PROJECT
KOUSTUV GHOSH
PRINCIPLE
The R language was primarily designed as a language for data manipulation, modeling and visualization, and many of the data structures reflect this view. ####When R is started, a workspace is created and that workspace is where the user creates and manipulates variables.This workspace is an environment, and an environment is a set of bindings of names, or symbols, to values. The top-level workspace can be accessed through its name, which is GlobalEnv. Assignment of value to a variable is generally done with either the = (equals) character, or a special symbol that is the concatenation of less than and minus,<-. Assignment creates a binding between a symbol and a value, in a particular environment. ###An R package typically consists of a coherent collection of functions and data structures that are suitable for addressing a particular problem. ####Here we use some package : ###1. Bio3d ###2. ggbiplot ###3. peptides
ASSIGNMENT
PROJECT 1
download.pdb= function(protein){
base.url= "http://www.rcsb.org/pdb/files/"
dest.file= paste(protein,'.pdb',sep = "")
protein.url= paste(base.url,dest.file,sep = "")
download.file(protein.url,destfile = dest.file)
}
proteins =list( "4PGU","3ZKV")
require(plyr)## Loading required package: plyrl_ply(proteins,download.pdb)
library("bio3d")## Warning: package 'bio3d' was built under R version 3.4.4library("Peptides")## Warning: package 'Peptides' was built under R version 3.4.4#Membrane protein
mem.pgu<-read.pdb("4pgu.pdb")## PDB has ALT records, taking A only, rm.alt=TRUEseq.pgu<-pdbseq(mem.pgu)
#Thermostability value of the membrane protein
seq.pgu.i<-aIndex(seq.pgu)
ik<-which(seq.pgu.i==0.0)
seq.pgu.in<-seq.pgu.i[-ik]
therm.pgu<-sum(seq.pgu.in)/length(seq.pgu.in)
(therm.pgu)## [1] 305.9574#Protein Interaction
seq.pgu.pp<-boman(seq.pgu)
bin.pot<-sum(seq.pgu.pp)/length(seq.pgu)
bin.pot## [1] -0.6791787#binpot<2.68 which implies the protein has lower affinity to bind with another protein
#Cytosolic protein
cyto.zkv<-read.pdb("3zkv.pdb")
seq.zkv<-pdbseq(cyto.zkv)
#Amino acid descriptors for the cytosolic protein compared with the reference 20 aa present
seq.zkv.d<-aaDescriptors(seq.zkv)
summary(seq.zkv.d)## PP1.1 PP2.1 PP3.1 KF1.1
## Min. :-1.0000 Min. :-1.0000 Min. :-1.00000 Min. :-1.5600
## 1st Qu.:-0.9000 1st Qu.:-0.6400 1st Qu.:-0.24000 1st Qu.:-1.0400
## Median :-0.5500 Median :-0.4000 Median :-0.08000 Median :-0.4100
## Mean :-0.1225 Mean :-0.2631 Mean :-0.01286 Mean :-0.2437
## 3rd Qu.: 0.6700 3rd Qu.: 0.0300 3rd Qu.: 0.37000 3rd Qu.: 0.3000
## Max. : 1.0000 Max. : 1.0000 Max. : 1.00000 Max. : 2.0600
## KF2.1 KF3.1 KF4.1
## Min. :-1.9600 Min. :-1.61000 Min. :-1.57000
## 1st Qu.:-0.7100 1st Qu.:-0.42000 1st Qu.:-0.77000
## Median :-0.0700 Median :-0.23000 Median :-0.27000
## Mean :-0.1287 Mean : 0.05529 Mean : 0.02849
## 3rd Qu.: 0.2400 3rd Qu.: 0.80000 3rd Qu.: 0.81000
## Max. : 2.1000 Max. : 2.04000 Max. : 1.87000
## KF5.1 KF6.1 KF7.1
## Min. :-1.70000 Min. :-2.0500 Min. :-1.890000
## 1st Qu.:-0.71000 1st Qu.:-0.8100 1st Qu.:-0.830000
## Median :-0.21000 Median : 0.0300 Median : 0.240000
## Mean :-0.07866 Mean :-0.2244 Mean : 0.001294
## 3rd Qu.: 0.50000 3rd Qu.: 0.4000 3rd Qu.: 0.920000
## Max. : 2.00000 Max. : 2.4100 Max. : 1.520000
## KF8.1 KF9.1 KF10.1 Z1.1
## Min. :-2.3000 Min. :-2.30000 Min. :-2.3300 Min. :-4.3600
## 1st Qu.:-0.7600 1st Qu.:-0.48000 1st Qu.:-0.4400 1st Qu.:-2.8500
## Median :-0.0800 Median : 0.21000 Median : 0.1900 Median : 0.7500
## Mean :-0.1106 Mean : 0.04092 Mean :-0.0193 Mean :-0.2018
## 3rd Qu.: 0.4700 3rd Qu.: 0.66000 3rd Qu.: 0.7000 3rd Qu.: 2.3900
## Max. : 2.3600 Max. : 1.71000 Max. : 1.6300 Max. : 3.9800
## Z2.1 Z3.1 Z4.1 Z5.1
## Min. :-4.0600 Min. :-3.500 Min. :-3.0400 Min. :-2.6500
## 1st Qu.:-1.7300 1st Qu.:-1.490 1st Qu.:-1.3400 1st Qu.:-0.3800
## Median :-1.0700 Median :-0.110 Median :-0.7200 Median : 0.2600
## Mean :-0.4083 Mean :-0.309 Mean :-0.3544 Mean : 0.1833
## 3rd Qu.: 0.8900 3rd Qu.: 0.600 3rd Qu.: 0.5400 3rd Qu.: 0.8400
## Max. : 3.9400 Max. : 3.750 Max. : 3.9000 Max. : 2.0000
## F1.1 F2.1 F3.1 F4.1
## Min. :-1.3870 Min. :-2.2190 Min. :-1.65600 Min. :-2.0800
## 1st Qu.:-1.2290 1st Qu.:-0.4500 1st Qu.:-0.61000 1st Qu.:-0.3280
## Median :-0.2050 Median : 0.4610 Median :-0.02400 Median : 0.3330
## Mean :-0.1001 Mean : 0.2093 Mean :-0.02822 Mean : 0.3088
## 3rd Qu.: 0.9970 3rd Qu.: 0.8210 3rd Qu.: 0.70300 3rd Qu.: 1.0260
## Max. : 1.5240 Max. : 1.3880 Max. : 2.06900 Max. : 1.9040
## F5.1 F6.1 T1.1 T2.1
## Min. :-1.11500 Min. :-1.76200 Min. :-10.61 Min. :-3.54000
## 1st Qu.:-0.19600 1st Qu.:-0.60000 1st Qu.: -5.97 1st Qu.:-0.94000
## Median : 0.00100 Median :-0.06800 Median : -4.38 Median :-0.28000
## Mean : 0.08623 Mean : 0.02142 Mean : -4.29 Mean : 0.04866
## 3rd Qu.: 0.11700 3rd Qu.: 0.50200 3rd Qu.: -3.00 3rd Qu.: 0.98000
## Max. : 3.84700 Max. : 2.72800 Max. : 5.73 Max. : 3.89000
## T3.1 T4.1 T5.1 VHSE1.1
## Min. :-2.34000 Min. :-1.9600 Min. :-0.7900 Min. :-1.47000
## 1st Qu.:-0.49000 1st Qu.:-0.3900 1st Qu.:-0.2100 1st Qu.:-0.96000
## Median : 0.01000 Median : 0.3100 Median : 0.3300 Median : 0.15000
## Mean :-0.05346 Mean : 0.2676 Mean : 0.4768 Mean : 0.04167
## 3rd Qu.: 0.63000 3rd Qu.: 1.1000 3rd Qu.: 0.9500 3rd Qu.: 1.01000
## Max. : 1.39000 Max. : 1.6400 Max. : 3.2500 Max. : 1.52000
## VHSE2.1 VHSE3.1 VHSE4.1
## Min. :-1.67000 Min. :-2.63000 Min. :-1.9100
## 1st Qu.:-0.86000 1st Qu.:-0.50000 1st Qu.:-0.9200
## Median :-0.14000 Median : 0.10000 Median :-0.1600
## Mean :-0.08737 Mean :-0.04296 Mean :-0.2499
## 3rd Qu.: 0.40000 3rd Qu.: 0.37000 3rd Qu.: 0.3600
## Max. : 2.06000 Max. : 1.79000 Max. : 2.2800
## VHSE5.1 VHSE6.1 VHSE7.1 VHSE8.1
## Min. :-2.68000 Min. :-1.6100 Min. :-1.61000 Min. :-1.3400
## 1st Qu.:-0.06000 1st Qu.:-1.3700 1st Qu.:-0.64000 1st Qu.:-0.5200
## Median : 0.22000 Median :-0.0100 Median :-0.16000 Median :-0.1300
## Mean :-0.03978 Mean :-0.2049 Mean : 0.04066 Mean :-0.0169
## 3rd Qu.: 0.30000 3rd Qu.: 0.4200 3rd Qu.: 0.73000 3rd Qu.: 0.1300
## Max. : 1.64000 Max. : 1.4700 Max. : 2.01000 Max. : 3.5600
## ProtFP1.1 ProtFP2.1 ProtFP3.1 ProtFP4.1
## Min. :-6.6100 Min. :-8.7200 Min. :-3.5900 Min. :-4.5800
## 1st Qu.:-4.5700 1st Qu.:-2.5500 1st Qu.:-2.2900 1st Qu.:-1.1200
## Median :-0.1000 Median :-1.3300 Median :-0.8300 Median : 0.7100
## Mean : 0.1791 Mean :-0.3101 Mean :-0.6521 Mean : 0.2796
## 3rd Qu.: 5.1100 3rd Qu.: 2.2000 3rd Qu.: 0.8900 3rd Qu.: 1.1100
## Max. : 7.3300 Max. : 6.6000 Max. : 4.1800 Max. : 3.0000
## ProtFP5.1 ProtFP6.1 ProtFP7.1
## Min. :-3.2200 Min. :-3.54000 Min. :-2.95000
## 1st Qu.:-1.2300 1st Qu.:-0.55000 1st Qu.:-0.45000
## Median :-0.3100 Median : 0.08000 Median : 0.00000
## Mean :-0.1774 Mean : 0.05045 Mean : 0.04758
## 3rd Qu.: 0.9900 3rd Qu.: 0.76000 3rd Qu.: 0.74000
## Max. : 3.2700 Max. : 2.91000 Max. : 1.99000
## ProtFP8.1 ST1.1 ST2.1 ST3.1
## Min. :-2.790000 Min. :-1.8440 Min. :-1.010 Min. :-0.9170
## 1st Qu.:-0.510000 1st Qu.:-1.1330 1st Qu.:-0.791 1st Qu.:-0.6270
## Median : 0.300000 Median :-0.8260 Median :-0.379 Median :-0.1930
## Mean : 0.008729 Mean :-0.8091 Mean :-0.258 Mean :-0.1694
## 3rd Qu.: 0.870000 3rd Qu.:-0.6290 3rd Qu.: 0.228 3rd Qu.: 0.0380
## Max. : 1.650000 Max. : 0.8530 Max. : 0.731 Max. : 1.1000
## ST4.1 ST5.1 ST6.1
## Min. :-1.16300 Min. :-0.93700 Min. :-3.31700
## 1st Qu.:-0.21400 1st Qu.:-0.56100 1st Qu.:-0.77500
## Median :-0.06500 Median :-0.25300 Median : 0.27300
## Mean :-0.05008 Mean :-0.07831 Mean :-0.06242
## 3rd Qu.:-0.04900 3rd Qu.: 0.53800 3rd Qu.: 1.01100
## Max. : 0.85900 Max. : 1.12000 Max. : 1.09100
## ST7.1 ST8.1 BLOSUM1.1 BLOSUM2.1
## Min. :-1.0990 Min. :-0.89400 Min. :-1.620000 Min. :-1.230
## 1st Qu.:-0.1470 1st Qu.:-0.71700 1st Qu.:-1.140000 1st Qu.:-0.860
## Median : 0.1660 Median :-0.07500 Median : 0.190000 Median :-0.450
## Mean : 0.1141 Mean : 0.06613 Mean :-0.003723 Mean :-0.265
## 3rd Qu.: 0.2480 3rd Qu.: 0.36700 3rd Qu.: 1.090000 3rd Qu.: 0.220
## Max. : 1.2560 Max. : 2.52200 Max. : 1.550000 Max. : 2.280
## BLOSUM3.1 BLOSUM4.1 BLOSUM5.1
## Min. :-0.9700 Min. :-1.610000 Min. :-1.240000
## 1st Qu.:-0.8600 1st Qu.:-0.360000 1st Qu.:-0.550000
## Median :-0.6300 Median : 0.000000 Median : 0.030000
## Mean :-0.2491 Mean :-0.007583 Mean : 0.009381
## 3rd Qu.: 0.3200 3rd Qu.: 0.380000 3rd Qu.: 0.240000
## Max. : 1.7300 Max. : 1.550000 Max. : 1.830000
## BLOSUM6.1 BLOSUM7.1 BLOSUM8.1
## Min. :-2.02000 Min. :-1.62000 Min. :-1.96000
## 1st Qu.: 0.01000 1st Qu.:-0.30000 1st Qu.:-0.08000
## Median : 0.20000 Median : 0.01000 Median : 0.15000
## Mean : 0.09162 Mean :-0.02557 Mean :-0.06479
## 3rd Qu.: 0.34000 3rd Qu.: 0.21000 3rd Qu.: 0.20000
## Max. : 1.19000 Max. : 1.21000 Max. : 0.87000
## BLOSUM9.1 BLOSUM10.1 MSWHIM1.1 MSWHIM2.1
## Min. :-1.20000 Min. :-1.29000 Min. :-1.0000 Min. :-1.0000
## 1st Qu.:-0.22000 1st Qu.:-0.25000 1st Qu.:-0.7400 1st Qu.: 0.2000
## Median : 0.01000 Median : 0.06000 Median :-0.5800 Median : 0.6700
## Mean : 0.09893 Mean : 0.02646 Mean :-0.3291 Mean : 0.4594
## 3rd Qu.: 0.43000 3rd Qu.: 0.33000 3rd Qu.: 0.1400 3rd Qu.: 0.8300
## Max. : 1.36000 Max. : 0.99000 Max. : 1.0000 Max. : 1.0000
## MSWHIM3.1
## Min. :-1.0000
## 1st Qu.:-0.6600
## Median :-0.3200
## Mean :-0.3532
## 3rd Qu.:-0.1600
## Max. : 1.0000#Thermostability value of the cytosolic protein
seq.zkv.i<-aIndex(seq.zkv)
jk<-which(seq.zkv.i==0.0)
seq.zkv.in<-seq.zkv.i[-jk]
therm.zkv<-sum(seq.zkv.in)/length(seq.zkv.in)
