SNU8_OVARY|SR786_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|SUIT2_PANCREAS|SUPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|SUPT1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|SW1463_LARGE_INTESTINE|SW403_LARGE_INTESTINE|SW48_LARGE_INTESTINE|SW620_LARGE_INTESTINE|SW837_LARGE_INTESTINE|SW982_SOFT_TISSUE|SYO1_SOFT_TISSUE|T24_URINARY_TRACT|T3M4_PANCREAS|T84_LARGE_INTESTINE|T98G_CENTRAL_NERVOUS_SYSTEM|TC106_BONE|TC138_BONE|TC205_BONE|TCCPAN2_PANCREAS|TCCSUP_URINARY_TRACT|TE1_OESOPHAGUS|TE4_OESOPHAGUS|TE5_OESOPHAGUS|TE6_OESOPHAGUS|TE8_OESOPHAGUS|TEN_ENDOMETRIUM|TF1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|THP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|TOV21G_OVARY|TUHR10TKB_KIDNEY|TUHR14TKB_KIDNEY|TUHR4TKB_KIDNEY|U118MG_CENTRAL_NERVOUS_SYSTEM|U178_CENTRAL_NERVOUS_SYSTEM|U251MG_CENTRAL_NERVOUS_SYSTEM|U2OS_BONE|U343_CENTRAL_NERVOUS_SYSTEM|U87MG_CENTRAL_NERVOUS_SYSTEM|U937_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|UACC257_SKIN|UACC62_SKIN|UMUC1_URINARY_TRACT|UMUC3_URINARY_TRACT|UOK101_KIDNEY|UPCISCC152_UPPER_AERODIGESTIVE_TRACT|UW228_CENTRAL_NERVOUS_SYSTEM|VMCUB1_URINARY_TRACT|VMRCRCW_KIDNEY|WM115_SKIN|WM1799_SKIN|WM2664_SKIN|WM793_SKIN|WM983B_SKIN|YAPC_PANCREAS|YD38_UPPER_AERODIGESTIVE_TRACT|YH13_CENTRAL_NERVOUS_SYSTEM|YKG1_CENTRAL_NERVOUS_SYSTEM|ZR751_BREAST
## No duplicate found!
##
##
##
## Response is not included!
## Cluster size 15753 broken into 14049 1704
## Cluster size 14049 broken into 8375 5674
## Cluster size 8375 broken into 2442 5933
## Cluster size 2442 broken into 1770 672
## Cluster size 1770 broken into 898 872
## Done cluster 898
## Done cluster 872
## Done cluster 1770
## Done cluster 672
## Done cluster 2442
## Cluster size 5933 broken into 3103 2830
## Cluster size 3103 broken into 1729 1374
## Cluster size 1729 broken into 875 854
## Done cluster 875
## Done cluster 854
## Done cluster 1729
## Done cluster 1374
## Done cluster 3103
## Cluster size 2830 broken into 1367 1463
## Done cluster 1367
## Done cluster 1463
## Done cluster 2830
## Done cluster 5933
## Done cluster 8375
## Cluster size 5674 broken into 1666 4008
## Cluster size 1666 broken into 638 1028
## Done cluster 638
## Done cluster 1028
## Done cluster 1666
## Cluster size 4008 broken into 1717 2291
## Cluster size 1717 broken into 794 923
## Done cluster 794
## Done cluster 923
## Done cluster 1717
## Cluster size 2291 broken into 1498 793
## Done cluster 1498
## Done cluster 793
## Done cluster 2291
## Done cluster 4008
## Done cluster 5674
## Done cluster 14049
## Cluster size 1704 broken into 620 1084
## Done cluster 620
## Done cluster 1084
## Done cluster 1704
##
##
##
## Final dataset : rows = 15753 | columns = 436
##
##
##
## Variable names :
## length = 436
##
##
## list: 127399_SOFT_TISSUE|143B_BONE|253J_URINARY_TRACT|42MGBA_CENTRAL_NERVOUS_SYSTEM|5637_URINARY_TRACT|59M_OVARY|639V_URINARY_TRACT|647V_URINARY_TRACT|697_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|769P_KIDNEY|786O_KIDNEY|8305C_THYROID|8MGBA_CENTRAL_NERVOUS_SYSTEM|A2058_SKIN|A2780_OVARY|A3KAW_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|A549_LUNG|ABC1_LUNG|AGS_STOMACH|ASPC1_PANCREAS|AU565_BREAST|BC3C_URINARY_TRACT|BFTC905_URINARY_TRACT|BFTC909_KIDNEY|BHY_UPPER_AERODIGESTIVE_TRACT|BICR16_UPPER_AERODIGESTIVE_TRACT|BICR22_UPPER_AERODIGESTIVE_TRACT|BICR31_UPPER_AERODIGESTIVE_TRACT|BICR56_UPPER_AERODIGESTIVE_TRACT|BICR6_UPPER_AERODIGESTIVE_TRACT|BIN67_OVARY|BT549_BREAST|C2BBE1_LARGE_INTESTINE|C32_SKIN|CAKI1_KIDNEY|CAKI2_KIDNEY|CAL27_UPPER_AERODIGESTIVE_TRACT|CAL29_URINARY_TRACT|CAL33_UPPER_AERODIGESTIVE_TRACT|CAL51_BREAST|CAL78_BONE|CALU6_LUNG|CAMA1_BREAST|CAOV3_OVARY|CAS1_CENTRAL_NERVOUS_SYSTEM|CCFSTTG1_CENTRAL_NERVOUS_SYSTEM|CCK81_LARGE_INTESTINE|CFPAC1_PANCREAS|CH157MN_CENTRAL_NERVOUS_SYSTEM|CHAGOK1_LUNG|CHLA15_AUTONOMIC_GANGLIA|CHLA266_SOFT_TISSUE|CHP212_AUTONOMIC_GANGLIA|CJM_SKIN|CL40_LARGE_INTESTINE|CME1_SOFT_TISSUE|COGAR359_SOFT_TISSUE|COGE352_BONE|COGN278_AUTONOMIC_GANGLIA|COGN305_AUTONOMIC_GANGLIA|COLO201_LARGE_INTESTINE|COLO678_LARGE_INTESTINE|COLO679_SKIN|COLO792_SKIN|COLO800_SKIN|CORL279_LUNG|CORL47_LUNG|COV318_OVARY|COV362_OVARY|COV434_OVARY|COV504_OVARY|COV644_OVARY|CW9019_SOFT_TISSUE|D283MED_CENTRAL_NERVOUS_SYSTEM|D341MED_CENTRAL_NERVOUS_SYSTEM|D425_CENTRAL_NERVOUS_SYSTEM|D458_CENTRAL_NERVOUS_SYSTEM|DANG_PANCREAS|DAOY_CENTRAL_NERVOUS_SYSTEM|DB_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|DETROIT562_UPPER_AERODIGESTIVE_TRACT|DKMG_CENTRAL_NERVOUS_SYSTEM|DLD1_LARGE_INTESTINE|DU4475_BREAST|EFE184_ENDOMETRIUM|EFM19_BREAST|EFO21_OVARY|EFO27_OVARY|EJM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|EKVX_LUNG|EN_ENDOMETRIUM|EPLC272H_LUNG|ES2_OVARY|ESS1_ENDOMETRIUM|F5_CENTRAL_NERVOUS_SYSTEM|FADU_UPPER_AERODIGESTIVE_TRACT|FU97_STOMACH|G292CLONEA141B1_BONE|GAMG_CENTRAL_NERVOUS_SYSTEM|GB1_CENTRAL_NERVOUS_SYSTEM|GCIY_STOMACH|GCT_SOFT_TISSUE|GI1_CENTRAL_NERVOUS_SYSTEM|GSS_STOMACH|GSU_STOMACH|H4_CENTRAL_NERVOUS_SYSTEM|HARA_LUNG|HCC1143_BREAST|HCC1359_LUNG|HCC1395_BREAST|HCC1419_BREAST|HCC1428_BREAST|HCC15_LUNG|HCC1806_BREAST|HCC1937_BREAST|HCC1954_BREAST|HCC202_BREAST|HCC56_LARGE_INTESTINE|HCC827_LUNG|HCC95_LUNG|HEC151_ENDOMETRIUM|HEC1A_ENDOMETRIUM|HEC1B_ENDOMETRIUM|HEC251_ENDOMETRIUM|HEC50B_ENDOMETRIUM|HEC59_ENDOMETRIUM|HEC6_ENDOMETRIUM|HEYA8_OVARY|HGC27_STOMACH|HLF_LIVER|HMC18_BREAST|HOP62_LUNG|HS578T_BREAST|HS683_CENTRAL_NERVOUS_SYSTEM|HS695T_SKIN|HS729_SOFT_TISSUE|HS766T_PANCREAS|HS944T_SKIN|HSC2_UPPER_AERODIGESTIVE_TRACT|HSC3_UPPER_AERODIGESTIVE_TRACT|HT1080_SOFT_TISSUE|HT115_LARGE_INTESTINE|HT1197_URINARY_TRACT|HT1376_URINARY_TRACT|HT144_SKIN|HT29_LARGE_INTESTINE|HT55_LARGE_INTESTINE|HUH1_LIVER|HUH6_LIVER|HUH7_LIVER|HUPT3_PANCREAS|IGR1_SKIN|IGR39_SKIN|IMR32_AUTONOMIC_GANGLIA|INA6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|IPC298_SKIN|ISTMES1_PLEURA|ISTMES2_PLEURA|JHH1_LIVER|JHH4_LIVER|JHH5_LIVER|JHH7_LIVER|JHOC5_OVARY|JHOM1_OVARY|JHOS2_OVARY|JHOS4_OVARY|JHUEM1_ENDOMETRIUM|JIMT1_BREAST|JJN3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|JMSU1_URINARY_TRACT|JR_SOFT_TISSUE|K029AX_SKIN|KALS1_CENTRAL_NERVOUS_SYSTEM|KARPAS299_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|KARPAS422_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|KE39_STOMACH|KELLY_AUTONOMIC_GANGLIA|KIJK_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|KLE_ENDOMETRIUM|KM12_LARGE_INTESTINE|KMBC2_URINARY_TRACT|KMRC1_KIDNEY|KMRC20_KIDNEY|KMS11_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|KMS20_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|KMS26_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|KMS27_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|KMS34_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|KNS42_CENTRAL_NERVOUS_SYSTEM|KNS60_CENTRAL_NERVOUS_SYSTEM|KNS62_LUNG|KNS81_CENTRAL_NERVOUS_SYSTEM|KP2_PANCREAS|KP3_PANCREAS|KP4_PANCREAS|KPL1_BREAST|KPNYN_AUTONOMIC_GANGLIA|KS1_CENTRAL_NERVOUS_SYSTEM|KU1919_URINARY_TRACT|KURAMOCHI_OVARY|KYSE180_OESOPHAGUS|KYSE270_OESOPHAGUS|KYSE30_OESOPHAGUS|KYSE410_OESOPHAGUS|KYSE450_OESOPHAGUS|KYSE510_OESOPHAGUS|KYSE70_OESOPHAGUS|L82_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|LCLC103H_LUNG|LI7_LIVER|LK2_LUNG|LMSU_STOMACH|LN18_CENTRAL_NERVOUS_SYSTEM|LN235_CENTRAL_NERVOUS_SYSTEM|LN319_CENTRAL_NERVOUS_SYSTEM|LN382_CENTRAL_NERVOUS_SYSTEM|LN443_CENTRAL_NERVOUS_SYSTEM|LNZ308_CENTRAL_NERVOUS_SYSTEM|LOVO_LARGE_INTESTINE|LP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|LS1034_LARGE_INTESTINE|LS180_LARGE_INTESTINE|LS513_LARGE_INTESTINE|LU99_LUNG|LUDLU1_LUNG|LXF289_LUNG|M059K_CENTRAL_NERVOUS_SYSTEM|MAC2A_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|MALME3M_SKIN|MCAS_OVARY|MDAMB157_BREAST|MDAMB231_BREAST|MDAMB415_BREAST|MDAMB435S_SKIN|MDAMB436_BREAST|MDAMB453_BREAST|MDAMB468_BREAST|MDST8_LARGE_INTESTINE|MELHO_SKIN|MELJUSO_SKIN|MFE319_ENDOMETRIUM|MHHNB11_AUTONOMIC_GANGLIA|MIAPACA2_PANCREAS|MKN45_STOMACH|MM1S_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|MOLM13_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|MORCPR_LUNG|MPP89_PLEURA|MSTO211H_PLEURA|MV411_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|MYLA_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|NALM6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|NB1_AUTONOMIC_GANGLIA|NB4_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|NCIH1299_LUNG|NCIH1437_LUNG|NCIH1568_LUNG|NCIH1573_LUNG|NCIH1581_LUNG|NCIH1648_LUNG|NCIH1650_LUNG|NCIH1666_LUNG|NCIH1693_LUNG|NCIH1703_LUNG|NCIH1792_LUNG|NCIH1944_LUNG|NCIH2023_LUNG|NCIH2030_LUNG|NCIH2052_PLEURA|NCIH2087_LUNG|NCIH2110_LUNG|NCIH2122_LUNG|NCIH2126_LUNG|NCIH2170_LUNG|NCIH2172_LUNG|NCIH2291_LUNG|NCIH23_LUNG|NCIH2452_PLEURA|NCIH322_LUNG|NCIH441_LUNG|NCIH460_LUNG|NCIH520_LUNG|NCIH716_LARGE_INTESTINE|NCIH747_LARGE_INTESTINE|NCIH838_LUNG|NCIN87_STOMACH|NH6_AUTONOMIC_GANGLIA|NOMO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|NUGC3_STOMACH|OAW28_OVARY|OCIAML2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|OCIAML3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|OE21_OESOPHAGUS|OE33_OESOPHAGUS|ONCODG1_OVARY|ONS76_CENTRAL_NERVOUS_SYSTEM|OSRC2_KIDNEY|OUMS23_LARGE_INTESTINE|OV7_OVARY|OV90_OVARY|OVCAR5_OVARY|OVCAR8_OVARY|OVISE_OVARY|OVK18_OVARY|OVMANA_OVARY|OVTOKO_OVARY|P31FUJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|PANC0203_PANCREAS|PANC0403_PANCREAS|PANC1005_PANCREAS|PATU8988S_PANCREAS|PC14_LUNG|PECAPJ34CLONEC12_UPPER_AERODIGESTIVE_TRACT|PECAPJ41CLONED2_UPPER_AERODIGESTIVE_TRACT|PEDS005TPFAD_KIDNEY|PEDS015T_SOFT_TISSUE|PF382_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|PK1_PANCREAS|PK45H_PANCREAS|PLCPRF5_LIVER|PSN1_PANCREAS|RCC10RGB_KIDNEY|RD_SOFT_TISSUE|REH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|RERFLCAD1_LUNG|RERFLCAI_LUNG|RH30_SOFT_TISSUE|RKN_SOFT_TISSUE|RKO_LARGE_INTESTINE|RMUGS_OVARY|RPMI7951_SKIN|RPMI8226_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|RT11284_URINARY_TRACT|RT112_URINARY_TRACT|RT4_URINARY_TRACT|RVH421_SKIN|S117_SOFT_TISSUE|SAOS2_BONE|SCABER_URINARY_TRACT|SCS214_SOFT_TISSUE|SEM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|SF172_CENTRAL_NERVOUS_SYSTEM|SF295_CENTRAL_NERVOUS_SYSTEM|SF767_CENTRAL_NERVOUS_SYSTEM|SH10TC_STOMACH|SIHA_CERVIX|SIMA_AUTONOMIC_GANGLIA|SJSA1_BONE|SKBR3_BREAST|SKHEP1_LIVER|SKMEL24_SKIN|SKMEL30_SKIN|SKMES1_LUNG|SKNAS_AUTONOMIC_GANGLIA|SKNBE2_AUTONOMIC_GANGLIA|SKNDZ_AUTONOMIC_GANGLIA|SKNFI_AUTONOMIC_GANGLIA|SKNMC_BONE|SKOV3_OVARY|SLR20_KIDNEY|SLR23_KIDNEY|SLR26_KIDNEY|SMSCTR_SOFT_TISSUE|SMZ1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|SNGM_ENDOMETRIUM|SNU1105_CENTRAL_NERVOUS_SYSTEM|SNU182_LIVER|SNU1_STOMACH|SNU201_CENTRAL_NERVOUS_SYSTEM|SNU213_PANCREAS|SNU349_KIDNEY|SNU398_LIVER|SNU410_PANCREAS|SNU449_LIVER|SNU503_LARGE_INTESTINE|SNU685_ENDOMETRIUM|SNU840_OVARY|SNU8_OVARY|SR786_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|SUIT2_PANCREAS|SUPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|SUPT1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|SW1463_LARGE_INTESTINE|SW403_LARGE_INTESTINE|SW48_LARGE_INTESTINE|SW620_LARGE_INTESTINE|SW837_LARGE_INTESTINE|SW982_SOFT_TISSUE|SYO1_SOFT_TISSUE|T24_URINARY_TRACT|T3M4_PANCREAS|T84_LARGE_INTESTINE|T98G_CENTRAL_NERVOUS_SYSTEM|TC106_BONE|TC138_BONE|TC205_BONE|TCCPAN2_PANCREAS|TCCSUP_URINARY_TRACT|TE1_OESOPHAGUS|TE4_OESOPHAGUS|TE5_OESOPHAGUS|TE6_OESOPHAGUS|TE8_OESOPHAGUS|TEN_ENDOMETRIUM|TF1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|THP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|TOV21G_OVARY|TUHR10TKB_KIDNEY|TUHR14TKB_KIDNEY|TUHR4TKB_KIDNEY|U118MG_CENTRAL_NERVOUS_SYSTEM|U178_CENTRAL_NERVOUS_SYSTEM|U251MG_CENTRAL_NERVOUS_SYSTEM|U2OS_BONE|U343_CENTRAL_NERVOUS_SYSTEM|U87MG_CENTRAL_NERVOUS_SYSTEM|U937_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE|UACC257_SKIN|UACC62_SKIN|UMUC1_URINARY_TRACT|UMUC3_URINARY_TRACT|UOK101_KIDNEY|UPCISCC152_UPPER_AERODIGESTIVE_TRACT|UW228_CENTRAL_NERVOUS_SYSTEM|VMCUB1_URINARY_TRACT|VMRCRCW_KIDNEY|WM115_SKIN|WM1799_SKIN|WM2664_SKIN|WM793_SKIN|WM983B_SKIN|YAPC_PANCREAS|YD38_UPPER_AERODIGESTIVE_TRACT|YH13_CENTRAL_NERVOUS_SYSTEM|YKG1_CENTRAL_NERVOUS_SYSTEM|ZR751_BREAST
# plot00(DR7_viability = DR7_viability,
# data = aH436,
# main = '436 Human scores')
bH436 = tranSamp(
object = aH436,
percent = percent,
FUN = function(x) {
r = apply(x, 2, function(y) {
y
})
return(r)
},
speed = TRUE,
seed = seed
)
##
## Total observations: 15753. Selected samples: 15753 [Shuffled]
##
# PCA or UMAP
bH436$rdata = pcaUmap0(
bH436$rdata,
main = 'H436',
cname = 'H436',
umap = FALSE,
ncomp = 3
)
## pca in progress ...
#### #### #### #### #### #### #### #### #### #### #### #### ####
### 2.4 GTEX
aG = initialization(
data = GTEX[, !names(GTEX) %in% c('human_ensembl_gene')],
response = NULL,
speed = TRUE,
seed = seed
)
##
## Response is not found and then set to NULL
##
##
## Input dataset : rows = 16403 | columns = 54
##
##
## Variable names (54):
## Adipose_Subcutaneous|Adipose_Visceral_(Omentum)|Adrenal_Gland|Artery_Aorta|Artery_Coronary|Artery_Tibial|Bladder|Brain_Amygdala|Brain_Anterior_cingulate_cortex_(BA24)|Brain_Caudate_(basal_ganglia)|Brain_Cerebellar_Hemisphere|Brain_Cerebellum|Brain_Cortex|Brain_Frontal_Cortex_(BA9)|Brain_Hippocampus|Brain_Hypothalamus|Brain_Nucleus_accumbens_(basal_ganglia)|Brain_Putamen_(basal_ganglia)|Brain_Spinal_cord_(cervical_c-1)|Brain_Substantia_nigra|Breast_Mammary_Tissue|Cells_EBV-transformed_lymphocytes|Cells_Transformed_fibroblasts|Cervix_Ectocervix|Cervix_Endocervix|Colon_Sigmoid|Colon_Transverse|Esophagus_Gastroesophageal_Junction|Esophagus_Mucosa|Esophagus_Muscularis|Fallopian_Tube|Heart_Atrial_Appendage|Heart_Left_Ventricle|Kidney_Cortex|Liver|Lung|Minor_Salivary_Gland|Muscle_Skeletal|Nerve_Tibial|Ovary|Pancreas|Pituitary|Prostate|Skin_Not_Sun_Exposed_(Suprapubic)|Skin_Sun_Exposed_(Lower_leg)|Small_Intestine_Terminal_Ileum|Spleen|Stomach|Testis|Thyroid|Uterus|Vagina|Whole_Blood|MGI id
## No duplicate found!
##
##
##
## Response is not included!
## Cluster size 16403 broken into 334 16069
## Done cluster 334
## Cluster size 16069 broken into 13966 2103
## Cluster size 13966 broken into 9404 4562
## Cluster size 9404 broken into 3365 6039
## Cluster size 3365 broken into 2058 1307
## Cluster size 2058 broken into 15 2043
## Done cluster 15
## Cluster size 2043 broken into 1216 827
## Done cluster 1216
## Done cluster 827
## Done cluster 2043
## Done cluster 2058
## Done cluster 1307
## Done cluster 3365
## Cluster size 6039 broken into 5580 459
## Cluster size 5580 broken into 761 4819
## Done cluster 761
## Cluster size 4819 broken into 1464 3355
## Done cluster 1464
## Cluster size 3355 broken into 2954 401
## Cluster size 2954 broken into 2926 28
## Cluster size 2926 broken into 1007 1919
## Done cluster 1007
## Cluster size 1919 broken into 272 1647
## Done cluster 272
## Cluster size 1647 broken into 237 1410
## Done cluster 237
## Done cluster 1410
## Done cluster 1647
## Done cluster 1919
## Done cluster 2926
## Done cluster 28
## Done cluster 2954
## Done cluster 401
## Done cluster 3355
## Done cluster 4819
## Done cluster 5580
## Done cluster 459
## Done cluster 6039
## Done cluster 9404
## Cluster size 4562 broken into 950 3612
## Done cluster 950
## Cluster size 3612 broken into 1124 2488
## Done cluster 1124
## Cluster size 2488 broken into 1625 863
## Cluster size 1625 broken into 461 1164
## Done cluster 461
## Done cluster 1164
## Done cluster 1625
## Done cluster 863
## Done cluster 2488
## Done cluster 3612
## Done cluster 4562
## Done cluster 13966
## Cluster size 2103 broken into 410 1693
## Done cluster 410
## Cluster size 1693 broken into 50 1643
## Done cluster 50
## Cluster size 1643 broken into 1376 267
## Done cluster 1376
## Done cluster 267
## Done cluster 1643
## Done cluster 1693
## Done cluster 2103
## Done cluster 16069
##
##
##
## Final dataset : rows = 16403 | columns = 53
##
##
##
## Variable names :
## length = 53
##
##
## list: Adipose_Subcutaneous|Adipose_Visceral_(Omentum)|Adrenal_Gland|Artery_Aorta|Artery_Coronary|Artery_Tibial|Bladder|Brain_Amygdala|Brain_Anterior_cingulate_cortex_(BA24)|Brain_Caudate_(basal_ganglia)|Brain_Cerebellar_Hemisphere|Brain_Cerebellum|Brain_Cortex|Brain_Frontal_Cortex_(BA9)|Brain_Hippocampus|Brain_Hypothalamus|Brain_Nucleus_accumbens_(basal_ganglia)|Brain_Putamen_(basal_ganglia)|Brain_Spinal_cord_(cervical_c-1)|Brain_Substantia_nigra|Breast_Mammary_Tissue|Cells_EBV-transformed_lymphocytes|Cells_Transformed_fibroblasts|Cervix_Ectocervix|Cervix_Endocervix|Colon_Sigmoid|Colon_Transverse|Esophagus_Gastroesophageal_Junction|Esophagus_Mucosa|Esophagus_Muscularis|Fallopian_Tube|Heart_Atrial_Appendage|Heart_Left_Ventricle|Kidney_Cortex|Liver|Lung|Minor_Salivary_Gland|Muscle_Skeletal|Nerve_Tibial|Ovary|Pancreas|Pituitary|Prostate|Skin_Not_Sun_Exposed_(Suprapubic)|Skin_Sun_Exposed_(Lower_leg)|Small_Intestine_Terminal_Ileum|Spleen|Stomach|Testis|Thyroid|Uterus|Vagina|Whole_Blood
# plot00(DR7_viability = DR7_viability,
# data = aG,
# main = 'GTEX')
bG = tranSamp(
object = aG,
percent = percent,
FUN = function(x) {
r = apply(x, 2, function(y) {
# Scaling has no meaning here # HAMED
y2 = log(log(y + 1) + 1)
})
return(r)
},
speed = TRUE,
seed = seed
)
##
## Total observations: 16403. Selected samples: 16403 [Shuffled]
##
# PCA or UMAP
bG$rdata = pcaUmap0(
bG$rdata,
main = 'GTEX',
cname = 'GTEX',
umap = FALSE,
ncomp = 3
)
