Tuesday, October 31, 2017

equator network

http://www.equator-network.org/

writing tips


clarity

"I didn't have time to write a short letter, so I wrote a long one instead", Mark Twain.

mSystems

https://www.asm.org/index.php/journal-press-releases/93543-american-society-for-microbiology-to-launch-msystems-new-open-access-journal



Yewno



https://about.yewno.com/

Technology and products that extract meaning at the atomic level, to help you understand deeper

Intelligent Framework

The Yewno Biomedical Intelligent Framework is a true “next generation” research tool. It is the only comprehensive tool available today to model the behavior of complex bio-systems.  It uses Computational Linguistics, Neural Networks, Machine Learning, and Graph Theory to provide insight into the world of biomedical work.

With Yewno, you can explore the world’s hard or marginal evidence — from biological pathways and biochemical interactions to pharmacodynamics. With Yewno, you can take your research in directions never thought possible.

Wednesday, October 25, 2017

tissue specific gene regulation

How does these affect aging, controllability? Some tissue aging fast and some slow?

http://www.cell.com/cell-reports/abstract/S2211-1247%2817%2931418-3#.We-APbV2phM.facebook






Highlights

  • Regulatory network connections are more tissue specific than nodes (genes and transcription factors)
  • Tissue-specific function is not solely regulated by transcription factor expression
  • Tissue-specific genes assume bottleneck positions in their corresponding networks
  • Tissue specificity is driven by context-dependent, non-canonical regulatory paths