(therm.zkv)## [1] 306.4413#Protein Interaction
seq.zkv.pp<-boman(seq.zkv)
bin.pot1<-sum(seq.zkv.pp)/length(seq.zkv)
(bin.pot1)## [1] 1.220504#binpot<2.68 which implies the protein has lower affinity to bind with another protein
#Principle components
#A function for protein pca
protanapca<-function(seq){
library("bio3d")
library("Peptides")
ch<-charge(seq, pH = 7, pKscale = "EMBOSS")
pp<-boman(seq)
ampI<-hmoment(seq, angle = 100, window = 11)
hy<-hydrophobicity(seq, scale = "KyteDoolittle")
pI<-pI(seq,pKscale = "EMBOSS")
mw<-mw(seq, monoisotopic = FALSE)
protana<-cbind(ch,pp,ampI,hy,pI,mw)
colnames(protana)<-c("CHARGE","P-P","AMPIPHILLICITY","HYDRPHOBICITY","ISOELECTRIC","MOL WEIGHT")
p<-cbind(seq,protana)
colnames(p)<-c("AA","CHARGE","P-P","AMPIPHILLICITY","HYDRPHOBICITY","ISOELECTRIC","MOL WEIGHT")
pr.p<-prcomp(protana)
return(pr.p)}
#A function for protein analysis of different properties
protana<-function(seq){
library("bio3d")
library("Peptides")
ch<-charge(seq, pH = 7, pKscale = "EMBOSS")
pp<-boman(seq)
ampI<-hmoment(seq, angle = 100, window = 11)
hy<-hydrophobicity(seq, scale = "KyteDoolittle")
pI<-pI(seq,pKscale = "EMBOSS")
mw<-mw(seq, monoisotopic = FALSE)
protana<-cbind(ch,pp,ampI,hy,pI,mw)
colnames(protana)<-c("CHARGE","P-P","AMPIPHILLICITY","HYDRPHOBICITY","ISOELECTRIC","MOL WEIGHT")
p<-cbind(seq,protana)
colnames(p)<-c("AA","CHARGE","P-P","AMPIPHILLICITY","HYDRPHOBICITY","ISOELECTRIC","MOL WEIGHT")
return(p)}
#Function for the matrix
prot<-function(seq){
library("bio3d")
library("Peptides")
ch<-charge(seq, pH = 7, pKscale = "EMBOSS")
pp<-boman(seq)
ampI<-hmoment(seq, angle = 100, window = 11)
hy<-hydrophobicity(seq, scale = "KyteDoolittle")
pI<-pI(seq,pKscale = "EMBOSS")
mw<-mw(seq, monoisotopic = FALSE)
protana<-cbind(ch,pp,ampI,hy,pI,mw)
colnames(protana)<-c("CHARGE","P-P","AMPIPHILLICITY","HYDRPHOBICITY","ISOELECTRIC","MOL WEIGHT")
p<-cbind(seq,protana)
colnames(p)<-c("AA","CHARGE","P-P","AMPIPHILLICITY","HYDRPHOBICITY","ISOELECTRIC","MOL WEIGHT")
return(p)}
protana1<-function(seq){
library("bio3d")
library("Peptides")
ch<-charge(seq, pH = 7, pKscale = "EMBOSS")
pp<-boman(seq)
ampI<-hmoment(seq, angle = 100, window = 11)
hy<-hydrophobicity(seq, scale = "KyteDoolittle")
pI<-pI(seq,pKscale = "EMBOSS")
mw<-mw(seq, monoisotopic = FALSE)
protana<-cbind(ch,pp,ampI,hy,pI,mw)
colnames(protana)<-c("CHARGE","P-P","AMPIPHILLICITY","HYDRPHOBICITY","ISOELECTRIC","MOL WEIGHT")
return(protana)}
#Membrane protein
mem.pgu<-read.pdb("4pgu.pdb")## PDB has ALT records, taking A only, rm.alt=TRUEseq.pgu<-pdbseq(mem.pgu)
pgu.a<-protana(seq.pgu)
pca.pgu<-protanapca(seq.pgu)
par(mfrow=c(1,1))
pgu.m<-prot(seq.pgu)
biplot(pca.pgu,main="4PGU")
#Cytosolic protein
cyto.zkv<-read.pdb("3zkv.pdb")
seq.zkv<-pdbseq(cyto.zkv)
zkv.a<-protana(seq.zkv)
pca.zkv<-protanapca(seq.zkv)
zkv.m<-prot(seq.zkv)
zpca<-protana1(seq.zkv)
#pca and biplot
biplot(pca.zkv,main="3ZKV")
ppca<-protana1(seq.pgu)
fm<-rbind(zkv.m,pgu.m)
fm1<-data.frame(fm)## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,990,991,992,993,994,995,996,997,998,999,1000,1001,1002,1003,1004,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080
## --> row.names NOT usedfm2<-na.omit(fm1)
mf<-as.matrix(fm)
pm<-rbind(zpca,ppca)
matr.f<-as.matrix(pm)
pm.pca<-prcomp(matr.f)
matr.f## CHARGE P-P AMPIPHILLICITY HYDRPHOBICITY ISOELECTRIC MOL WEIGHT
## 54 -0.02410542 5.54 0.85 -3.5 6.099982 146.14594
## 55 -1.02284808 6.81 0.74 -3.5 3.849974 147.13074
## 56 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 57 -0.02410542 5.54 0.85 -3.5 6.099982 146.14594
## 58 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 59 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 60 -0.02410542 -0.94 0.48 -0.4 6.099982 75.06714
## 61 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 62 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 63 -0.02410542 2.57 0.05 -0.7 6.099982 119.12034
## 64 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 65 0.21614765 4.66 0.40 -3.2 7.550325 155.15634
## 66 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344
## 67 0.97573612 5.55 1.50 -3.9 9.700016 146.18934
## 68 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 69 -0.02410542 -2.35 0.64 1.9 6.099982 149.20784
## 70 0.97573612 5.55 1.50 -3.9 9.700016 146.18934
## 71 0.21614765 4.66 0.40 -3.2 7.550325 155.15634
## 72 -0.02410542 -2.33 0.81 -0.9 6.099982 204.22844
## 73 0.21614765 4.66 0.40 -3.2 7.550325 155.15634
## 74 -1.02284808 6.81 0.74 -3.5 3.849974 147.13074
## 75 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 76 -0.02410542 0.00 0.12 -1.6 6.099982 115.13194
## 99 0.97573612 5.55 1.50 -3.9 9.700016 146.18934
## 100 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 101 