## pca in progress ...
#### #### #### #### #### #### #### #### #### #### #### #### ####
### PLI
aP = initialization(
data = PLI,
response = NULL,
speed = TRUE,
seed = seed
)
##
## Response is not found and then set to NULL
##
##
## Input dataset : rows = 16044 | columns = 2
##
##
## Variable names (2):
## MGI id|pLI
## No duplicate found!
##
##
##
## Response is not included!
##
##
##
## [if possible] missing data removed!
##
##
##
## Final dataset : rows = 16044 | columns = 1
##
##
##
## Variable names :
## length = 1
##
##
## list: pLI
# plot00(DR7_viability = DR7_viability,
# data = aP,
# main = 'PLI')
bP = tranSamp(
object = aP,
percent = percent,
FUN = function(x) {
r = apply(x, 2, function(y) {
(scale(y, min(y), max(y) - min(y)) - .5) * 2
})
return(r)
},
speed = TRUE,
seed = seed
)
##
## Total observations: 16044. Selected samples: 16044 [Shuffled]
##
#### #### #### #### #### #### #### #### #### #### #### #### ####
pie2(c(
rep('Gtex', dim(bG$rdata)[1]),
rep('DMDD', dim(bM$rdata)[1]),
rep('SHET', dim(bS$rdata)[1]),
rep('PLI', dim(bP$rdata)[1]),
rep('H436', dim(bH436$rdata)[1]),
rep('Human Crisper lines', dim(bH$rdata)[1])
),
col = 1:6,
#density=0,
main = 'Datasets contribution')
####################
# Components
# a/bM ===> DMDD
# a/bS ===> SHet
# a/bH ===> Humans scores
# a/bG ===> Gtex
# a/bP ===> PLI
# a/bH436 ===> 436 cell lines
####################
#### #### #### #### #### #### #### #### #### #### #### #### ####
# 3. Merging the entire datasets from the previous datasets [key = MGI id]
f0 =
merge(
merge(
merge(
data.frame(
'MGI id' = rownames(bP$rdata),
bP$rdata ,
check.names = FALSE
),
merge(
data.frame(
'MGI id' = rownames(bM$rdata),
bM$rdata ,
check.names = FALSE
),
merge(
data.frame(
bH436$rdata,
'MGI id' = rownames(bH436$rdata),
check.names = FALSE
),
data.frame(
bG$rdata,
'MGI id' = rownames(bG$rdata),
check.names = FALSE
),
by = 'MGI id',
all = TRUE
),
by = 'MGI id',
all = TRUE
),
by = 'MGI id',
all = TRUE
),
data.frame(
bS$rdata,
'MGI id' = rownames(bS$rdata),
check.names = FALSE
),
by = 'MGI id',
all = TRUE
),
data.frame(
bH$rdata,
'MGI id' = rownames(bH$rdata),
check.names = FALSE
),
by = 'MGI id',
all = TRUE
)
# Do you want remove any dataset?
# DMDD GTEK 436HGenes Shet PLI
#f0 = f0[, !colnames(f0) %in% colnames(bH436$rdata)]
#f0 = f0[, !colnames(f0) %in% colnames(bP$rdata)]
#f0 = f0[, !colnames(f0) %in% colnames(bS$rdata)]
#f0 = f0[, !colnames(f0) %in% colnames(bM$rdata)]
#f0 = f0[, !colnames(f0) %in% colnames(bG$rdata)]
#f0 = f0[, !colnames(f0) %in% colnames(bH$rdata)]
message('Final dataset: \n\t ', paste(c('Rows = ', 'columns = '), dim(f0), collapse = ' | '))
## Final dataset:
## Rows = 23739 | columns = 42
message(
'Variables: \n\t length = ',
length(colnames(f0)),
'\n\t list: ',
paste(colnames(f0), collapse = ', ')
)
## Variables:
## length = 42
## list: MGI id, pLI, 5-somite stage, 6-somite stage, 7-somite stage, 8-somite stage, 9-somite stage, 10-somite stage, 11-somite stage, 12-somite stage, 13-somite stage, 14-somite stage, 15-somite stage, 16-somite stage, 17-somite stage, 18-somite stage, 19-somite stage, 20-somite stage, 21-somite stage, 22-somite stage, 23-somite stage, 24-somite stage, 25-somite stage, 26-somite stage, 27-somite stage, 28-somite stage, 34-somite stage, 35-somite stage, 36-somite stage, H4361, H4362, H4363, GTEX1, GTEX2, GTEX3, s_het, HScore1, HScore2, HScore3, HScore4, HScore5, HScore6
#### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
# 4.
# 4.1 add viability labels
final_data = f0
final_data = merge(DR7_viability, final_data, by = 'MGI id', all.y = TRUE)
final_data = final_data[complete.cases(final_data[, -c(1:2)]),]
#Just for me (rename the viability column)
names(final_data)[which(names(final_data) %in% 'Viability')] = 'viability'
levels(final_data$viability)
## [1] "Conflicting" "Lethal" "Subviable" "Viable"
# 4.2 ready to NA ---> unknown
final_data$viability = addNA(final_data$viability)
levels(final_data$viability)
## [1] "Conflicting" "Lethal" "Subviable" "Viable" NA
levels(final_data$viability)[which(is.na(levels(final_data$viability)))] = 'unknown'
addmargins(table(final_data$viability, useNA = 'always'))
##
## Conflicting Lethal Subviable Viable unknown <NA>
## 23 868 326 1809 8881 0
## Sum
## 11907
# ready for processing data
rownames(final_data) = final_data[, 'MGI id']
final_data = forg = final_data[, -which(colnames(final_data) %in% 'MGI id')]
pie2(
x = final_data$viability,
col = 1:nlevels(final_data$viability),
main = 'Final input file',
cex = .85
)
# 4.3 shuffling data ...
nshuffle = function(x, n = 0, seed) {
set.seed(seed)
message('\n', n, 'x Shuffling the final dataset ... \n')
if (n > 0) {
for (i in 1:n) {
message('Shuffle id: ', i)
x = x[sample(nrow(x)), ]
}
}
return(x)
}
f00 = final_data
f00 = nshuffle(x = f00, n = 10, seed = seed)
##
## 10x Shuffling the final dataset ...
## Shuffle id: 1
## Shuffle id: 2
## Shuffle id: 3
## Shuffle id: 4
## Shuffle id: 5
## Shuffle id: 6
## Shuffle id: 7
## Shuffle id: 8
## Shuffle id: 9
## Shuffle id: 10
#### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
# 4.4 Merge SubViables and Conflictings into unknown
f00.with.conf = f00
levels(f00$viability)[which(levels(f00$viability) %in% c('Conflicting', 'Subviable', ''))] =
'unknown'
f00 = droplevels(f00)
addmargins(table(f00.with.conf$viability))
##
## Conflicting Lethal Subviable Viable unknown Sum
## 23 868 326 1809 8881 11907
addmargins(table(f00$viability))
##
## unknown Lethal Viable Sum
## 9230 868 1809 11907
#### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
# Enrichment using clustering
# 6. initial clustering and [if possible] assigning unknowns to a class (_p)
mergOrg = mergeClust(
f00,
seed = seed,
sub = 'Enrichment',
method = 'hkmeans',
cl = 110,
minin = 20,
thresh = 30
)
##
## [h]kmeans in progress ....
## *info : nstart is not defined for hkmeans method.
## N:22, Ratio: Inf; Cluster 10 unknown assigned to -> Lethal; Details: unknown:66; Lethal:22; Viable:0
## N:35, Ratio: 34; Cluster 58 unknown assigned to -> Viable; Details: unknown:126; Lethal:1; Viable:34
## N:22, Ratio: Inf; Cluster 68 unknown assigned to -> Lethal; Details: unknown:64; Lethal:22; Viable:0
##
## Lethal Lethal_p unknown Viable Viable_p
## 868 130 8974 1809 126
##
## Total clusters: 110. Min points in clusters [including unknown]: 10. Min information required in each cluster : 20 Points.
### Second plot
tplot = table(mergOrg$out$clusters,
mergOrg$out$viability,
dnn = c('Cluster', 'Viability'))
print(tplot)
## Viability
## Cluster unknown Lethal Viable
## 1 170 5 39
## 2 58 3 11
## 3 134 3 29
## 4 99 4 23
## 5 193 14 47
## 6 179 8 54
## 7 119 2 28
## 8 73 2 16
## 9 148 8 40
## 10 66 22 0
## 11 113 15 14
## 12 49 5 0
## 13 64 5 10
## 14 108 4 36
## 15 138 11 34
## 16 51 19 0
## 17 93 3 18
## 18 88 3 16
## 19 172 5 39
## 20 115 6 23
## 21 85 4 27
## 22 97 11 13
## 23 92 6 32
## 24 8 2 3
## 25 30 17 0
## 26 124 12 29
## 27 76 2 13
## 28 128 12 23
## 29 106 4 19
## 30 160 6 41
## 31 174 19 38
## 32 92 4 17
## 33 171 24 26
## 34 59 1 17
## 35 37 7 0
## 36 93 2 21
## 37 82 5 26
## 38 196 11 51
## 39 80 17 5
## 40 104 8 16
## 41 162 9 46
## 42 116 1 28
## 43 67 6 11
## 44 74 3 27
## 45 151 11 35
## 46 69 4 21
## 47 49 15 0
## 48 43 15 0
## 49 66 10 9
## 50 139 12 42
## 51 96 8 25
## 52 172 9 33
## 53 65 1 18
## 54 75 20 5
## 55 17 9 0
## 56 130 3 46
## 57 189 18 49
## 58 126 1 34
## 59 49 10 3
## 60 63 4 8
## 61 66 4 8
## 62 41 17 0
## 63 35 11 0
## 64 131 5 31
## 65 91 1 19
## 66 50 19 0
## 67 47 18 2
## 68 64 22 0
## 69 222 8 53
## 70 43 3 8
## 71 116 6 32
## 72 41 2 15
## 73 48 14 0
## 74 39 15 0
## 75 21 4 3
## 76 118 7 31
## 77 35 12 0
## 78 102 11 7
## 79 17 4 0
## 80 73 0 10
## 81 104 9 30
## 82 42 13 0
## 83 52 1 8
## 84 43 13 3
## 85 130 5 28
## 86 93 4 32
## 87 63 1 12
## 88 50 2 21
## 89 55 15 11
## 90 64 8 0
## 91 125 8 16
## 92 52 3 7
## 93 26 5 0
## 94 51 5 0
## 95 220 8 47
## 96 34 17 3
## 97 32 9 0
## 98 12 3 1
## 99 14 4 0
## 100 54 11 0
## 101 13 2 0
## 102 46 13 0
## 103 76 2 14
## 104 36 4 0
## 105 53 2 9
## 106 39 16 0
## 107 49 0 14
## 108 13 4 0
## 109 38 7 0
## 110 9 1 0
Plotlabs = t(tplot)
plotlabelsCol = sign(table(mergOrg$out$merged.clu, mergOrg$out$clusters)[2, , drop =
FALSE])
a = barplot(
(Plotlabs),
las = 3,
col = 3:5,
#Plotlabs + 2,
xlab = ' Cluster [blue = merged]',
ylab = 'Points in cluster',
xaxt = 'n'
)
legend(
'top',
legend = rownames(Plotlabs),
fill = 3:5,
#Plotlabs + 2,
xpd = TRUE,
horiz = TRUE,
inset = -.081
)
mtext(
side = 1,
at = a,
#adj = 0,
text = 1:length(a),
col = plotlabelsCol * 3 + 1,
las = 2,
xpd = TRUE,
cex = .6
)
