Conclusion: longest RLS are highly modular (interaction among themselves). Can we use an interaction index to gauge its modularity?

rm(list=ls())
start = Sys.time()
debug = 0
library(igraph)

## 
## Attaching package: 'igraph'

## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum

## The following object is masked from 'package:base':
## 
##     union

pairs= read.csv("Data/yeast.pin.csv", colClasses = c("character", "character"))
if (debug > 9) {
  pairs = pairs[1E3:4E3,]
}

RLS data preprocessing

rls.klab = read.csv("Data/RLSset.sandbox/rls_BY4742YPD30C_20181221.csv")
rls.klab = rls.klab[,c("set_ORF", "set_lifespan_mean","set_vs_ref_RLS_ratio")]
names(rls.klab) = c("Gene", "RLS", "RLS.ratio")
rls.essen = read.csv("Data/RLSset.sandbox/RLS.essential.mean.csv")
rls.essen$RLS.ratio = 0;
names(rls.essen) = c("Gene", "RLS", "RLS.ratio")
rls.tb = rbind( rls.klab, rls.essen)
rls.tb$Gene = as.character(rls.tb$Gene)
paste("There are ", length(rls.essen[,1])/ length(rls.tb[,1]), "essential genes")

## [1] "There are  0.189597315436242 essential genes"

#summary(rls.tb$RLS.ratio)

Percentiles of RLS.ratio

myq.rls = quantile( rls.tb$RLS.ratio, 
              prob = c( 0.19, 0.3, 0.6, 0.9, 0.95, 0.96, 0.97, 0.98, 0.99, 0.995, 0.999, 1))
d.myq.rls = data.frame(myq.rls)
d.myq.rls$Q = rownames(d.myq.rls)
names(d.myq.rls) = c("RLS", "Q")

RLS2Q = function( x ){
  if ( max(d.myq.rls$RLS ) > x) {
    sub = d.myq.rls[ d.myq.rls$RLS >= x, ]
    return (  sub$Q[ sub$RLS == min(sub$RLS)] )
  } else {
    return("100%")
  }
}

RLS2Q(0)

## [1] "19%"

RLS2Q(0.2)

## [1] "30%"

RLS2Q(1.5)

## [1] "97%"

rls.tb$Q = unlist(lapply( rls.tb$RLS.ratio, RLS2Q))
g.rls.Q = rls.tb$Q
names(g.rls.Q) = rls.tb$Gene

F.obs

pairs$cat1 = as.character( g.rls.Q[pairs$geneA] )
pairs$cat2 = as.character( g.rls.Q[pairs$geneB] )
pairs$cat1[is.na(pairs$cat1)] = "NA"
pairs$cat2[is.na(pairs$cat2)] = "NA"

pairs[ pairs$geneA == "YAL002W", ]

##       geneA   geneB cat1 cat2
## 233 YAL002W YAL021C   NA   NA
## 234 YAL002W YBR245C   NA  60%
## 235 YAL002W YDL077C   NA  30%
## 236 YAL002W YDR495C   NA  60%
## 237 YAL002W YGL212W   NA  30%
## 238 YAL002W YGR218W   NA  19%
## 239 YAL002W YKL193C   NA  19%
## 240 YAL002W YKL203C   NA  19%
## 241 YAL002W YKR014C   NA  95%
## 242 YAL002W YKR026C   NA  60%
## 243 YAL002W YLR148W   NA  30%
## 244 YAL002W YLR396C   NA  60%
## 245 YAL002W YMR231W   NA  30%
## 246 YAL002W YNL093W   NA  95%
## 247 YAL002W YOR036W   NA  60%
## 248 YAL002W YOR089C   NA  90%
## 249 YAL002W YOR359W   NA  90%
## 250 YAL002W YPL045W   NA  30%

rls.tb[rls.tb$Gene=="YAL002W",] #visual check passed

## [1] Gene      RLS       RLS.ratio Q        
## <0 rows> (or 0-length row.names)

tags = t(apply(pairs[,c("cat1", "cat2")], 1, sort))
pairs$tag = paste( tags[,1], tags[,2], sep='_')
F.obs = data.frame( table(pairs$tag))
names(F.obs) = c("tag", "freq")
F.obs [1:10,]

##         tag  freq
## 1  100%_19%    41
## 2  100%_30%    18
## 3  100%_60%    30
## 4  100%_90%    26
## 5  100%_95%     7
## 6  100%_97%     1
## 7  100%_98%     3
## 8   100%_NA    14
## 9   19%_19% 11068
## 10  19%_30%  5456

#load MS02 null networks

ms02files = list.files(path='yeastMS02')
if (debug > 0 ) {ms02files = ms02files[1: 3] }
F.ms02 = data.frame(matrix(data=NA, nrow=1, ncol=3)) #null distributions
names(F.ms02) = c('tag', 'freq', 'file')