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 102 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 103 -0.02410542 6.64 0.78 -3.5 6.099982 132.11904
## 104 0.97589142 14.92 2.53 -4.5 10.549999 174.20274
## 105 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 106 -0.05475885 -1.28 0.29 2.5 5.922539 121.15404
## 107 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 108 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344
## 109 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 110 -0.02410542 -0.94 0.48 -0.4 6.099982 75.06714
## 111 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 112 -0.02489912 0.14 0.26 -1.3 6.093221 181.19124
## 113 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 114 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 115 0.21614765 4.66 0.40 -3.2 7.550325 155.15634
## 116 -0.02410542 -2.35 0.64 1.9 6.099982 149.20784
## 117 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 118 -0.02410542 -0.94 0.48 -0.4 6.099982 75.06714
## 122 -0.02410542 -0.94 0.48 -0.4 6.099982 75.06714
## 123 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 124 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 125 -1.02284808 6.81 0.74 -3.5 3.849974 147.13074
## 126 -1.02284808 6.81 0.74 -3.5 3.849974 147.13074
## 127 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 128 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 129 -0.02410542 6.64 0.78 -3.5 6.099982 132.11904
## 130 -0.02410542 2.57 0.05 -0.7 6.099982 119.12034
## 131 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 132 -0.02410542 5.54 0.85 -3.5 6.099982 146.14594
## 133 -0.02410542 6.64 0.78 -3.5 6.099982 132.11904
## 134 -0.02410542 5.54 0.85 -3.5 6.099982 146.14594
## 135 0.97589142 14.92 2.53 -4.5 10.549999 174.20274
## 136 -0.02410542 -2.35 0.64 1.9 6.099982 149.20784
## 140 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344
## 141 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 142 -1.02331172 8.72 0.90 -3.5 3.749972 133.10384
## 143 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 144 -0.02410542 5.54 0.85 -3.5 6.099982 146.14594
## 145 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 146 -0.02410542 -2.33 0.81 -0.9 6.099982 204.22844
## 147 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 148 -0.02410542 -2.35 0.64 1.9 6.099982 149.20784
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## 27 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 28 -0.02410542 2.57 0.05 -0.7 6.099982 119.12034
## 29 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 30 -0.02410542 -0.94 0.48 -0.4 6.099982 75.06714
## 31 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 32 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 33 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 34 -0.02410542 -0.94 0.48 -0.4 6.099982 75.06714
## 35 -0.02410542 5.54 0.85 -3.5 6.099982 146.14594
## 36 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 37 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 38 -0.02410542 0.00 0.12 -1.6 6.099982 115.13194
## 39 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 40 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 41 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 42 -0.02410542 -2.35 0.64 1.9 6.099982 149.20784
## 43 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 44 -0.02410542 0.00 0.12 -1.6 6.099982 115.13194
## 45 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 46 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344
## 47 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 48 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 49 -1.02284808 6.81 0.74 -3.5 3.849974 147.13074
## 50 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 51 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 52 -0.02410542 -2.35 0.64 1.9 6.099982 149.20784
## 53 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 54 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 55 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 56 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 57 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 58 -0.02410542 -2.33 0.81 -0.9 6.099982 204.22844
## 59 -0.02410542 -2.35 0.64 1.9 6.099982 149.20784
## 60 0.97589142 14.92 2.53 -4.5 10.549999 174.20274
## 61 0.97589142 14.92 2.53 -4.5 10.549999 174.20274
## 62 0.97589142 14.92 2.53 -4.5 10.549999 174.20274
## 63 0.97573612 5.55 1.50 -3.9 9.700016 146.18934
## 64 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 65 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 66 -0.02410542 -0.94 0.48 -0.4 6.099982 75.06714
## 67 -0.02489912 0.14 0.26 -1.3 6.093221 181.19124
## 68 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 69 