###
# 7.
pie2(
x = mergOrg$out$assigned.viability,
col = 1:nlevels(mergOrg$out$assigned.viability),
main = 'Enrichment (_p)',
cex = .75
)
table(mergOrg$out$assigned.viability)
##
## Lethal Lethal_p unknown Viable Viable_p
## 868 130 8974 1809 126
# merge predictions (_p) to the original data
### evaluate clustering predictions using DR8
mergedTwo = merge(
data.frame(
'MGI id' = rownames(mergOrg$out),
mergOrg$out,
check.names = FALSE
),
viabi_DR8,
by = 'MGI id',
all.y = TRUE,
all.x = FALSE
)
t2a = droplevels(subset(
mergedTwo,
!(assigned.viability %in% 'unknown') &
!(viability.dr8 %in% 'Subviable')
))
table(t2a$assigned.viability,
t2a$viability.dr8,
dnn = c('Predicted', 'TRUE'))
## TRUE
## Predicted Lethal Viable
## Lethal 844 0
## Lethal_p 1 0
## Viable 0 1746
## Viable_p 0 1
# Preparing to add enrichment results to the raw data
newdata = f00
newdata$viability[mergOrg$out$assigned.viability %in% 'Lethal_p'] = 'Lethal'
newdata$viability[mergOrg$out$assigned.viability %in% 'Viable_p'] = 'Viable'
addmargins(table(newdata$viability, useNA = 'always'))
##
## unknown Lethal Viable <NA> Sum
## 8974 998 1935 0 11907
addmargins(table(newdata$viability, f00$viability, useNA = 'always'))
##
## unknown Lethal Viable <NA> Sum
## unknown 8974 0 0 0 8974
## Lethal 130 868 0 0 998
## Viable 126 0 1809 0 1935
## <NA> 0 0 0 0 0
## Sum 9230 868 1809 0 11907
pie2(
x = newdata$viability,
col = 1:nlevels(newdata$viability),
main = 'Final data after enrichment'
)
#### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
# 8. Final preparations before feeding to ML
###########################
f2 = f20 = fbackup = newdata
###########################
f20$viability[f20$viability %in% c('unknown', 'Subviable', 'Conflicting')] = NA
f20 = droplevels(data.frame (f20, check.names = FALSE))
f20 = f20.backup = f20[!is.na(f20$viability),]
addmargins(table(f2$viability , useNA = 'always'))
##
## unknown Lethal Viable <NA> Sum
## 8974 998 1935 0 11907
addmargins(table(f20$viability, useNA = 'always'))
##
## Lethal Viable <NA> Sum
## 998 1935 0 2933
#### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
# # 8.0.1 suspesion data removal
# f010 = suspectRemove(replica = 1500,data = f2,seed = seed,nree = 150,formula = viability~.)
# f011 = rownames(f010$all.sus)
# message( 'The number of removed Genes :', sum(rownames(f20 ) %in% rownames(f010$all.sus)))
# f2 = f2 [!rownames(f2 ) %in% rownames(f010$all.sus),]
# f20 = f20[!rownames(f20) %in% rownames(f010$all.sus),]
#
#
# table( f2$viability)
# table(f20$viability)
###########################
#### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
# ML starts here
library(randomForestSRC)
library(randomForest)
## randomForest 4.6-14
## Type rfNews() to see new features/changes/bug fixes.
library(pROC)
## Type 'citation("pROC")' for a citation.
##
## Attaching package: 'pROC'
## The following objects are masked from 'package:stats':
##
## cov, smooth, var
f000 = data.frame(f2,check.names=TRUE)
#### Prediction after removing th suspecious cases
levels(f000$viability)[which(levels(f000$viability) %in% c('Subviable', 'Conflicting', 'unknown'))] = 'unknown'
# Separate Lethals and Viables for the downsampling
nx3 = droplevels(f000[which(!f000$viability %in% c('unknown')), ])
nxLethal = subset(nx3, viability %in% 'Lethal')
nxViable = subset(nx3, viability %in% 'Viable')
lnxl = nrow(nxLethal)
lnxV = nrow(nxViable)
# Down sampling
set.seed(seed)
if (lnxl < lnxV) {
nx3 = rbind(nxLethal, nxViable[sample(1:lnxV, size = lnxl, replace = FALSE), ])
} else{
nx3 = rbind(nxViable, nxLethal[sample(1:lnxl, size = lnxV, replace = FALSE), ])
}
#######
# Specify train and test set
nx3 = randomForest::na.roughfix(nx3)
n = nrow(nx3)
x = sample(1:n, size = .60 * n)
train = (nx3[x ,])
test = (nx3[-x,])
##### Random forest
# rf = rfsrc(
# formula = viability ~ .,
# data = droplevels(train),
# ntree = 1500,
# importance = TRUE,
# proximity = 'oob',
# seed = seed,
# nodedepth =10,
# nodesize = 1
# )
# plot(rf, plots.one.page = FALSE)
##############
set.seed(seed = seed)
th = .41
rf = randomForest(
formula = viability ~ .,
data = droplevels(train),
importance = TRUE,
proximity = TRUE,
#xtest = test[, -1],
#ytest = test$viability,
cutoff = c(th, 1 - th),
ntree = 1500,
mtry = 10,
localImp = TRUE,
na.action = na.omit,
norm.votes=TRUE,
do.trac=1
)
## ntree OOB 1 2
## 1: 30.47% 27.18% 33.48%
## 2: 29.14% 22.32% 35.64%
## 3: 29.32% 19.32% 38.95%
## 4: 30.78% 19.80% 41.62%
## 5: 28.99% 20.11% 37.57%
## 6: 29.46% 21.16% 37.57%
## 7: 28.94% 22.42% 35.33%
## 8: 27.64% 20.93% 34.23%
## 9: 27.11% 21.73% 32.44%
## 10: 26.83% 20.95% 32.66%
## 11: 27.20% 21.72% 32.66%
## 12: 26.63% 21.89% 31.33%
## 13: 26.53% 21.51% 31.50%
## 14: 26.11% 21.51% 30.67%
## 15: 25.81% 21.68% 29.90%
## 16: 25.48% 20.67% 30.23%
## 17: 25.81% 21.18% 30.40%
## 18: 24.90% 21.01% 28.74%
## 19: 24.81% 21.34% 28.24%
## 20: 24.98% 21.34% 28.57%
## 21: 24.90% 21.34% 28.41%
## 22: 25.23% 21.85% 28.57%
## 23: 25.15% 21.85% 28.41%
## 24: 24.81% 21.68% 27.91%
## 25: 24.31% 21.85% 26.74%
## 26: 24.56% 21.68% 27.41%
## 27: 24.23% 21.51% 26.91%
## 28: 24.14% 21.68% 26.58%
## 29: 24.06% 21.51% 26.58%
## 30: 23.89% 21.51% 26.25%
## 31: 23.73% 21.85% 25.58%
## 32: 23.73% 21.51% 25.91%
## 33: 23.89% 22.18% 25.58%
## 34: 23.73% 21.68% 25.75%
## 35: 23.14% 21.34% 24.92%
## 36: 23.06% 21.51% 24.58%
## 37: 23.06% 21.85% 24.25%
## 38: 23.22% 21.68% 24.75%
## 39: 22.97% 22.02% 23.92%
## 40: 23.48% 22.35% 24.58%
## 41: 23.48% 22.52% 24.42%
## 42: 23.64% 22.52% 24.75%
## 43: 23.64% 22.69% 24.58%
## 44: 23.98% 22.86% 25.08%
## 45: 23.56% 22.52% 24.58%
## 46: 23.73% 22.52% 24.92%
## 47: 23.14% 22.35% 23.92%
## 48: 23.31% 22.35% 24.25%
## 49: 22.56% 21.85% 23.26%
## 50: 22.89% 22.69% 23.09%
## 51: 23.14% 22.69% 23.59%
## 52: 22.97% 23.19% 22.76%
## 53: 22.97% 23.70% 22.26%
## 54: 23.48% 24.37% 22.59%
## 55: 23.39% 24.71% 22.09%
## 56: 22.89% 24.03% 21.76%
## 57: 22.89% 23.53% 22.26%
## 58: 22.72% 23.87% 21.59%
## 59: 22.31% 23.53% 21.10%
## 60: 22.72% 23.70% 21.76%
## 61: 22.89% 23.36% 22.43%
## 62: 23.14% 23.87% 22.43%
## 63: 23.14% 23.87% 22.43%
## 64: 23.14% 24.03% 22.26%
## 65: 23.48% 24.54% 22.43%
## 66: 22.81% 23.87% 21.76%
## 67: 22.64% 23.70% 21.59%
## 68: 23.39% 23.70% 23.09%
## 69: 23.22% 24.03% 22.43%
## 70: 23.56% 24.37% 22.76%
## 71: 23.48% 24.20% 22.76%
## 72: 23.31% 23.87% 22.76%
## 73: 22.97% 23.36% 22.59%
## 74: 22.64% 23.36% 21.93%
## 75: 22.81% 23.53% 22.09%
## 76: 22.89% 23.70% 22.09%
## 77: 23.22% 23.53% 22.92%
## 78: 23.56% 23.70% 23.42%
## 79: 23.73% 23.70% 23.75%
## 80: 23.89% 23.70% 24.09%
## 81: 24.23% 24.03% 24.42%
## 82: 24.06% 24.20% 23.92%
## 83: 23.56% 24.03% 23.09%
## 84: 23.31% 23.87% 22.76%
## 85: 23.64% 23.70% 23.59%
## 86: 24.06% 24.20% 23.92%
## 87: 23.73% 23.70% 23.75%
## 88: 23.89% 24.03% 23.75%
## 89: 23.64% 24.03% 23.26%
## 90: 23.56% 24.20% 22.92%
## 91: 23.31% 23.87% 22.76%
## 92: 23.22% 24.20% 22.26%
## 93: 23.22% 24.03% 22.43%
## 94: 23.39% 24.03% 22.76%
## 95: 23.48% 24.37% 22.59%
## 96: 23.48% 24.54% 22.43%
## 97: 23.14% 23.87% 22.43%
## 98: 23.22% 23.70% 22.76%
## 99: 23.14% 23.70% 22.59%
## 100: 23.31% 23.87% 22.76%
## 101: 23.22% 23.87% 22.59%
## 102: 22.97% 23.87% 22.09%
## 103: 23.22% 24.03% 22.43%
## 104: 23.14% 24.03% 22.26%
## 105: 23.31% 24.54% 22.09%
## 106: 23.22% 24.37% 22.09%
## 107: 23.14% 24.20% 22.09%
## 108: 23.06% 24.20% 21.93%
## 109: 23.22% 24.37% 22.09%
## 110: 22.97% 23.87% 22.09%
## 111: 23.39% 24.03% 22.76%
## 112: 23.48% 24.20% 22.76%
## 113: 23.64% 24.37% 22.92%
## 114: 23.64% 24.54% 22.76%
## 115: 23.48% 24.37% 22.59%
## 116: 23.31% 24.54% 22.09%
## 117: 22.81% 24.20% 21.43%
## 118: 23.06% 24.03% 22.09%
## 119: 23.39% 24.54% 22.26%
## 120: 23.14% 24.20% 22.09%
## 121: 23.64% 24.54% 22.76%
## 122: 23.48% 24.54% 22.43%
## 123: 23.64% 24.54% 22.76%
## 124: 23.31% 24.54% 22.09%
## 125: 23.39% 24.54% 22.26%
## 126: 23.64% 24.54% 22.76%
## 127: 23.31% 24.54% 22.09%
## 128: 23.56% 24.71% 22.43%
## 129: 23.31% 24.71% 21.93%
## 130: 23.22% 24.71% 21.76%
## 131: 23.22% 24.87% 21.59%
## 132: 23.22% 25.04% 21.43%
## 133: 22.97% 24.87% 21.10%
## 134: 23.06% 24.87% 21.26%
## 135: 22.89% 24.54% 