Tuesday, October 24, 2017

CellMap analysis


Q: Why do I have 'abnormal', 'essential', 'nonessential' genes? 
CELLMAP provids genetics interaction in pairwise format. The interactions are provided for strain_ids (alleles). The mapping of strain_id and ORF is in “strain_ids_and_single_mutant_fitness.csv” .
rm(list=ls());
set.seed(2017);
datapath = "~/data/Sce/CellMap/20170626/S1.pairwise/";
debug = 0;
list.files(path=datapath);
## [1] "SGA_DAmP.txt"                             
## [2] "SGA_ExE.txt"                              
## [3] "SGA_ExN_NxE.txt"                          
## [4] "SGA_NxN.txt"                              
## [5] "strain_ids_and_single_mutant_fitness.csv" 
## [6] "strain_ids_and_single_mutant_fitness.xlsx"
Load naming lookup tables
dic = read.csv(paste(datapath, "strain_ids_and_single_mutant_fitness.csv", sep=''))
Load essential and non-essential infor that H. Qin generated.
list.files(path="data");
## [1] "SummaryRegressionHetHom2015Oct12.csv"          
## [2] "SummaryRegressionHetHomFactorized2015Oct13.csv"
fit = read.csv("data/SummaryRegressionHetHomFactorized2015Oct13.csv")
Load pairwise interaction data
#Essential X Essential 
tb.ee = read.table(paste(datapath,"SGA_ExE.txt", sep=''), header=T, sep="\t");
summary(tb.ee);
##         Query.Strain.ID       Query.allele.name        Array.Strain.ID  
##  YNL308C_tsq2680:   792   mob2-11-supp1:  1419   YCR002C_tsa78 :  1090  
##  YEL019C_tsq533 :   788   kri1-5001    :   792   YCR002C_tsa79 :  1090  
##  YNL287W_tsq38  :   788   mms21-1      :   788   YAL041W_tsa410:  1088  
##  YCL059C_tsq1104:   785   sec21-1      :   788   YAR007C_tsa273:  1088  
##  YCL059C_tsq326 :   785   cdc10-1      :   785   YAL038W_tsa34 :  1087  
##  YCR002C_tsq1072:   785   cdc10-2      :   785   YAL041W_tsa412:  1086  
##  (Other)        :813847   (Other)      :813213   (Other)       :812041  
##  Array.allele.name Arraytype.Temp Genetic.interaction.score..ε.
##  cdc10-1:  1090    TSA26:815635   Min.   :-0.790500            
##  cdc10-2:  1090    TSA30:  2935   1st Qu.:-0.034800            
##  cdc24-2:  1088                   Median : 0.005500            
##  rfa1-m2:  1088                   Mean   :-0.008011            
##  cdc19-1:  1087                   3rd Qu.: 0.035700            
##  cdc24-3:  1086                   Max.   : 0.440300            
##  (Other):812041                                                
##     P.value        Query.single.mutant.fitness..SMF.   Array.SMF     
##  Min.   :0.00000   Min.   :0.13                      Min.   :0.1137  
##  1st Qu.:0.03872   1st Qu.:0.72                      1st Qu.:0.7570  
##  Median :0.19940   Median :0.85                      Median :0.8581  
##  Mean   :0.20933   Mean   :0.82                      Mean   :0.8326  
##  3rd Qu.:0.35770   3rd Qu.:0.93                      3rd Qu.:0.9359  
##  Max.   :1.00000   Max.   :1.14                      Max.   :1.0550  
##                    NA's   :109963                                    
##  Double.mutant.fitness Double.mutant.fitness.standard.deviation
##  Min.   :-0.1435       Min.   :0.00000                         
##  1st Qu.: 0.5666       1st Qu.:0.02290                         
##  Median : 0.7123       Median :0.03850                         
##  Mean   : 0.6912       Mean   :0.04786                         
##  3rd Qu.: 0.8370       3rd Qu.:0.06120                         
##  Max.   : 1.3817       Max.   :1.24310                         
## 
#EXN and NXE
tb.en = read.table(paste(datapath,"SGA_ExN_NxE.txt", sep=''), header=T, sep="\t");
summary(tb.en);
##         Query.Strain.ID     Query.allele.name          Array.Strain.ID   
##  YJL143W_tsq3031:   3657   prp21-ts  :   3657   YAR007C_tsa273 :   2212  
##  YJL203W_tsq401 :   3657   tim17-5001:   3657   YFR036W_tsa88  :   2212  
##  YIL021W_tsq1136:   3623   med6-ts   :   3646   YAL025C_tsa1066:   2211  
##  YJL072C_tsq2842:   3623   psf2-5001 :   3623   YAL038W_tsa34  :   2210  
##  YIL021W_tsq2727:   3615   rpb3-2    :   3623   YFL008W_tsa68  :   2210  
##  YJR064W_tsq2853:   3585   rpb3-5001 :   3615   YFL009W_tsa334 :   2210  
##  (Other)        :3687193   (Other)   :3687132   (Other)        :3695688  
##   Array.allele.name   Arraytype.Temp  Genetic.interaction.score..ε.
##  cdc26-1   :   2212   DMA26:1212145   Min.   :-1.09550             
##  rfa1-m2   :   2212   DMA30: 798532   1st Qu.:-0.02740             
##  mak16-5001:   2211   TSA26:1677715   Median :-0.00060             
##  cdc19-1   :   2210   TSA30:  20561   Mean   :-0.00528             
##  cdc4-3    :   2210                   3rd Qu.: 0.02390             
##  rsc8-ts16 :   2210                   Max.   : 1.33530             
##  (Other)   :3695688                                                
##     P.value       Query.single.mutant.fitness..SMF.   Array.SMF     
##  Min.   :0.0000   Min.   :0.1                       Min.   :0.1137  
##  1st Qu.:0.1155   1st Qu.:0.7                       1st Qu.:0.8393  
##  Median :0.2746   Median :0.9                       Median :0.9577  
##  Mean   :0.2554   Mean   :0.8                       Mean   :0.9072  
##  3rd Qu.:0.3950   3rd Qu.:1.0                       3rd Qu.:1.0065  
##  Max.   :1.0000   Max.   :1.1                       Max.   :1.1118  
##                   NA's   :403475                                    
##  Double.mutant.fitness Double.mutant.fitness.standard.deviation
##  Min.   :-0.1764       Min.   :0.00000                         
##  1st Qu.: 0.6532       1st Qu.:0.02160                         
##  Median : 0.8001       Median :0.03650                         
##  Mean   : 0.7680       Mean   :0.04677                         
##  3rd Qu.: 0.9126       3rd Qu.:0.05910                         
##  Max.   : 2.1866       Max.   :1.19310                         
## 
#NXN I do not need consider NxN for my aging modeling project