# file = "ms02.1.csv" #debug
for (file in ms02files ){
  if ( debug > 0 ) { print(file) }
  ms02_pairs= read.csv(paste("yeastMS02/", file, sep=''),
                       colClasses = c("character", "character"))
  ms02_pairs = ms02_pairs[,1:2]
  
  ms02_pairs$cat1 = as.character( g.rls.Q[ms02_pairs$id1] )
  ms02_pairs$cat2 = as.character( g.rls.Q[ms02_pairs$id2] )
  ms02_pairs$cat1[is.na(ms02_pairs$cat1)] = "NA"
  ms02_pairs$cat2[is.na(ms02_pairs$cat2)] = "NA" 
 
  tags2 = t(apply(ms02_pairs[,c("cat1", "cat2")], 1, sort))
  ms02_pairs$tag = paste( tags2[,1], tags2[,2], sep='_')
  F.ms02current = data.frame( table(ms02_pairs$tag))
  F.ms02current$file = file
  names(F.ms02current) = c('tag', 'freq', 'file')
  F.ms02 =  data.frame( rbind(F.ms02, data.frame(F.ms02current)) )
}
F.ms02 = F.ms02[ !is.na(F.ms02$tag), ]
summary(F.ms02)

##      tag                 freq           file          
##  Length:8309        Min.   :    1   Length:8309       
##  Class :character   1st Qu.:    7   Class :character  
##  Mode  :character   Median :   96   Mode  :character  
##                     Mean   : 1237                     
##                     3rd Qu.:  293                     
##                     Max.   :13621

allCombinationsOfTwoVectors = function (els1, els2 ) {
  tagbuffer = c();
  for (e1 in els1) {
    for (e2 in els2) {
         tmp = sort(c(e1, e2));
         current_tag = paste(tmp[1], tmp[2], sep="_")
         tagbuffer = c(tagbuffer, current_tag)
    }
  }
  return( tagbuffer)
}

Initialize the Z-score matrix

all_tags = unique( allCombinationsOfTwoVectors(d.myq.rls$Q, d.myq.rls$Q) )
Zs = data.frame(all_tags)
names(Zs) = c('tag')
Zs$freq = ifelse( all_tags %in% Zs$tag, F.obs$freq[ match( Zs$tag , F.obs$tag) ], 0)
Zs$freq[is.na(Zs$freq)] = 0; 
summary(Zs)

##         tag          freq         
##  100%_100%: 1   Min.   :    0.00  
##  100%_19% : 1   1st Qu.:    2.25  
##  100%_30% : 1   Median :   45.00  
##  100%_60% : 1   Mean   : 1078.13  
##  100%_90% : 1   3rd Qu.:  237.50  
##  100%_95% : 1   Max.   :11832.00  
##  (Other)  :72

calculate Z-score. This take a few minutes. Need be modified by multicore

for (i in 1 : length(Zs$tag)) {
#i = 2
  sub = F.ms02[ F.ms02$tag == Zs$tag[i], ]
  if ( length(sub[,1])> 0) {
    Zs$Z[i] = ( Zs$freq[i] - mean(sub$freq) ) / max(sd(sub$freq), 0.5)
    if(debug>1 ){
       print( paste( Zs$tag[i],"lenthg(sub[,1]):",length(sub[,1]), "mean:", mean(sub$freq), "sd:", sd(sub$freq) ))  
       sub
    } 
  } else {
    #Zs$Z[i] = ( Zs$freq[i] - 0 ) / 1E-10  #never observed in ms02 nulls?? what to do??
    Zs$Z[i] = 999  #never observed in ms02 nulls?? what to do??
    if(debug>0 ){
       print( paste( Zs$tag[i],"lenthg(sub[,1]):",length(sub[,1]), "mean:", mean(sub$freq), "sd:", sd(sub$freq) ))  
    } 
  }
}
summary(Zs$Z)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -16.4052  -1.5414  -0.3094  -0.1638   1.4189  21.1630

generate Z matrix

#unique categories
cats =  as.character( d.myq.rls$Q ) # do not sort this
Zmat = matrix(NA, ncol=length(cats), nrow=length(cats))
colnames(Zmat) = cats; 
rownames(Zmat) = rev(cats); 
for (i in 1:length(cats)){#row
  for (  j in 1:length(cats)) { #column
    tmp = sort(c(cats[i], rev(cats)[j]))
    mytag = paste(tmp[1], tmp[2], sep="_")
    if( mytag %in% Zs$tag) {
       Zmat[i,j] = Zs$Z[ Zs$tag == mytag ]
    } else {
       Zmat[i,j] = NA
    }
    if (debug >1 ) {
      print (paste(mytag, Zmat[i,j] ) )
    }
  }
}
Zmat