21.26%
## 136: 23.06% 24.54% 21.59%
## 137: 22.89% 24.54% 21.26%
## 138: 22.89% 24.37% 21.43%
## 139: 23.06% 24.54% 21.59%
## 140: 23.31% 24.87% 21.76%
## 141: 23.39% 25.04% 21.76%
## 142: 23.31% 25.04% 21.59%
## 143: 23.39% 25.04% 21.76%
## 144: 23.39% 25.04% 21.76%
## 145: 23.22% 24.87% 21.59%
## 146: 23.31% 25.04% 21.59%
## 147: 23.31% 24.87% 21.76%
## 148: 23.48% 25.21% 21.76%
## 149: 23.31% 25.21% 21.43%
## 150: 23.22% 24.87% 21.59%
## 151: 23.22% 24.71% 21.76%
## 152: 23.14% 24.71% 21.59%
## 153: 23.31% 24.87% 21.76%
## 154: 23.14% 24.54% 21.76%
## 155: 23.22% 24.54% 21.93%
## 156: 23.39% 24.87% 21.93%
## 157: 23.39% 25.04% 21.76%
## 158: 23.64% 25.38% 21.93%
## 159: 23.48% 25.21% 21.76%
## 160: 23.48% 25.21% 21.76%
## 161: 23.39% 25.21% 21.59%
## 162: 23.48% 25.21% 21.76%
## 163: 23.48% 25.21% 21.76%
## 164: 23.56% 25.21% 21.93%
## 165: 23.39% 25.21% 21.59%
## 166: 23.48% 25.21% 21.76%
## 167: 23.56% 25.04% 22.09%
## 168: 23.39% 25.04% 21.76%
## 169: 23.48% 25.21% 21.76%
## 170: 23.64% 24.87% 22.43%
## 171: 23.56% 24.71% 22.43%
## 172: 23.73% 24.87% 22.59%
## 173: 23.64% 24.87% 22.43%
## 174: 23.48% 24.87% 22.09%
## 175: 23.39% 25.04% 21.76%
## 176: 23.64% 24.87% 22.43%
## 177: 23.64% 24.87% 22.43%
## 178: 23.73% 25.04% 22.43%
## 179: 23.64% 24.87% 22.43%
## 180: 23.56% 24.87% 22.26%
## 181: 23.64% 24.87% 22.43%
## 182: 23.56% 24.87% 22.26%
## 183: 23.64% 25.04% 22.26%
## 184: 23.64% 25.04% 22.26%
## 185: 23.56% 24.87% 22.26%
## 186: 23.56% 24.87% 22.26%
## 187: 23.56% 24.87% 22.26%
## 188: 23.48% 24.71% 22.26%
## 189: 23.14% 24.71% 21.59%
## 190: 23.06% 24.54% 21.59%
## 191: 23.14% 24.37% 21.93%
## 192: 23.06% 24.54% 21.59%
## 193: 23.14% 24.54% 21.76%
## 194: 23.14% 24.87% 21.43%
## 195: 23.39% 25.04% 21.76%
## 196: 23.56% 25.04% 22.09%
## 197: 23.48% 25.04% 21.93%
## 198: 23.48% 24.87% 22.09%
## 199: 23.39% 24.87% 21.93%
## 200: 23.31% 24.87% 21.76%
## 201: 23.31% 24.87% 21.76%
## 202: 23.48% 24.71% 22.26%
## 203: 23.64% 24.71% 22.59%
## 204: 23.81% 24.87% 22.76%
## 205: 23.56% 24.71% 22.43%
## 206: 23.64% 24.71% 22.59%
## 207: 23.64% 24.71% 22.59%
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## 283: 23.06% 24.37% 21.76%
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## 341: 23.31% 24.03% 22.59%
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## 343: 23.14% 23.87% 22.43%
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## 354: 23.14% 24.03% 22.26%
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## 357: 23.06% 23.87% 22.26%
## 358: 22.89% 23.70% 22.09%
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## 383: 22.72% 24.20% 21.26%
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## 386: 22.81% 24.37% 21.26%
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## 681: 22.39% 23.19% 21.59%
## 682: 22.39% 23.19% 21.59%
## 683: 22.39% 23.19% 21.59%
## 684: 22.39% 23.03% 21.76%
## 685: 22.31% 23.03% 21.59%
## 686: 22.31% 23.03% 21.59%
## 687: 22.39% 23.03% 21.76%
## 688: 22.47% 23.03% 21.93%
## 689: 22.47% 23.03% 21.93%
## 690: 22.47% 23.03% 21.93%
## 691: 22.47% 23.03% 21.93%
## 692: 22.47% 23.03% 21.93%
## 693: 22.47% 23.03% 21.93%
## 694: 22.39% 23.03% 21.76%
## 695: 22.39% 23.03% 21.76%
## 696: 22.39% 23.03% 21.76%
## 697: 22.31% 23.03% 21.59%
## 698: 22.22% 23.03% 21.43%
## 699: 22.22% 23.03% 21.43%
## 700: 22.14% 23.03% 21.26%
## 701: 22.06% 23.03% 21.10%
## 702: 22.22% 23.03% 21.43%
## 703: 22.22% 22.86% 21.59%
## 704: 22.31% 22.86% 21.76%
## 705: 22.31% 22.86% 21.76%
## 706: 22.31% 22.86% 21.76%
## 707: 22.39% 22.86% 21.93%
## 708: 22.39% 22.86% 21.93%
## 709: 22.39% 22.86% 21.93%
## 710: 22.39% 22.86% 21.93%
## 711: 22.39% 22.86% 21.93%
## 712: 22.47% 23.03% 21.93%
## 713: 22.39% 22.86% 21.93%
## 714: 22.39% 22.86% 21.93%
## 715: 22.39% 22.86% 21.93%
## 716: 22.39% 22.86% 21.93%
## 717: 22.39% 22.86% 21.93%
## 718: 22.47% 23.03% 21.93%
## 719: 22.47% 23.03% 21.93%
## 720: 22.47% 23.03% 21.93%
## 721: 22.47% 23.03% 21.93%
## 722: 22.31% 23.03% 21.59%
## 723: 22.47% 23.03% 21.93%
## 724: 22.56% 23.03% 22.09%
## 725: 22.56% 23.03% 22.09%
## 726: 22.39% 23.03% 21.76%
## 727: 22.39% 23.03% 21.76%
## 728: 22.47% 23.03% 21.93%
## 729: 22.47% 23.03% 21.93%
## 730: 22.47% 23.03% 21.93%
## 731: 22.39% 23.03% 21.76%
## 732: 22.22% 23.03% 21.43%
## 733: 22.31% 23.03% 21.59%
## 734: 22.31% 23.03% 21.59%
## 735: 22.39% 23.03% 21.76%
## 736: 22.39% 23.19% 21.59%
## 737: 22.31% 23.19% 21.43%
## 738: 22.31% 23.19% 21.43%
## 739: 22.31% 23.19% 21.43%
## 740: 22.31% 23.19% 21.43%
## 741: 22.31% 23.19% 21.43%
## 742: 22.31% 23.19% 21.43%
## 743: 22.22% 23.19% 21.26%
## 744: 22.22% 23.19% 21.26%
## 745: 22.22% 23.19% 21.26%
## 746: 22.22% 23.19% 21.26%
## 747: 22.22% 23.19% 21.26%
## 748: 22.22% 23.19% 21.26%
## 749: 22.06% 23.19% 20.93%
## 750: 22.14% 23.19% 21.10%
## 751: 22.22% 23.19% 21.26%
## 752: 22.22% 23.19% 21.26%
## 753: 22.14% 23.03% 21.26%
## 754: 22.22% 23.03% 21.43%
## 755: 22.31% 23.19% 21.43%
## 756: 22.31% 23.19% 21.43%
## 757: 22.31% 23.19% 21.43%
## 758: 22.39% 23.19% 21.59%
## 759: 22.39% 23.19% 21.59%
## 760: 22.39% 23.19% 21.59%
## 761: 22.39% 23.19% 21.59%
## 762: 22.39% 23.19% 21.59%
## 763: 22.39% 23.19% 21.59%
## 764: 22.39% 23.19% 21.59%
## 765: 22.31% 23.03% 21.59%
## 766: 22.31% 23.03% 21.59%
## 767: 22.14% 23.03% 21.26%
## 768: 22.14% 23.03% 21.26%
## 769: 22.06% 22.86% 21.26%
## 770: 22.06% 22.86% 21.26%
## 771: 22.14% 23.03% 21.26%
## 772: 22.14% 23.03% 21.26%
## 773: 22.22% 23.03% 21.43%
## 774: 22.22% 23.03% 21.43%
## 775: 22.22% 23.03% 21.43%
## 776: 22.14% 22.86% 21.43%
## 777: 22.14% 22.86% 21.43%
## 778: 22.22% 22.86% 21.59%
## 779: 22.22% 22.86% 21.59%
## 780: 22.22% 22.86% 21.59%
## 781: 22.22% 22.86% 21.59%
## 782: 22.14% 22.69% 21.59%
## 783: 22.14% 22.69% 21.59%
## 784: 22.14% 22.69% 21.59%
## 785: 22.22% 22.86% 21.59%
## 786: 22.22% 22.86% 21.59%
## 787: 22.22% 22.86% 21.59%
## 788: 22.22% 22.86% 21.59%
## 789: 22.22% 22.86% 21.59%
## 790: 22.22% 22.86% 21.59%
## 791: 22.31% 22.86% 21.76%
## 792: 22.31% 22.86% 21.76%
## 793: 22.31% 22.86% 21.76%
## 794: 22.31% 22.86% 21.76%
## 795: 22.31% 22.86% 21.76%
## 796: 22.31% 22.86% 21.76%
## 797: 22.22% 22.86% 21.59%
## 798: 22.31% 22.86% 21.76%
## 799: 22.31% 22.86% 21.76%
## 800: 22.31% 22.86% 21.76%
## 801: 22.22% 22.86% 21.59%
## 802: 22.22% 22.86% 21.59%
## 803: 22.31% 22.86% 21.76%
## 804: 22.31% 22.86% 21.76%
## 805: 22.31% 22.86% 21.76%
## 806: 22.31% 22.86% 21.76%
## 807: 22.14% 22.86% 21.43%
## 808: 22.14% 22.86% 21.43%
## 809: 22.31% 23.03% 21.59%
## 810: 22.31% 23.03% 21.59%
## 811: 22.22% 22.86% 21.59%
## 812: 22.22% 22.86% 21.59%
## 813: 22.31% 22.86% 21.76%
## 814: 22.39% 22.86% 21.93%
## 815: 22.31% 22.86% 21.76%
## 816: 22.39% 22.86% 21.93%
## 817: 22.39% 22.86% 21.93%
## 818: 22.31% 22.86% 21.76%
## 819: 22.22% 22.86% 21.59%
## 820: 22.31% 22.86% 21.76%
## 821: 22.31% 22.86% 21.76%
## 822: 22.31% 22.86% 21.76%
## 823: 22.31% 22.86% 21.76%
## 824: 22.14% 22.86% 21.43%
## 825: 22.22% 22.86% 21.59%
## 826: 22.22% 22.69% 21.76%
## 827: 22.22% 22.69% 21.76%
## 828: 22.22% 22.69% 21.76%
## 829: 22.22% 22.69% 21.76%
## 830: 22.31% 22.69% 21.93%
## 831: 22.31% 22.69% 21.93%
## 832: 22.31% 22.69% 21.93%
## 833: 22.31% 22.69% 21.93%
## 834: 22.22% 22.69% 21.76%
## 835: 22.22% 22.69% 21.76%
## 836: 22.22% 22.69% 21.76%
## 837: 22.22% 22.69% 21.76%
## 838: 22.22% 22.69% 21.76%
## 839: 22.22% 22.69% 21.76%
## 840: 22.22% 22.69% 21.76%
## 841: 22.14% 22.69% 21.59%
## 842: 22.06% 22.69% 21.43%
## 843: 22.14% 22.69% 21.59%
## 844: 22.06% 22.69% 21.43%
## 845: 21.97% 22.52% 21.43%
## 846: 22.14% 22.69% 21.59%
## 847: 22.22% 22.86% 21.59%
## 848: 22.14% 22.69% 21.59%
## 849: 22.22% 22.69% 21.76%
## 850: 22.22% 22.69% 21.76%
## 851: 22.14% 22.69% 21.59%
## 852: 22.06% 22.69% 21.43%
## 853: 22.14% 22.69% 21.59%
## 854: 22.22% 22.69% 21.76%
## 855: 22.22% 22.69% 21.76%
## 856: 22.14% 22.69% 21.59%
## 857: 22.22% 22.69% 21.76%
## 858: 22.31% 22.69% 21.93%
## 859: 22.22% 22.69% 21.76%
## 860: 22.22% 22.69% 21.76%
## 861: 22.14% 22.69% 21.59%
## 862: 22.22% 22.69% 21.76%
## 863: 22.06% 22.69% 21.43%
## 864: 21.97% 22.69% 21.26%
## 865: 21.97% 22.52% 21.43%
## 866: 