tb.nn = read.table(paste(datapath,"SGA_NxN.txt", sep=''), header=T, sep="\t");
## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec =
## dec, : EOF within quoted string
## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec =
## dec, : number of items read is not a multiple of the number of columns
summary(tb.nn);
##        Query.Strain.ID    Query.allele.name        Array.Strain.ID   
##  YJL219W_sn2362:   3708   hxt9   :   3708   YIL173W_dma2391:   1712  
##  YIR017C_sn2001:   3696   met28  :   3696   YIL165C_dma2396:   1710  
##  YJL154C_sn371 :   3674   vps35  :   3674   YIL170W_dma2392:   1706  
##  YJL141C_sn1525:   3673   yak1   :   3673   YIL141W_dma2367:   1700  
##  YJL056C_sn2004:   3667   zap1   :   3667   YIL145C_dma2366:   1698  
##  YJR117W_sn882 :   3666   ste24  :   3666   YIL140W_dma2368:   1694  
##  (Other)       :6261409   (Other):6261409   (Other)        :6273273  
##  Array.allele.name Arraytype.Temp  Genetic.interaction.score..ε.
##  vth1   :   1712        :      1   Min.   :-1.16160             
##  yil165c:   1710   DMA26: 203777   1st Qu.:-0.02200             
##  hxt12  :   1706   DMA30:6079715   Median :-0.00220             
##  yil141w:   1700                   Mean   :-0.00389             
##  pan6   :   1698                   3rd Qu.: 0.01780             
##  axl2   :   1694                   Max.   : 0.86760             
##  (Other):6273273                   NA's   :1                    
##     P.value       Query.single.mutant.fitness..SMF.   Array.SMF     
##  Min.   :0.0000   Min.   :0.2                       Min.   :0.2265  
##  1st Qu.:0.1633   1st Qu.:0.9                       1st Qu.:0.9667  
##  Median :0.3083   Median :1.0                       Median :1.0010  
##  Mean   :0.2809   Mean   :0.9                       Mean   :0.9712  
##  3rd Qu.:0.4113   3rd Qu.:1.0                       3rd Qu.:1.0195  
##  Max.   :1.0000   Max.   :1.1                       Max.   :1.1118  
##  NA's   :1        NA's   :479989                    NA's   :1       
##  Double.mutant.fitness Double.mutant.fitness.standard.deviation
##  Min.   :-0.2577       Min.   :0.00000                         
##  1st Qu.: 0.8521       1st Qu.:0.01900                         
##  Median : 0.9695       Median :0.03170                         
##  Mean   : 0.9069       Mean   :0.04062                         
##  3rd Qu.: 1.0199       3rd Qu.:0.05050                         
##  Max.   : 1.7606       Max.   :1.00090                         
##  NA's   :1             NA's   :1
Columns names in the 3 tables are the same.
rbind( names(tb.en), names(tb.ee), names(tb.nn))
##      [,1]              [,2]                [,3]             
## [1,] "Query.Strain.ID" "Query.allele.name" "Array.Strain.ID"
## [2,] "Query.Strain.ID" "Query.allele.name" "Array.Strain.ID"
## [3,] "Query.Strain.ID" "Query.allele.name" "Array.Strain.ID"
##      [,4]                [,5]             [,6]                           
## [1,] "Array.allele.name" "Arraytype.Temp" "Genetic.interaction.score..ε."
## [2,] "Array.allele.name" "Arraytype.Temp" "Genetic.interaction.score..ε."
## [3,] "Array.allele.name" "Arraytype.Temp" "Genetic.interaction.score..ε."
##      [,7]      [,8]                                [,9]       
## [1,] "P.value" "Query.single.mutant.fitness..SMF." "Array.SMF"
## [2,] "P.value" "Query.single.mutant.fitness..SMF." "Array.SMF"
## [3,] "P.value" "Query.single.mutant.fitness..SMF." "Array.SMF"
##      [,10]                   [,11]                                     
## [1,] "Double.mutant.fitness" "Double.mutant.fitness.standard.deviation"
## [2,] "Double.mutant.fitness" "Double.mutant.fitness.standard.deviation"
## [3,] "Double.mutant.fitness" "Double.mutant.fitness.standard.deviation"
Merge 3 tables into 1 table.
tb.gin = rbind(tb.ee, tb.en, tb.nn);
Double-check the merged results
length(tb.gin[,1]) == sum(length(tb.ee[,1]), length(tb.en[,1]), length(tb.nn[,1]))
## [1] TRUE
Remove unused table to free up memory
ls()
## [1] "datapath" "debug"    "dic"      "fit"      "tb.ee"    "tb.en"   
## [7] "tb.gin"   "tb.nn"
rm(tb.en, tb.ee, tb.nn)
ls()
## [1] "datapath" "debug"    "dic"      "fit"      "tb.gin"
Costanzo2016 suggested lenient, intermediate, and stringennt ways for gin quality check.
tb.gin.lenient = tb.gin[ tb.gin$P.value<=0.05, ];
if (debug ==0) { rm(tb.gin); } #freeup memory
Map strain IDs to ORFs, add my essentialFalgs
tb.gin.lenient$ORF1 = dic$Systematic.gene.name[match( tb.gin.lenient$Query.Strain.ID, dic$Strain.ID)]
tb.gin.lenient$ORF2 = dic$Systematic.gene.name[match( tb.gin.lenient$Array.Strain.ID, dic$Strain.ID)]