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 70 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 71 -0.02489912 0.14 0.26 -1.3 6.093221 181.19124
## 72 -0.02410542 2.57 0.05 -0.7 6.099982 119.12034
## 73 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 74 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 75 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 76 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 77 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344
## 78 -0.02410542 -0.94 0.48 -0.4 6.099982 75.06714
## 79 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 80 -0.02410542 2.57 0.05 -0.7 6.099982 119.12034
## 81 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 82 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 83 -0.02410542 0.00 0.12 -1.6 6.099982 115.13194
## 84 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 85 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 86 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344
## 87 0.21614765 4.66 0.40 -3.2 7.550325 155.15634
## 88 -0.02489912 0.14 0.26 -1.3 6.093221 181.19124
## 89 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 90 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344
## 91 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 92 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 93 -0.02410542 -0.94 0.48 -0.4 6.099982 75.06714
## 94 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 95 -0.02489912 0.14 0.26 -1.3 6.093221 181.19124
## 96 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 97 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 98 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 99 -1.02284808 6.81 0.74 -3.5 3.849974 147.13074
## 100 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 101 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 102 -0.02410542 -0.94 0.48 -0.4 6.099982 75.06714
## 103 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344
## 104 -0.02410542 2.57 0.05 -0.7 6.099982 119.12034
## 105 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 106 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 107 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 108 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 109 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 110 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 111 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 112 -0.02410542 -0.94 0.48 -0.4 6.099982 75.06714
## 113 -0.02410542 2.57 0.05 -0.7 6.099982 119.12034
## 114 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 115 -0.02410542 -0.94 0.48 -0.4 6.099982 75.06714
## 116 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 117 0.97573612 5.55 1.50 -3.9 9.700016 146.18934
## 118 -0.02410542 -2.35 0.64 1.9 6.099982 149.20784
## 119 0.97573612 5.55 1.50 -3.9 9.700016 146.18934
## 120 0.97573612 5.55 1.50 -3.9 9.700016 146.18934
## 121 -1.02331172 8.72 0.90 -3.5 3.749972 133.10384
## 122 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 123 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344
## 124 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 125 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 126 -0.02410542 -2.33 0.81 -0.9 6.099982 204.22844
## 127 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344
## 128 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 129 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 130 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 131 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 132 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 133 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 134 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 135 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 136 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 137 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 138 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 139 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 140 -0.02410542 -0.94 0.48 -0.4 6.099982 75.06714
## 141 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 142 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 143 -0.02410542 6.64 0.78 -3.5 6.099982 132.11904
## 144 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 145 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 146 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344
## 147 -0.02410542 0.00 0.12 -1.6 6.099982 115.13194
## 148 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 149 -0.02410542 6.64 0.78 -3.5 6.099982 132.11904
## 150 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344
## 151 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 152 