22.14% 22.52% 21.76%
## 867: 22.06% 22.52% 21.59%
## 868: 22.06% 22.52% 21.59%
## 869: 22.06% 22.52% 21.59%
## 870: 21.97% 22.69% 21.26%
## 871: 21.80% 22.52% 21.10%
## 872: 21.89% 22.52% 21.26%
## 873: 21.72% 22.35% 21.10%
## 874: 21.80% 22.52% 21.10%
## 875: 21.80% 22.69% 20.93%
## 876: 21.89% 22.86% 20.93%
## 877: 21.89% 22.86% 20.93%
## 878: 21.89% 22.86% 20.93%
## 879: 21.97% 23.03% 20.93%
## 880: 21.97% 23.03% 20.93%
## 881: 22.06% 23.03% 21.10%
## 882: 22.14% 22.86% 21.43%
## 883: 22.06% 22.86% 21.26%
## 884: 21.97% 22.69% 21.26%
## 885: 21.89% 22.69% 21.10%
## 886: 21.89% 22.69% 21.10%
## 887: 21.89% 22.69% 21.10%
## 888: 21.89% 22.86% 20.93%
## 889: 21.89% 22.86% 20.93%
## 890: 21.89% 22.69% 21.10%
## 891: 21.89% 22.69% 21.10%
## 892: 21.89% 22.69% 21.10%
## 893: 21.80% 22.69% 20.93%
## 894: 21.80% 22.69% 20.93%
## 895: 21.80% 22.69% 20.93%
## 896: 21.80% 22.69% 20.93%
## 897: 21.80% 22.69% 20.93%
## 898: 21.80% 22.69% 20.93%
## 899: 21.80% 22.69% 20.93%
## 900: 21.80% 22.69% 20.93%
## 901: 21.80% 22.69% 20.93%
## 902: 21.72% 22.69% 20.76%
## 903: 21.80% 22.69% 20.93%
## 904: 21.80% 22.69% 20.93%
## 905: 21.80% 22.69% 20.93%
## 906: 21.80% 22.69% 20.93%
## 907: 21.89% 22.69% 21.10%
## 908: 21.89% 22.69% 21.10%
## 909: 22.06% 22.86% 21.26%
## 910: 22.06% 22.86% 21.26%
## 911: 22.06% 22.86% 21.26%
## 912: 22.06% 22.86% 21.26%
## 913: 22.14% 23.03% 21.26%
## 914: 22.14% 23.03% 21.26%
## 915: 21.97% 22.86% 21.10%
## 916: 21.97% 22.86% 21.10%
## 917: 21.80% 22.86% 20.76%
## 918: 21.80% 22.86% 20.76%
## 919: 21.89% 23.03% 20.76%
## 920: 21.80% 22.86% 20.76%
## 921: 21.80% 22.86% 20.76%
## 922: 21.97% 23.03% 20.93%
## 923: 21.97% 23.03% 20.93%
## 924: 21.72% 22.69% 20.76%
## 925: 21.72% 22.69% 20.76%
## 926: 21.89% 22.86% 20.93%
## 927: 21.80% 22.86% 20.76%
## 928: 21.80% 22.86% 20.76%
## 929: 21.80% 22.86% 20.76%
## 930: 21.89% 22.86% 20.93%
## 931: 21.80% 22.69% 20.93%
## 932: 21.80% 22.69% 20.93%
## 933: 21.72% 22.52% 20.93%
## 934: 21.80% 22.69% 20.93%
## 935: 21.80% 22.69% 20.93%
## 936: 21.89% 22.69% 21.10%
## 937: 21.80% 22.69% 20.93%
## 938: 21.80% 22.69% 20.93%
## 939: 21.72% 22.69% 20.76%
## 940: 21.80% 22.69% 20.93%
## 941: 21.80% 22.69% 20.93%
## 942: 21.80% 22.69% 20.93%
## 943: 21.89% 22.69% 21.10%
## 944: 21.80% 22.69% 20.93%
## 945: 21.80% 22.69% 20.93%
## 946: 21.80% 22.52% 21.10%
## 947: 21.89% 22.52% 21.26%
## 948: 21.97% 22.52% 21.43%
## 949: 22.14% 22.69% 21.59%
## 950: 22.14% 22.69% 21.59%
## 951: 22.22% 22.86% 21.59%
## 952: 22.22% 22.86% 21.59%
## 953: 22.14% 22.86% 21.43%
## 954: 22.14% 22.86% 21.43%
## 955: 22.14% 22.86% 21.43%
## 956: 22.14% 22.86% 21.43%
## 957: 22.14% 22.86% 21.43%
## 958: 22.22% 23.03% 21.43%
## 959: 22.14% 22.86% 21.43%
## 960: 22.22% 22.86% 21.59%
## 961: 22.22% 22.86% 21.59%
## 962: 22.31% 23.03% 21.59%
## 963: 22.22% 22.86% 21.59%
## 964: 22.22% 22.86% 21.59%
## 965: 22.31% 23.03% 21.59%
## 966: 22.31% 23.19% 21.43%
## 967: 22.22% 23.03% 21.43%
## 968: 22.31% 23.19% 21.43%
## 969: 22.14% 22.86% 21.43%
## 970: 22.14% 22.69% 21.59%
## 971: 22.14% 22.86% 21.43%
## 972: 22.14% 22.86% 21.43%
## 973: 22.22% 22.69% 21.76%
## 974: 22.14% 22.52% 21.76%
## 975: 22.22% 22.69% 21.76%
## 976: 22.31% 22.86% 21.76%
## 977: 22.22% 22.69% 21.76%
## 978: 22.22% 22.69% 21.76%
## 979: 22.22% 22.69% 21.76%
## 980: 22.22% 22.69% 21.76%
## 981: 22.31% 22.86% 21.76%
## 982: 22.31% 22.86% 21.76%
## 983: 22.22% 22.69% 21.76%
## 984: 22.31% 22.86% 21.76%
## 985: 22.31% 22.86% 21.76%
## 986: 22.22% 22.86% 21.59%
## 987: 22.31% 22.86% 21.76%
## 988: 22.22% 22.86% 21.59%
## 989: 22.31% 23.03% 21.59%
## 990: 22.31% 23.03% 21.59%
## 991: 22.31% 23.03% 21.59%
## 992: 22.31% 22.86% 21.76%
## 993: 22.31% 22.86% 21.76%
## 994: 22.22% 22.86% 21.59%
## 995: 22.39% 22.86% 21.93%
## 996: 22.39% 22.86% 21.93%
## 997: 22.39% 22.86% 21.93%
## 998: 22.31% 22.86% 21.76%
## 999: 22.39% 22.86% 21.93%
## 1000: 22.39% 22.86% 21.93%
## 1001: 22.39% 22.86% 21.93%
## 1002: 22.47% 23.03% 21.93%
## 1003: 22.47% 22.86% 22.09%
## 1004: 22.47% 22.86% 22.09%
## 1005: 22.56% 23.03% 22.09%
## 1006: 22.56% 23.03% 22.09%
## 1007: 22.56% 23.03% 22.09%
## 1008: 22.56% 23.03% 22.09%
## 1009: 22.64% 23.03% 22.26%
## 1010: 22.64% 23.03% 22.26%
## 1011: 22.72% 23.19% 22.26%
## 1012: 22.56% 22.86% 22.26%
## 1013: 22.56% 22.86% 22.26%
## 1014: 22.56% 22.86% 22.26%
## 1015: 22.64% 22.86% 22.43%
## 1016: 22.81% 23.19% 22.43%
## 1017: 22.64% 23.03% 22.26%
## 1018: 22.72% 23.19% 22.26%
## 1019: 22.56% 22.86% 22.26%
## 1020: 22.56% 23.03% 22.09%
## 1021: 22.47% 22.86% 22.09%
## 1022: 22.56% 23.03% 22.09%
## 1023: 22.56% 23.03% 22.09%
## 1024: 22.56% 23.03% 22.09%
## 1025: 22.56% 23.03% 22.09%
## 1026: 22.47% 22.86% 22.09%
## 1027: 22.47% 22.86% 22.09%
## 1028: 22.47% 22.86% 22.09%
## 1029: 22.47% 22.86% 22.09%
## 1030: 22.47% 22.86% 22.09%
## 1031: 22.47% 22.86% 22.09%
## 1032: 22.47% 22.86% 22.09%
## 1033: 22.47% 22.86% 22.09%
## 1034: 22.56% 23.03% 22.09%
## 1035: 22.47% 22.86% 22.09%
## 1036: 22.47% 22.86% 22.09%
## 1037: 22.56% 23.03% 22.09%
## 1038: 22.64% 23.03% 22.26%
## 1039: 22.64% 23.03% 22.26%
## 1040: 22.72% 23.03% 22.43%
## 1041: 22.81% 23.03% 22.59%
## 1042: 22.81% 23.03% 22.59%
## 1043: 22.81% 23.03% 22.59%
## 1044: 22.64% 22.86% 22.43%
## 1045: 22.72% 23.03% 22.43%
## 1046: 22.64% 22.86% 22.43%
## 1047: 22.64% 23.03% 22.26%
## 1048: 22.64% 23.03% 22.26%
## 1049: 22.56% 22.86% 22.26%
## 1050: 22.47% 22.86% 22.09%
## 1051: 22.47% 22.86% 22.09%
## 1052: 22.47% 22.86% 22.09%
## 1053: 22.47% 22.86% 22.09%
## 1054: 22.47% 23.03% 21.93%
## 1055: 22.47% 23.03% 21.93%
## 1056: 22.47% 23.03% 21.93%
## 1057: 22.56% 23.03% 22.09%
## 1058: 22.56% 23.03% 22.09%
## 1059: 22.39% 22.86% 21.93%
## 1060: 22.39% 22.86% 21.93%
## 1061: 22.39% 22.86% 21.93%
## 1062: 22.47% 22.86% 22.09%
## 1063: 22.56% 22.86% 22.26%
## 1064: 22.47% 22.86% 22.09%
## 1065: 22.64% 23.03% 22.26%
## 1066: 22.64% 23.03% 22.26%
## 1067: 22.47% 22.86% 22.09%
## 1068: 22.47% 22.86% 22.09%
## 1069: 22.39% 22.86% 21.93%
## 1070: 22.39% 23.03% 21.76%
## 1071: 22.39% 23.03% 21.76%
## 1072: 22.31% 22.86% 21.76%
## 1073: 22.31% 22.86% 21.76%
## 1074: 22.31% 22.86% 21.76%
## 1075: 22.39% 22.86% 21.93%
## 1076: 22.31% 22.86% 21.76%
## 1077: 22.31% 22.86% 21.76%
## 1078: 22.31% 22.86% 21.76%
## 1079: 22.31% 22.86% 21.76%
## 1080: 22.31% 22.86% 21.76%
## 1081: 22.14% 22.86% 21.43%
## 1082: 22.22% 22.86% 21.59%
## 1083: 22.22% 22.86% 21.59%
## 1084: 22.14% 22.86% 21.43%
## 1085: 22.14% 22.86% 21.43%
## 1086: 22.14% 22.86% 21.43%
## 1087: 22.14% 22.86% 21.43%
## 1088: 22.14% 22.86% 21.43%
## 1089: 22.06% 22.86% 21.26%
## 1090: 22.14% 22.86% 21.43%
## 1091: 22.14% 22.86% 21.43%
## 1092: 22.06% 22.86% 21.26%
## 1093: 22.14% 22.86% 21.43%
## 1094: 22.06% 22.86% 21.26%
## 1095: 22.06% 22.86% 21.26%
## 1096: 22.14% 22.86% 21.43%
## 1097: 22.14% 22.86% 21.43%
## 1098: 22.14% 22.86% 21.43%
## 1099: 22.14% 22.86% 21.43%
## 1100: 22.22% 22.86% 21.59%
## 1101: 22.22% 22.86% 21.59%
## 1102: 22.22% 22.86% 21.59%
## 1103: 22.22% 22.86% 21.59%
## 1104: 22.22% 22.86% 21.59%
## 1105: 22.22% 22.86% 21.59%
## 1106: 22.22% 22.86% 21.59%
## 1107: 22.22% 22.86% 21.59%
## 1108: 22.31% 23.03% 21.59%
## 1109: 22.31% 23.03% 21.59%
## 1110: 22.31% 23.03% 21.59%
## 1111: 22.39% 23.19% 21.59%
## 1112: 22.31% 23.03% 21.59%
## 1113: 22.31% 23.03% 21.59%
## 1114: 22.22% 22.86% 21.59%
## 1115: 22.22% 22.86% 21.59%
## 1116: 22.22% 22.86% 21.59%
## 1117: 22.22% 22.86% 21.59%
## 1118: 22.22% 22.86% 21.59%
## 1119: 22.22% 22.86% 21.59%
## 1120: 22.22% 22.86% 21.59%
## 1121: 22.22% 22.86% 21.59%
## 1122: 22.22% 22.86% 21.59%
## 1123: 22.22% 22.86% 21.59%
## 1124: 22.22% 22.86% 21.59%
## 1125: 22.22% 22.86% 21.59%
## 1126: 22.22% 22.86% 21.59%
## 1127: 22.22% 22.86% 21.59%
## 1128: 22.22% 22.86% 21.59%
## 1129: 22.22% 22.86% 21.59%
## 1130: 22.22% 22.86% 21.59%
## 1131: 22.22% 22.86% 21.59%
## 1132: 22.22% 22.86% 21.59%
## 1133: 22.22% 22.86% 21.59%
## 1134: 22.22% 22.86% 21.59%
## 1135: 22.22% 22.86% 21.59%
## 1136: 22.22% 22.86% 21.59%
## 1137: 22.22% 22.86% 21.59%
## 1138: 22.22% 22.86% 21.59%
## 1139: 22.22% 22.86% 21.59%
## 1140: 22.22% 22.86% 21.59%
## 1141: 22.31% 