tb.gin.lenient$essenflag1 = fit$essenflag[ match(tb.gin.lenient$ORF1, fit$orf)]
tb.gin.lenient$essenflag2 = fit$essenflag[ match(tb.gin.lenient$ORF2, fit$orf)]

head(tb.gin.lenient)
##    Query.Strain.ID Query.allele.name Array.Strain.ID Array.allele.name
## 1   YAL001C_tsq508        tfc3-g349e  YBL023C_tsa111            mcm2-1
## 2   YAL001C_tsq508        tfc3-g349e YBL026W_tsa1065         lsm2-5001
## 7   YAL001C_tsq508        tfc3-g349e  YBL034C_tsa950            stu1-7
## 13  YAL001C_tsq508        tfc3-g349e  YBL076C_tsa275            ils1-1
## 16  YAL001C_tsq508        tfc3-g349e  YBL097W_tsa510            brn1-9
## 25  YAL001C_tsq508        tfc3-g349e YBR029C_tsa1063         cds1-5001
##    Arraytype.Temp Genetic.interaction.score..ε.   P.value
## 1           TSA30                       -0.0348 5.042e-03
## 2           TSA30                       -0.3529 3.591e-06
## 7           TSA30                       -0.1294 1.931e-02
## 13          TSA30                       -0.0250 1.301e-04
## 16          TSA30                       -0.0808 5.582e-15
## 25          TSA30                       -0.1173 8.243e-05
##    Query.single.mutant.fitness..SMF. Array.SMF Double.mutant.fitness
## 1                             0.8285    0.9254                0.7319
## 2                             0.8285    0.9408                0.4266
## 7                             0.8285    0.6690                0.4249
## 13                            0.8285    0.8097                0.6458
## 16                            0.8285    0.5464                0.3719
## 25                            0.8285    0.9007                0.6289
##    Double.mutant.fitness.standard.deviation    ORF1    ORF2   essenflag1
## 1                                    0.0102 YAL001C YBL023C nonessential
## 2                                    0.0790 YAL001C YBL026W nonessential
## 7                                    0.0482 YAL001C YBL034C nonessential
## 13                                   0.0054 YAL001C YBL076C nonessential
## 16                                   0.0077 YAL001C YBL097W nonessential
## 25                                   0.0226 YAL001C YBR029C nonessential
##    essenflag2
## 1   essential
## 2   essential
## 7    abnormal
## 13  essential
## 16  essential
## 25  essential
tb.gin.intermediate = tb.gin.lenient[ abs(tb.gin.lenient$Genetic.interaction.score..ε.) >0.08, ];
tb.gin.stringent = tb.gin.lenient[ tb.gin.lenient$Genetic.interaction.score..ε.>0.16 | tb.gin.lenient$Genetic.interaction.score..ε.< -0.12, ]
hist(tb.gin.lenient$Genetic.interaction.score..ε., breaks = 100)
summary(tb.gin.lenient);
##         Query.Strain.ID     Query.allele.name         Array.Strain.ID   
##  YJL029C_sn248  :   1364   vps53     :   1364   YIL048W_tsa188:   1368  
##  YIR033W_sn1943 :   1214   mga2      :   1214   YDL008W_tsa783:   1328  
##  YIL004C_tsq1171:   1208   bet1-1    :   1208   YDR172W_tsa28 :   1316  
##  YJR002W_tsq2069:   1124   mpp10-5001:   1124   YFL039C_tsa140:   1315  
##  YJR045C_tsq2790:   1045   ssc1-2    :   1045   YHR164C_tsa352:   1274  
##  (Other)        :1563516   (Other)   :1563516   (Other)       :1562870  
##  NA's           :      1   NA's      :      1   NA's          :      1  
##  Array.allele.name  Arraytype.Temp Genetic.interaction.score..ε.
##  neo1-2  :   1368   TSA26:523518   Min.   :-1.16160             
##  apc11-13:   1328   TSA30:  5813   1st Qu.:-0.07190             
##  sup35-td:   1316   DMA26:203445   Median :-0.01780             
##  act1-125:   1315   DMA30:836695   Mean   :-0.01934             
##  dna2-2  :   1274        :     0   3rd Qu.: 0.05520             
##  (Other) :1562870   NA's :     1   Max.   : 1.33530             
##  NA's    :      1                  NA's   :1                    
##     P.value          Query.single.mutant.fitness..SMF.   Array.SMF     
##  Min.   :0.0000000   Min.   :0.11                      Min.   :0.1137  
##  1st Qu.:0.0000455   1st Qu.:0.76                      1st Qu.:0.8243  
##  Median :0.0048550   Median :0.92                      Median :0.9525  
##  Mean   :0.0125985   Mean   :0.86                      Mean   :0.8981  
##  3rd Qu.:0.0229400   3rd Qu.:1.00                      3rd Qu.:1.0060  
##  Max.   :0.0500000   Max.   :1.14                      Max.   :1.1118  
##  NA's   :1           NA's   :165086                    NA's   :1       
##  Double.mutant.fitness Double.mutant.fitness.standard.deviation
##  Min.   :-0.2577       Min.   :0.0000                          
##  1st Qu.: 0.6044       1st Qu.:0.0090                          
##  Median : 0.8079       Median :0.0163                          
##  Mean   : 0.7654       Mean   :0.0260                          
##  3rd Qu.: 0.9619       3rd Qu.:0.0301                          
##  Max.   : 2.1866       Max.   :0.9147                          
##  NA's   :1             NA's   :1                               
##         ORF1                ORF2                essenflag1    
##  YFL039C  :   8053   YFL039C  :  14881   abnormal    : 27510  
##  YFL034C-B:   3562   YFL034C-B:   7918   essential   :493722  
##  YNL181W  :   3529   YHR036W  :   4162   nonessential:916926  
##  YBL105C  :   3090   YNL061W  :   3851   NA's        :131314  
##  YBR088C  :   2778   YJR076C  :   3789                        
##  (Other)  :1548459   (Other)  :1534870                        
##  NA's     :      1   NA's     :      1                        
##         essenflag2     
##  abnormal    :  25160  
##  essential   : 480824  
##  nonessential:1009399  
##  NA's        :  54089  
##                        
##                        
## 
summary(tb.gin.intermediate);
##         Query.Strain.ID    Query.allele.name        Array.Strain.ID  
##  YNL243W_sn760  :   711   sla2      :   711   YDL008W_tsa783:  1017  
##  YDR293C_sn729  :   692   ssd1      :   692   YLR078C_tsa199:   940  
##  YKL154W_tsq1163:   678   srp102-510:   678   YDR172W_tsa28 :   935  
##  YJR045C_tsq2790:   664   ssc1-2    :   664   YFL039C_tsa140:   883  
##  YLR275W_tsq2653:   656   smd2-5005 :   656   YER157W_tsa41 :   881  
##  (Other)        :566233   (Other)   :566233   (Other)       :564978  
##  NA's           :     1   NA's      :     1   NA's          :     1  
##  Array.allele.name Arraytype.Temp Genetic.interaction.score..ε.
##  apc11-13:  1017   TSA26:256208   Min.   :-1.16160             
##  bos1-1  :   940   TSA30:  3153   1st Qu.:-0.15690             
##  sup35-td:   935   DMA26: 81038   Median :-0.09740             
##  act1-125:   883   DMA30:229235   Mean   :-0.05968             
##  cog3-1  :   881        :     0   3rd Qu.: 0.09720             
##  (Other) :564978   NA's :     1   Max.   : 1.33530             
##  NA's    :     1                  NA's   :1                    
##     P.value          Query.single.mutant.fitness..SMF.   Array.SMF     
##  Min.   :0.0000000   Min.   :0.18                      Min.   :0.1137  
##  1st Qu.:0.0000015   1st Qu.:0.76                      1st Qu.:0.7400  
##  Median :0.0016875   Median :0.89                      Median :0.8690  
##  Mean   :0.0097757   Mean   :0.85                      Mean   :0.8414  
##  3rd Qu.:0.0160700   3rd Qu.:0.99                      3rd Qu.:0.9745  
##  Max.   :0.0500000   Max.   :1.14                      Max.   :1.1118  
##  NA's   :1           NA's   :87094                     NA's   :1       
##  Double.mutant.fitness Double.mutant.fitness.standard.deviation
##  Min.   :-0.2577       Min.   :0.00000                         
##  1st Qu.: 0.4933       1st Qu.:0.02280                         
##  Median : 0.6851       Median :0.03680                         
##  Mean   : 0.6773       Mean   :0.04859                         
##  3rd Qu.: 0.8650       3rd Qu.:0.05780                         
##  Max.   : 2.1866       Max.   :0.91470                         
##  NA's   :1             NA's   :1                               
##         ORF1               ORF2               essenflag1    
##  YFL039C  :  4584   YFL039C  :  8373   abnormal    : 13170  
##  YNL181W  :  2435   YFL034C-B:  4706   essential   :221747  
##  YFL034C-B:  2028   YJR076C  :  2437   nonessential:290106  
##  YBL105C  :  1752   YDR182W  :  2224   NA's        : 44612  
##  YEL034W  :  1522   YNL061W  :  1928                        
##  (Other)  :557313   (Other)  :549966                        
##  NA's     :     1   NA's     :     1                        
##         essenflag2    
##  abnormal    : 12310  
##  essential   :234519  
##  nonessential:303111  
##  NA's        : 19695  
##                       
##                       
## 
summary(tb.gin.stringent)
##         Query.Strain.ID       Query.allele.name        Array.Strain.ID  
##  YNL243W_sn760  :   574   sla2         :   574   YDL008W_tsa783:   649  
##  YDR293C_sn729  :   460   ssd1         :   460   YLR268W_tsa121:   629  
##  YBR049C_tsq1348:   448   reb1-5001    :   448   YFL039C_tsa140:   518  
##  YLR275W_tsq2653:   425   smd2-5005    :   425   YER157W_tsa41 :   512  
##  YEL034W_tsq737 :   377   mob2-11-supp1:   380   YJR076C_tsa84 :   494  
##  (Other)        :250780   (Other)      :250777   (Other)       :250262  
##  NA's           :     1   NA's         :     1   NA's          :     1  
##  Array.allele.name Arraytype.Temp Genetic.interaction.score..ε.
##  apc11-13:   649   TSA26:124897   Min.   :-1.1616              
##  sec22-3 :   629   TSA30:  1638   1st Qu.:-0.2354              
##  act1-125:   518   DMA26: 35986   Median :-0.1670              
##  cog3-1  :   512   DMA30: 90543   Mean   :-0.1610              
##  cdc11-5 :   494        :     0   3rd Qu.:-0.1322              
##  (Other) :250262   NA's :     1   Max.   : 1.3353              
##  NA's    :     1                  NA's   :1                    
##     P.value          Query.single.mutant.fitness..SMF.   Array.SMF     
##  Min.   :0.0000000   Min.   :0.18                      Min.   :0.1137  
##  1st Qu.:0.0000000   1st Qu.:0.76                      1st Qu.:0.7235  
##  Median :0.0003424   Median :0.89                      Median :0.8465  
##  Mean   :0.0072841   Mean   :0.86                      Mean   :0.8220  
##  3rd Qu.:0.0092682   3rd Qu.:0.98                      3rd Qu.:0.9510  
##  Max.   :0.0500000   Max.   :1.14                      Max.   :1.1118  
##  NA's   :1           NA's   :43715                     NA's   :1       
##  Double.mutant.fitness Double.mutant.fitness.standard.deviation
##  Min.   :-0.2577       Min.   :0.0000                          
##  1st Qu.: 0.3829       1st Qu.:0.0310                          
##  Median : 0.5491       Median :0.0536                          
##  Mean   : 0.5619       Mean   :0.0686                          
##  3rd Qu.: 0.7233       3rd Qu.:0.0857                          
##  Max.   : 2.1866       Max.   :0.9147                          
##  NA's   :1             NA's   :1                               
##         ORF1               ORF2               essenflag1    
##  YFL039C  :  2200   YFL039C  :  4416   abnormal    :  6379  
##  YNL181W  :  1350   YFL034C-B:  2238   essential   :104477  
##  YFL034C-B:  1068   YJR076C  :  1381   nonessential:122575  
##  YEL034W  :   897   YDR182W  :  1215   NA's        : 19634  
##  YBL105C  :   818   YLR268W  :   950                        
##  (Other)  :246731   (Other)  :242864                        
##  NA's     :     1   NA's     :     1                        
##         essenflag2    
##  abnormal    :  6140  
##  essential   :113948  
##  nonessential:124214  
##  NA's        :  8763  
##                       
##                       
##