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 153 -0.02410542 -2.35 0.64 1.9 6.099982 149.20784
## 154 -0.02410542 -2.35 0.64 1.9 6.099982 149.20784
## 155 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 156 -0.02489912 0.14 0.26 -1.3 6.093221 181.19124
## 157 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344
## 158 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 159 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 160 -0.02410542 -0.94 0.48 -0.4 6.099982 75.06714
## 161 -0.02410542 2.57 0.05 -0.7 6.099982 119.12034
## 162 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 163 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 164 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 165 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344
## 166 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 167 -0.02489912 0.14 0.26 -1.3 6.093221 181.19124
## 168 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 169 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 170 -0.02489912 0.14 0.26 -1.3 6.093221 181.19124
## 171 -1.02331172 8.72 0.90 -3.5 3.749972 133.10384
## 172 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 173 -0.02410542 6.64 0.78 -3.5 6.099982 132.11904
## 174 -0.02410542 5.54 0.85 -3.5 6.099982 146.14594
## 175 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 176 0.97573612 5.55 1.50 -3.9 9.700016 146.18934
## 177 0.21614765 4.66 0.40 -3.2 7.550325 155.15634
## 178 0.97589142 14.92 2.53 -4.5 10.549999 174.20274
## 179 0.21614765 4.66 0.40 -3.2 7.550325 155.15634
## 180 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 181 -0.02410542 2.57 0.05 -0.7 6.099982 119.12034
## 182 -1.02284808 6.81 0.74 -3.5 3.849974 147.13074
## 183 -1.02331172 8.72 0.90 -3.5 3.749972 133.10384
## 184 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 185 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 186 -0.02410542 0.00 0.12 -1.6 6.099982 115.13194
## 187 -0.02410542 -4.04 1.08 4.2 6.099982 117.14784
## 188 -0.02410542 -2.35 0.64 1.9 6.099982 149.20784
## 189 -0.02410542 -1.81 0.62 1.8 6.099982 89.09404
## 190 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 191 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344
## 192 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 193 -0.02489912 0.14 0.26 -1.3 6.093221 181.19124
## 194 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 195 -1.02331172 8.72 0.90 -3.5 3.749972 133.10384
## 196 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 197 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 198 -0.02410542 6.64 0.78 -3.5 6.099982 132.11904
## 199 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 200 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 201 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 202 -0.02410542 6.64 0.78 -3.5 6.099982 132.11904
## 203 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 204 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 205 0.97589142 14.92 2.53 -4.5 10.549999 174.20274
## 206 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 207 -0.02410542 -2.98 1.19 2.8 6.099982 165.19184
## 208 -0.02410542 -0.94 0.48 -0.4 6.099982 75.06714
## 209 -0.02410542 -4.92 1.38 4.5 6.099982 131.17464
## 210 -0.02410542 -4.92 1.06 3.8 6.099982 131.17464
## 211 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344
## 212 -0.02410542 3.40 0.18 -0.8 6.099982 105.09344biplot(pm.pca)
#CONCLUSION
#The membrane and cytosolic protein have equal thermal stability
#The protein protein interaction parameter concludes that the cytosolic protein has higher binding potential for a protein of equal thermal stability
#The pca shows out of the six parameters chosen for analysis of this protein molecular weight is an outlier and has minimum relation to protein functionality whereas protein-protein interaction and hydrophobicity are inversely related
#The other four factors such as charge,pI,Amphophilicity and polarity are related factors for a protein irrespective of its origin
#Hence the a conclusive outcome for the above pca could be that for proteins of different origins is that hydrophobicity and protein protein interaction are inversely related irrespective of its origin
#The stastical tool used are pca,biplot and self made function protanapca(which gives the pca values of any protein sequence) and protana(which gives a matrix of all amino acid vs six parameters for any given protein)PROJECT 2
A webserver based bio3d application to explore molecular dynamics 
The analysis breaks down secondary structure assignments (i.e. helix, sheet, turn, etc.) by individual residues for each fluctutaions, allowing you to visualize or quantify the data in meaningful ways.