22.86% 21.76%
## 1142: 22.39% 22.86% 21.93%
## 1143: 22.39% 22.86% 21.93%
## 1144: 22.47% 23.03% 21.93%
## 1145: 22.39% 23.03% 21.76%
## 1146: 22.39% 23.03% 21.76%
## 1147: 22.39% 23.03% 21.76%
## 1148: 22.39% 23.03% 21.76%
## 1149: 22.39% 23.03% 21.76%
## 1150: 22.39% 23.03% 21.76%
## 1151: 22.39% 23.03% 21.76%
## 1152: 22.22% 23.03% 21.43%
## 1153: 22.47% 23.03% 21.93%
## 1154: 22.47% 23.19% 21.76%
## 1155: 22.47% 23.19% 21.76%
## 1156: 22.56% 23.19% 21.93%
## 1157: 22.56% 23.19% 21.93%
## 1158: 22.64% 23.19% 22.09%
## 1159: 22.64% 23.19% 22.09%
## 1160: 22.64% 23.19% 22.09%
## 1161: 22.64% 23.19% 22.09%
## 1162: 22.64% 23.19% 22.09%
## 1163: 22.64% 23.19% 22.09%
## 1164: 22.47% 23.03% 21.93%
## 1165: 22.39% 23.03% 21.76%
## 1166: 22.47% 23.03% 21.93%
## 1167: 22.47% 23.03% 21.93%
## 1168: 22.47% 23.03% 21.93%
## 1169: 22.56% 23.03% 22.09%
## 1170: 22.64% 23.03% 22.26%
## 1171: 22.56% 23.03% 22.09%
## 1172: 22.56% 23.03% 22.09%
## 1173: 22.56% 23.03% 22.09%
## 1174: 22.56% 23.03% 22.09%
## 1175: 22.56% 23.03% 22.09%
## 1176: 22.47% 23.03% 21.93%
## 1177: 22.47% 23.03% 21.93%
## 1178: 22.47% 23.03% 21.93%
## 1179: 22.47% 23.03% 21.93%
## 1180: 22.47% 23.03% 21.93%
## 1181: 22.47% 23.19% 21.76%
## 1182: 22.56% 23.19% 21.93%
## 1183: 22.56% 23.19% 21.93%
## 1184: 22.56% 23.19% 21.93%
## 1185: 22.56% 23.19% 21.93%
## 1186: 22.39% 23.19% 21.59%
## 1187: 22.56% 23.19% 21.93%
## 1188: 22.56% 23.19% 21.93%
## 1189: 22.56% 23.19% 21.93%
## 1190: 22.47% 23.19% 21.76%
## 1191: 22.47% 23.19% 21.76%
## 1192: 22.47% 23.19% 21.76%
## 1193: 22.47% 23.19% 21.76%
## 1194: 22.47% 23.19% 21.76%
## 1195: 22.64% 23.19% 22.09%
## 1196: 22.56% 23.03% 22.09%
## 1197: 22.64% 23.19% 22.09%
## 1198: 22.47% 23.19% 21.76%
## 1199: 22.39% 23.03% 21.76%
## 1200: 22.39% 23.03% 21.76%
## 1201: 22.47% 23.19% 21.76%
## 1202: 22.39% 23.03% 21.76%
## 1203: 22.47% 23.19% 21.76%
## 1204: 22.47% 23.19% 21.76%
## 1205: 22.56% 23.19% 21.93%
## 1206: 22.56% 23.19% 21.93%
## 1207: 22.56% 23.19% 21.93%
## 1208: 22.56% 23.19% 21.93%
## 1209: 22.56% 23.19% 21.93%
## 1210: 22.64% 23.19% 22.09%
## 1211: 22.64% 23.19% 22.09%
## 1212: 22.64% 23.19% 22.09%
## 1213: 22.64% 23.19% 22.09%
## 1214: 22.64% 23.19% 22.09%
## 1215: 22.64% 23.19% 22.09%
## 1216: 22.64% 23.19% 22.09%
## 1217: 22.64% 23.19% 22.09%
## 1218: 22.64% 23.19% 22.09%
## 1219: 22.64% 23.36% 21.93%
## 1220: 22.56% 23.19% 21.93%
## 1221: 22.56% 23.19% 21.93%
## 1222: 22.56% 23.19% 21.93%
## 1223: 22.56% 23.19% 21.93%
## 1224: 22.56% 23.19% 21.93%
## 1225: 22.56% 23.19% 21.93%
## 1226: 22.56% 23.19% 21.93%
## 1227: 22.56% 23.19% 21.93%
## 1228: 22.56% 23.19% 21.93%
## 1229: 22.64% 23.36% 21.93%
## 1230: 22.64% 23.36% 21.93%
## 1231: 22.64% 23.36% 21.93%
## 1232: 22.56% 23.19% 21.93%
## 1233: 22.56% 23.19% 21.93%
## 1234: 22.56% 23.19% 21.93%
## 1235: 22.56% 23.19% 21.93%
## 1236: 22.56% 23.19% 21.93%
## 1237: 22.56% 23.19% 21.93%
## 1238: 22.56% 23.19% 21.93%
## 1239: 22.56% 23.19% 21.93%
## 1240: 22.47% 23.19% 21.76%
## 1241: 22.47% 23.19% 21.76%
## 1242: 22.56% 23.19% 21.93%
## 1243: 22.64% 23.36% 21.93%
## 1244: 22.47% 23.19% 21.76%
## 1245: 22.47% 23.19% 21.76%
## 1246: 22.39% 23.03% 21.76%
## 1247: 22.39% 23.03% 21.76%
## 1248: 22.47% 23.19% 21.76%
## 1249: 22.47% 23.19% 21.76%
## 1250: 22.39% 23.03% 21.76%
## 1251: 22.56% 23.36% 21.76%
## 1252: 22.56% 23.36% 21.76%
## 1253: 22.47% 23.19% 21.76%
## 1254: 22.56% 23.36% 21.76%
## 1255: 22.56% 23.36% 21.76%
## 1256: 22.56% 23.36% 21.76%
## 1257: 22.56% 23.36% 21.76%
## 1258: 22.56% 23.36% 21.76%
## 1259: 22.39% 23.03% 21.76%
## 1260: 22.56% 23.19% 21.93%
## 1261: 22.56% 23.19% 21.93%
## 1262: 22.39% 22.86% 21.93%
## 1263: 22.56% 23.19% 21.93%
## 1264: 22.56% 23.19% 21.93%
## 1265: 22.56% 23.19% 21.93%
## 1266: 22.72% 23.36% 22.09%
## 1267: 22.64% 23.19% 22.09%
## 1268: 22.56% 23.19% 21.93%
## 1269: 22.47% 23.19% 21.76%
## 1270: 22.39% 23.03% 21.76%
## 1271: 22.39% 23.03% 21.76%
## 1272: 22.39% 23.03% 21.76%
## 1273: 22.39% 23.03% 21.76%
## 1274: 22.39% 23.03% 21.76%
## 1275: 22.39% 23.03% 21.76%
## 1276: 22.39% 23.03% 21.76%
## 1277: 22.39% 23.03% 21.76%
## 1278: 22.39% 23.03% 21.76%
## 1279: 22.39% 23.03% 21.76%
## 1280: 22.39% 23.03% 21.76%
## 1281: 22.39% 23.03% 21.76%
## 1282: 22.31% 23.03% 21.59%
## 1283: 22.31% 23.03% 21.59%
## 1284: 22.31% 23.03% 21.59%
## 1285: 22.31% 23.03% 21.59%
## 1286: 22.39% 23.03% 21.76%
## 1287: 22.31% 23.03% 21.59%
## 1288: 22.22% 23.03% 21.43%
## 1289: 22.14% 22.86% 21.43%
## 1290: 22.14% 22.86% 21.43%
## 1291: 22.06% 22.86% 21.26%
## 1292: 22.06% 22.86% 21.26%
## 1293: 21.97% 22.69% 21.26%
## 1294: 21.89% 22.69% 21.10%
## 1295: 21.89% 22.69% 21.10%
## 1296: 21.89% 22.69% 21.10%
## 1297: 21.80% 22.52% 21.10%
## 1298: 21.89% 22.69% 21.10%
## 1299: 21.97% 22.86% 21.10%
## 1300: 21.97% 22.86% 21.10%
## 1301: 22.06% 22.86% 21.26%
## 1302: 22.06% 22.86% 21.26%
## 1303: 22.06% 22.86% 21.26%
## 1304: 21.97% 22.69% 21.26%
## 1305: 21.97% 22.69% 21.26%
## 1306: 21.97% 22.69% 21.26%
## 1307: 21.97% 22.69% 21.26%
## 1308: 22.06% 22.86% 21.26%
## 1309: 22.06% 22.86% 21.26%
## 1310: 22.06% 22.86% 21.26%
## 1311: 22.06% 22.86% 21.26%
## 1312: 22.06% 22.86% 21.26%
## 1313: 22.14% 22.86% 21.43%
## 1314: 22.14% 22.86% 21.43%
## 1315: 22.14% 22.86% 21.43%
## 1316: 21.97% 22.86% 21.10%
## 1317: 21.97% 22.86% 21.10%
## 1318: 22.06% 23.03% 21.10%
## 1319: 22.06% 23.03% 21.10%
## 1320: 22.06% 23.03% 21.10%
## 1321: 21.97% 22.86% 21.10%
## 1322: 22.06% 22.86% 21.26%
## 1323: 22.06% 22.86% 21.26%
## 1324: 22.06% 22.86% 21.26%
## 1325: 21.97% 22.69% 21.26%
## 1326: 22.06% 22.86% 21.26%
## 1327: 22.06% 22.86% 21.26%
## 1328: 22.06% 22.86% 21.26%
## 1329: 22.22% 22.86% 21.59%
## 1330: 22.06% 22.86% 21.26%
## 1331: 22.06% 22.86% 21.26%
## 1332: 22.06% 22.86% 21.26%
## 1333: 21.97% 22.69% 21.26%
## 1334: 21.97% 22.69% 21.26%
## 1335: 21.97% 22.69% 21.26%
## 1336: 22.06% 22.69% 21.43%
## 1337: 22.06% 22.69% 21.43%
## 1338: 22.06% 22.69% 21.43%
## 1339: 22.06% 22.69% 21.43%
## 1340: 22.14% 22.69% 21.59%
## 1341: 21.97% 22.69% 21.26%
## 1342: 21.97% 22.69% 21.26%
## 1343: 22.06% 22.69% 21.43%
## 1344: 22.14% 22.69% 21.59%
## 1345: 22.14% 22.69% 21.59%
## 1346: 22.22% 22.69% 21.76%
## 1347: 22.22% 22.69% 21.76%
## 1348: 22.14% 22.69% 21.59%
## 1349: 22.14% 22.69% 21.59%
## 1350: 22.14% 22.69% 21.59%
## 1351: 22.14% 22.69% 21.59%
## 1352: 22.14% 22.69% 21.59%
## 1353: 22.14% 22.69% 21.59%
## 1354: 22.22% 22.86% 21.59%
## 1355: 22.31% 23.03% 21.59%
## 1356: 22.31% 23.03% 21.59%
## 1357: 22.22% 23.03% 21.43%
## 1358: 22.22% 23.03% 21.43%
## 1359: 22.31% 23.03% 21.59%
## 1360: 22.22% 23.03% 21.43%
## 1361: 22.22% 23.03% 21.43%
## 1362: 22.14% 22.86% 21.43%
## 1363: 22.14% 22.86% 21.43%
## 1364: 22.14% 22.86% 21.43%
## 1365: 22.22% 23.03% 21.43%
## 1366: 22.31% 23.19% 21.43%
## 1367: 22.39% 23.19% 21.59%
## 1368: 22.31% 23.19% 21.43%
## 1369: 22.31% 23.19% 21.43%
## 1370: 22.31% 23.19% 21.43%
## 1371: 22.22% 23.03% 21.43%
## 1372: 22.22% 23.03% 21.43%
## 1373: 22.22% 23.03% 21.43%
## 1374: 22.22% 23.03% 21.43%
## 1375: 22.22% 23.03% 21.43%
## 1376: 22.22% 23.03% 21.43%
## 1377: 22.22% 23.03% 21.43%
## 1378: 22.22% 23.03% 21.43%
## 1379: 22.22% 23.03% 21.43%
## 1380: 22.22% 23.03% 21.43%
## 1381: 22.22% 23.03% 21.43%
## 1382: 22.22% 23.03% 21.43%
## 1383: 22.22% 23.03% 21.43%
## 1384: 22.22% 23.03% 21.43%
## 1385: 22.31% 23.03% 21.59%
## 1386: 22.22% 22.86% 21.59%
## 1387: 22.31% 23.03% 21.59%
## 1388: 22.31% 23.03% 21.59%
## 1389: 22.31% 23.03% 21.59%
## 1390: 22.31% 23.03% 21.59%
## 1391: 22.31% 23.03% 21.59%
## 1392: 22.22% 22.86% 21.59%
## 1393: 22.22% 22.86% 21.59%
## 1394: 22.22% 22.86% 21.59%
## 1395: 22.14% 22.86% 21.43%
## 1396: 22.14% 22.86% 21.43%
## 1397: 22.14% 22.86% 21.43%
## 1398: 22.14% 22.86% 21.43%
## 1399: 22.14% 22.86% 21.43%
## 1400: 22.14% 22.86% 21.43%
## 1401: 22.22% 23.03% 21.43%
## 1402: 22.22% 23.03% 21.43%
## 1403: 22.31% 23.19% 21.43%
## 1404: 22.31% 23.19% 21.43%
## 1405: 22.14% 23.03% 21.26%
## 1406: 22.14% 23.03% 21.26%
## 1407: 22.14% 23.03% 21.26%
## 1408: 22.22% 23.03% 21.43%
## 1409: 22.22% 23.03% 21.43%
## 1410: 22.39% 23.19% 21.59%
## 1411: 22.39% 23.19% 21.59%
## 1412: 