##              19%         30%        60%        90%         95%         96%
## 100%  -0.3374710 -2.98414372  2.3235392  2.0608708 -4.23851323 -1.90232853
## 99.9%  1.0230630  1.45716519 -0.7505994  0.9348178  0.02110673  0.78224205
## 99.5% -0.3329221 -0.44519845 -0.2438553 -1.0608464  2.43787700  0.70660598
## 99%   -1.0484267  3.42917241 -1.4274895  0.9409399  1.76150685  1.66871627
## 98%    1.3039715  0.28361270 -0.3089387 -0.9359136  1.60508218 -2.07010524
## 97%   -1.7682671  0.03234569 -1.3554500 -1.5043696 -0.27568083 -0.04170095
## 96%   -0.4962805 -0.78314504  2.4161518 -1.9884977 -0.31448341 -0.81163759
## 95%    2.9171375 -0.82933909 -1.5537491  0.3403722  0.65086870 -0.31448341
## 90%   -2.4000000 -0.91854343 -0.2736136 -1.7155966  0.34037218 -1.98849771
## 60%   -2.3334640 -1.88705683 -1.6129650 -0.2736136 -1.55374914  2.41615177
## 30%   -2.0000000 -2.16000000 -1.8870568 -0.9185434 -0.82933909 -0.78314504
## 19%   -2.0000000 -2.00000000 -2.3334640 -2.4000000  2.91713746 -0.49628053
##               97%        98%         99%        99.5%        99.9%
## 100%  -2.07716272 -4.2478001 -16.4051980 -14.37641566 -11.30265608
## 99.9%  1.22857918 -0.3098392   3.6972350   3.68534922   3.48007596
## 99.5% -0.02549391  1.9143634   8.1611295   3.70626606   3.68534922
## 99%    1.93606068  3.1689002   4.5016691   8.16112949   3.69723500
## 98%   -1.28504475 -0.2397023   3.1689002   1.91436341  -0.30983922
## 97%   -1.13304432 -1.2850447   1.9360607  -0.02549391   1.22857918
## 96%   -0.04170095 -2.0701052   1.6687163   0.70660598   0.78224205
## 95%   -0.27568083  1.6050822   1.7615068   2.43787700   0.02110673
## 90%   -1.50436956 -0.9359136   0.9409399  -1.06084640   0.93481777
## 60%   -1.35544999 -0.3089387  -1.4274895  -0.24385530  -0.75059938
## 30%    0.03234569  0.2836127   3.4291724  -0.44519845   1.45716519
## 19%   -1.76826710  1.3039715  -1.0484267  -0.33292209   1.02306298
##             100%
## 100%   21.163024
## 99.9% -11.302656
## 99.5% -14.376416
## 99%   -16.405198
## 98%    -4.247800
## 97%    -2.077163
## 96%    -1.902329
## 95%    -4.238513
## 90%     2.060871
## 60%     2.323539
## 30%    -2.984144
## 19%    -0.337471

#heatmap

Boundary = 5
for( i in 1:length(Zmat[1,])) {
 for( j in 1:length(Zmat[,1])){
   Zmat[i,j] =  ifelse( Zmat[i,j] > Boundary  , Boundary, Zmat[i,j] )
   Zmat[i,j] =  ifelse( Zmat[i,j] < -Boundary, -Boundary, Zmat[i,j] )
 }
}

library(gplots)

## 
## Attaching package: 'gplots'

## The following object is masked from 'package:stats':
## 
##     lowess

#colors = c(seq(min(Zmat),-10.1,length=100),seq(-9.9,9.9,length=100),seq(10.1,max(Zmat),length=100))
my_palette <- colorRampPalette(c("blue2", "white", "red2"))(n = 99)

heatmap.2( as.matrix(Zmat), col=my_palette, scale="none",
          # margins = c(5,4), key.title = NA, 
           ,key.xlab="Z-score", key.ylab=NA,
          dendrogram='none', Rowv=FALSE, Colv=FALSE,trace='none'
          )

end = Sys.time()
runtime = end - start
print(runtime)

## Time difference of 7.280587 mins

Open Notebook

Monday, September 20, 2021

ms02 randomness verification

Friday, December 21, 2018

rlsratio ~rlsratio, yeast PIN

yeast PIN RLS ratio ~ RLS ratio Z-score

H Qin

2018-12-21

RLS data preprocessing

Percentiles of RLS.ratio

F.obs

Initialize the Z-score matrix

calculate Z-score. This take a few minutes. Need be modified by multicore

generate Z matrix