Protein structure
Calcium homeostasis balances passive calcium leak and active calcium uptake. Human Bax inhibitor-1 (hBI-1) is an antiapoptotic protein that mediates a calcium leak and is representative of a highly conserved and widely distributed family, the transmembrane Bax inhibitor motif (TMBIM) proteins. Here, we present crystal structures of a bacterial homolog and characterize its calcium leak activity. The structure has a seven-transmembrane-helix fold that features two triple-helix sandwiches wrapped around a central C-terminal helix. Structures obtained in closed and open conformations are reversibly interconvertible by change of pH. A hydrogen-bonded, pKa (where Ka is the acid dissociation constant)-perturbed pair of conserved aspartate residues explains the pH dependence of this transition, and biochemical studies show that pH regulates calcium influx in proteoliposomes. Homology models for hBI-1 provide insights into TMBIM-mediated calcium leak and cytoprotective activity.
csvxtract<-function(csvfile){qq<-read.csv(csvfile,header = TRUE)
age<-qq$AGE
hb<-qq$HB
hba0<-qq$HBA0
hba2<-qq$HBA2
hbf<-qq$HBF
mcv<-qq$MCV
mch<-qq$MCH
s1<-cbind(age,hb,hba0,hba2,hbf,mcv,mch)
s<-list("NUM"=s1)
return(s)}
csvxxx<-function(csvfile){qq<-read.csv(csvfile,header = TRUE)
age<-qq$AGE
hb<-qq$HB
hba0<-qq$HBA0
hba2<-qq$HBA2
hbf<-qq$HBF
mcv<-qq$MCV
mch<-qq$MCH
sexx<-qq$SEX
s1<-cbind(age,hb,hba0,hba2,hbf,mcv,mch)
s<-list("NUM"=s1)
w<-s$NUM
s5<-as.character(sexx)
m<-cbind(w,s5)
return(m)
}
#csvm<-function(csvfile){qq<-read.csv(csvfile,header = TRUE)
#age<-qq$AGE
#hb<-qq$HB
#hba0<-qq$HBA0
#hba2<-qq$HBA2
#hbf<-qq$HBF
#mcv<-qq$MCV
#mch<-qq$MCH
#sexx<-qq$SEX
#s1<-cbind(age,hb,hba0,hba2,hbf,mcv,mch)
#s<-list("NUM"=s1)
#w<-s$NUM
#s5<-as.character(sexx)
#dist<-rep("",nrow(qq))
#m<-cbind(dist,w,s5)
#m1<-list("DIST"=dist,"NUM"=w,"SEX"=s5)
#return(m)
#}
ali<-csvxtract("Alipurduarr.csv")
summary(ali)## Length Class Mode
## NUM 7364 -none- numericalip<-csvxxx("Alipurduarr.csv")
summary(alip)## age hb hba0 hba2 hbf
## 16 :143 11.3 : 43 218 : 29 0 :143 0 :272
## 13 :134 11.6 : 40 228 : 27 2.8 :102 0.2 :192
## 14 :120 11 : 39 225 : 26 2.6 : 98 0.3 :149
## 17 :115 10.7 : 35 226 : 26 2.7 : 96 0.4 : 78
## 15 :114 10.8 : 35 217 : 25 3 : 91 0.1 : 50
## 12 :111 12 : 35 221 : 24 (Other):519 (Other):310
## (Other):315 (Other):825 (Other):895 NA's : 3 NA's : 1
## mcv mch s5
## 72.3 : 13 22.9 : 26 F:598
## 70.5 : 12 21.7 : 22 M:454
## 71.3 : 12 21.8 : 20
## 71.8 : 11 20.2 : 19
## 72.9 : 11 20.9 : 19
## (Other):992 22.7 : 19
## NA's : 1 (Other):927dist1<-rep("Alipur",nrow(alip))
alim<-cbind(dist1,alip)
ban<-csvxtract("Bankura.csv")
summary(ban)## Length Class Mode
## NUM 8050 -none- numericbanp<-csvxxx("Bankura.csv")
summary(banp)## age hb hba0 hba2 hbf
## 13 :138 12.1 : 53 88.3 : 61 2.8 :174 0.2 :349
## 12 :132 12 : 52 87.8 : 52 2.9 :158 0.3 :266
## 10 :100 11.2 : 51 88 : 52 2.7 :149 0.4 :120
## 8 : 91 11.9 : 50 87.9 : 50 2.6 :128 0 :106
## 15 : 86 11.3 : 48 88.1 : 50 3 :125 0.1 : 90
## (Other):602 11.7 : 47 87.5 : 48 3.1 : 88 0.5 : 78
## NA's : 1 (Other):849 (Other):837 (Other):328 (Other):141
## mcv mch s5
## 72.7 : 17 21.6 : 31 F:458
## 71.8 : 14 22.4 : 30 M:692
## 70.7 : 13 22.3 : 27
## 72.5 : 13 21.7 : 26
## 70.6 : 11 22 : 26
## 71.9 : 11 22.1 : 26
## (Other):1071 (Other):984dist2<-rep("Bankura",nrow(banp))
banm<-cbind(dist2,banp)
bir<-csvxtract("Birbhummm.csv")
summary(bir)## Length Class Mode
## NUM 6629 -none- numericbirp<-csvxxx("Birbhummm.csv")
summary(birp)## age hb hba0 hba2 hbf