22.39% 23.19% 21.59%
## 1413: 22.47% 23.19% 21.76%
## 1414: 22.39% 23.19% 21.59%
## 1415: 22.31% 23.03% 21.59%
## 1416: 22.31% 23.03% 21.59%
## 1417: 22.22% 22.86% 21.59%
## 1418: 22.06% 22.86% 21.26%
## 1419: 22.31% 22.86% 21.76%
## 1420: 22.31% 22.86% 21.76%
## 1421: 22.22% 22.86% 21.59%
## 1422: 22.14% 22.86% 21.43%
## 1423: 22.14% 22.86% 21.43%
## 1424: 22.14% 22.86% 21.43%
## 1425: 22.22% 23.03% 21.43%
## 1426: 22.14% 22.86% 21.43%
## 1427: 22.14% 22.86% 21.43%
## 1428: 22.22% 22.86% 21.59%
## 1429: 22.22% 22.86% 21.59%
## 1430: 22.22% 22.86% 21.59%
## 1431: 22.22% 22.86% 21.59%
## 1432: 22.06% 22.69% 21.43%
## 1433: 22.06% 22.86% 21.26%
## 1434: 22.06% 22.86% 21.26%
## 1435: 22.06% 22.86% 21.26%
## 1436: 22.06% 22.86% 21.26%
## 1437: 22.06% 22.86% 21.26%
## 1438: 22.06% 22.86% 21.26%
## 1439: 22.06% 22.86% 21.26%
## 1440: 22.14% 22.86% 21.43%
## 1441: 22.14% 22.86% 21.43%
## 1442: 22.14% 22.86% 21.43%
## 1443: 22.14% 22.86% 21.43%
## 1444: 22.14% 22.86% 21.43%
## 1445: 22.22% 22.86% 21.59%
## 1446: 22.14% 22.86% 21.43%
## 1447: 22.14% 22.86% 21.43%
## 1448: 22.14% 22.86% 21.43%
## 1449: 22.06% 22.86% 21.26%
## 1450: 22.14% 23.03% 21.26%
## 1451: 22.06% 22.86% 21.26%
## 1452: 22.06% 22.86% 21.26%
## 1453: 22.06% 22.86% 21.26%
## 1454: 22.06% 22.86% 21.26%
## 1455: 22.06% 22.86% 21.26%
## 1456: 22.06% 22.86% 21.26%
## 1457: 22.06% 22.86% 21.26%
## 1458: 22.06% 22.86% 21.26%
## 1459: 22.06% 22.86% 21.26%
## 1460: 22.06% 22.86% 21.26%
## 1461: 21.97% 22.69% 21.26%
## 1462: 22.06% 23.03% 21.10%
## 1463: 22.06% 23.03% 21.10%
## 1464: 22.06% 23.03% 21.10%
## 1465: 22.06% 23.03% 21.10%
## 1466: 22.06% 23.03% 21.10%
## 1467: 22.06% 23.03% 21.10%
## 1468: 22.06% 22.86% 21.26%
## 1469: 21.97% 22.86% 21.10%
## 1470: 22.06% 22.86% 21.26%
## 1471: 22.06% 22.86% 21.26%
## 1472: 22.06% 22.86% 21.26%
## 1473: 22.06% 22.86% 21.26%
## 1474: 21.97% 22.86% 21.10%
## 1475: 21.97% 22.86% 21.10%
## 1476: 22.06% 23.03% 21.10%
## 1477: 22.06% 23.03% 21.10%
## 1478: 22.14% 23.03% 21.26%
## 1479: 22.22% 23.03% 21.43%
## 1480: 22.14% 23.03% 21.26%
## 1481: 22.22% 23.03% 21.43%
## 1482: 22.31% 23.03% 21.59%
## 1483: 22.22% 23.03% 21.43%
## 1484: 22.22% 22.86% 21.59%
## 1485: 22.31% 23.03% 21.59%
## 1486: 22.39% 23.03% 21.76%
## 1487: 22.39% 23.03% 21.76%
## 1488: 22.39% 23.03% 21.76%
## 1489: 22.31% 23.03% 21.59%
## 1490: 22.31% 22.86% 21.76%
## 1491: 22.31% 23.03% 21.59%
## 1492: 22.22% 23.03% 21.43%
## 1493: 22.14% 23.03% 21.26%
## 1494: 22.06% 23.03% 21.10%
## 1495: 21.89% 22.86% 20.93%
## 1496: 21.89% 22.86% 20.93%
## 1497: 21.97% 22.86% 21.10%
## 1498: 21.97% 22.86% 21.10%
## 1499: 21.97% 22.86% 21.10%
## 1500: 21.97% 22.86% 21.10%
#MDSplot(rf,fac = 'viability')
#round(importance(rf), 2)
plot(rf)
print(rf)
##
## Call:
## randomForest(formula = viability ~ ., data = droplevels(train), importance = TRUE, proximity = TRUE, cutoff = c(th, 1 - th), ntree = 1500, mtry = 10, localImp = TRUE, norm.votes = TRUE, do.trac = 1, na.action = na.omit)
## Type of random forest: classification
## Number of trees: 1500
## No. of variables tried at each split: 10
##
## OOB estimate of error rate: 21.97%
## Confusion matrix:
## Lethal Viable class.error
## Lethal 459 136 0.2285714
## Viable 127 475 0.2109635
##########
########## Validate on the Test set
prTest = predict(rf, newdata = test[, -1],type = 'prob')
#pr2T = prTest$predicted[, 1]
pr2T = prTest[, 1]
pr2T [pr2T > th] = 'Lethal'
pr2T [pr2T <= th] = 'Viable'
prop.table(table(test$viability, pr2T), 2)
## pr2T
## Lethal Viable
## Lethal 0.7819026 0.1793478
## Viable 0.2180974 0.8206522
##### Prediction on the entire data
pr = predict(rf, newdata = f000[, -1],type = 'prob')
#pr2 = pr$predicted[, 1]
pr2 = pr[, 1]
pr2 [pr2 > th] = 'Lethal'
pr2 [pr2 <= th] = 'Viable'
f21 = cbind('MGI id' = rownames(f000), f000, pred = pr2)
prop.table(table(f21$viability, f21$pred),margin = 1)
##
## Lethal Viable
## unknown 0.41597950 0.58402050
## Lethal 0.93386774 0.06613226
## Viable 0.15193798 0.84806202
########
# ROC plot ...
roc(rf$y,rf$votes[,1],smooth = TRUE,percent = TRUE,auc = TRUE,ci = TRUE,plot = TRUE)
## Setting levels: control = Lethal, case = Viable
## Setting direction: controls > cases
##
## Call:
## roc.default(response = rf$y, predictor = rf$votes[, 1], percent = TRUE, smooth = TRUE, auc = TRUE, ci = TRUE, plot = TRUE)
##
## Data: rf$votes[, 1] in 595 controls (rf$y Lethal) > 602 cases (rf$y Viable).
## Smoothing: binormal
## Area under the curve: 87.03%
## 95% CI: 84.65%-88.82% (2000 stratified bootstrap replicates)
plot(rf$votes, col = rf$y, pch = as.integer(rf$y) + 1)
abline(
v = th,
h = 1 - th,
lwd = 3,
lty = 3,
col = 3
)
f01 = function(x, y, ...) {
lines(x, y, lwd = 3)
th = th
abline(
v = th,
col = 2,
lty = 3,
lwd = 3
)
}
f01 = function(x, y, ...) {
lines(x, y, lwd = 3)
th = th
abline(
v = th,
col = 2,
lty = 3,
lwd = 3
)
}
# plot(
# calc_roc.rfsrc(
# rf,
# yvar = rf$yvar,
# which.outcome = 1,
# oob = TRUE
# ),
# lower.panel = NULL,
# upper.panel = f01,
# labels = c('Sensitivity', 'Specificity', 'Pct')
# )
#plot(gg_roc(rf, which.outcome = 1, oob = TRUE))
# 10.7 predict and evaluate using DR8
mergedTwo = merge(f21,
viabi_DR8,
by = 'MGI id',
all.y = TRUE,
all.x = FALSE)
# 10.7.1
library(caret)
## Loading required package: lattice
## Loading required package: ggplot2
##
## Attaching package: 'ggplot2'
## The following object is masked from 'package:randomForest':
##
## margin
############# On the entire data
mergedTwo01 = droplevels(subset(mergedTwo, !(viability.dr8 %in% 'Subviable')))
t1 = table(
(mergedTwo01$pred),
(mergedTwo01$viability.dr8),
dnn = c('Predicted', 'From Dr8'),
useNA = 'no'
)
# 10.7.2
############# on the new data only
mergedTwo02 = droplevels(subset(
mergedTwo,
(!viability.dr8 %in% 'Subviable') &
(`viability` %in% 'unknown') &
!is.na(viability.dr8)
))
t2 = table(
trimws(mergedTwo02$pred),
trimws(mergedTwo02$viability.dr8)
,
dnn = c('Predicted', 'From Dr8')
)
# Performance on the entire DR 8
caret:::confusionMatrix(t1, mode = 'everything')
## Confusion Matrix and Statistics
##
## From Dr8
## Predicted Lethal Viable
## Lethal 804 296
## Viable 70 1546
##
## Accuracy : 0.8652
## 95% CI : (0.8518, 0.8779)
## No Information Rate : 0.6782
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.7109
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.9199
## Specificity : 0.8393
## Pos Pred Value : 0.7309
## Neg Pred Value : 0.9567
## Precision : 0.7309
## Recall : 0.9199
## F1 : 0.8146
## Prevalence : 0.3218
## Detection Rate : 0.2960
## Detection Prevalence : 0.4050
## Balanced Accuracy : 0.8796
##
## 'Positive' Class : Lethal
##
# Performance only on new genes
caret:::confusionMatrix(t2, mode = 'everything')
## Confusion Matrix and Statistics
##
## From Dr8
## Predicted Lethal Viable
## Lethal 24 18
## Viable 5 77
##
## Accuracy : 0.8145
## 95% CI : (0.7348, 0.8786)
## No Information Rate : 0.7661
## P-Value [Acc > NIR] : 0.12000
##
## Kappa : 0.5521
##
## Mcnemar's Test P-Value : 0.01234
##
## Sensitivity : 0.8276
## Specificity : 0.8105
## Pos Pred Value : 0.5714
## Neg Pred Value : 0.9390
## Precision : 0.5714
## Recall : 0.8276
## F1 : 0.6761
## Prevalence : 0.2339
## Detection Rate : 0.1935
## Detection Prevalence : 0.3387
## Balanced Accuracy : 0.8191
##
## 'Positive' Class : Lethal
##
############# Output data
output =
data.frame(
'MGI id' = rownames(f00),
enrichment.clust = mergOrg$out$clusters,
enrichment.merged = mergOrg$out$merged.clu,
enrichment.assigned.viability = mergOrg$out$assigned.viability,
threshold = th,
Viab.Inc.Conf.sviable = f00.with.conf$viability,
prediction = f21$pred,
# Lethal.probility = pr$predicted[, 1],
# Viable.probability = pr$predicted[, 2],
Lethal.probility = pr[, 1],
Viable.probability = pr[, 2],
dimensions = f2[, -1],
check.names = FALSE
)
# 11 store the outputs
write.csv(output,
file = paste('All data and predictions_', Sys.Date(), '.csv', sep = ''))
save.image(paste(Sys.Date() ,
'Total workspace.Rdata',
sep = '_'))
# Release memory and END.
gc()
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 2229962 119.1 4171340 222.8 4171340 222.8
## Vcells 29101647 222.1 183482727 1399.9 285693630 2179.7