## 4 :207 11.4 : 60 87.9 : 61 2.9 :140 0.2 :370
## 3 :203 11.5 : 52 87.5 : 54 3 :128 0.3 :237
## 5 :137 11.6 : 38 87.6 : 54 3.2 :121 0.1 :139
## 6 : 93 11.8 : 36 87.8 : 52 3.1 :113 0.4 : 77
## 2 : 82 11.9 : 36 87 : 48 2.8 :106 0.5 : 54
## 8 : 75 10.6 : 35 87.7 : 48 2.7 : 82 0.7 : 30
## (Other):150 (Other):690 (Other):630 (Other):257 (Other): 40
## mcv mch s5
## 69.7 : 22 20.5 : 29 F:326
## 70.7 : 17 20.8 : 28 M:621
## 77.6 : 16 21 : 28
## 68.5 : 15 21.2 : 28
## 69.2 : 15 19.9 : 27
## 72.9 : 15 21.8 : 27
## (Other):847 (Other):780dist3<-rep("Birbhum",nrow(birp))
birm<-cbind(dist3,birp)
bur<-csvxtract("Burdwann.csv")
summary(bur)## Length Class Mode
## NUM 15806 -none- numericburp<-csvxxx("Burdwann.csv")
summary(burp)## age hb hba0 hba2 hbf
## 14 :352 11 : 100 88 : 136 2.6 :358 0.2 :956
## 13 :351 11.4 : 89 87.8 : 125 2.7 :352 0.3 :429
## 15 :327 11.7 : 77 87.9 : 113 2.8 :311 0.1 :424
## 12 :324 11.2 : 76 87.7 : 112 2.5 :288 0.4 :215
## 11 :258 11.1 : 73 87.6 : 105 2.9 :209 0.5 : 92
## 10 :188 11.5 : 72 (Other):1665 (Other):733 (Other):139
## (Other):458 (Other):1771 NA's : 2 NA's : 7 NA's : 3
## mcv mch s5
## 72.3 : 28 22 : 60 F: 522
## 72.5 : 21 22.1 : 48 M:1736
## 71 : 20 21.5 : 45
## 71.2 : 19 21.4 : 44
## 73.2 : 19 22.4 : 44
## 76 : 19 20.7 : 40
## (Other):2132 (Other):1977dist4<-rep("Burdwanr",nrow(burp))
burm<-cbind(dist4,burp)
d.dinaj<-csvxtract("D Dinajpur.csv")
summary(d.dinaj)## Length Class Mode
## NUM 14630 -none- numericd.dinajp<-csvxxx("D Dinajpur.csv")
summary(d.dinajp)## age hb hba0 hba2 hbf
## 13 :408 10.2 : 183 88.1 : 117 2.9 :333 0.2 :772
## 14 :368 10.8 : 171 87.8 : 114 2.8 :330 0.3 :558
## 12 :357 11.2 : 154 88 : 109 2.7 :279 0.4 :223
## 15 :282 11.6 : 152 88.3 : 103 3 :275 0.1 :194
## 11 :206 11.9 : 129 87.7 : 102 2.6 :177 0.5 :119
## 16 :173 10.6 : 123 88.2 : 101 (Other):695 0.6 : 72
## (Other):296 (Other):1178 (Other):1444 NA's : 1 (Other):152
## mcv mch s5
## 80.2 : 85 21.9 : 137 F: 694
## 81.2 : 73 20.8 : 125 M:1396
## 78.2 : 66 21.3 : 94
## 78.6 : 63 22.8 : 94
## 76.2 : 53 21.5 : 84
## 69.8 : 45 22.4 : 84
## (Other):1705 (Other):1472dist5<-rep("D Dinajpur",nrow(d.dinajp))
d.dinajm<-cbind(dist5,d.dinajp)
e.med<-csvxtract("E Medinipurr.csv")
summary(e.med)## Length Class Mode
## NUM 5264 -none- numerice.medp<-csvxxx("E Medinipurr.csv")
summary(e.medp)## age hb hba0 hba2 hbf
## 12 :153 12.8 : 32 82.7 : 37 2.6 : 89 0.5 :204
## 13 :135 12.7 : 29 82.2 : 35 2.7 : 88 0.6 :134
## 14 :116 12.6 : 28 82.4 : 30 2.5 : 79 0.4 :117
## 15 : 82 11.4 : 24 82.3 : 29 2.8 : 74 0.2 : 82
## 11 : 80 12.9 : 24 82.5 : 29 2.4 : 62 0.3 : 73
## 16 : 66 11.9 : 23 81.9 : 28 (Other):357 0.7 : 47
## (Other):120 (Other):592 (Other):564 NA's : 3 (Other): 95
## mcv mch s5
## 65 : 13 22.1 : 24 F:123
## 65.5 : 12 22.6 : 22 M:629
## 70.6 : 11 21.8 : 19
## 73.8 : 11 22.8 : 19
## 63.7 : 10 22 : 18
## 63.2 : 9 22.2 : 18
## (Other):686 (Other):632dist6<-rep("E Mednipur",nrow(e.medp))
e.medm<-cbind(dist6,e.medp)
kol<-csvxtract("Kolkataa.csv")
summary(kol)## Length Class Mode
## NUM 672 -none- numerickolp<-csvxxx("Kolkataa.csv")
summary(kolp)## age hb hba0 hba2 hbf
## 20 :24 13.3 : 4 88.8 :10 2.6 :17 0.1 :42
## 21 :18 13.5 : 4 88.2 : 8 2.4 :13 0.2 :30
## 22 :17 14.6 : 4 88.9 : 8 2.5 :12 0.3 :11
## 19 :12 14.7 : 4 88.5 : 7 2.7 :12 0.4 : 6
## 18 :10 10.9 : 3 88.6 : 7 2.3 :10 0.5 : 2
## 23 : 7 11.8 : 3 89 : 5 2.9 : 7 0.6 : 1
## (Other): 8 (Other):74 (Other):51 (Other):25 (Other): 4
## mcv mch s5
## 85.3 : 4 22.5 : 3 F:21
## 71.1 : 2 26.4 : 3 M:75
## 71.3 : 2 26.7 : 3
## 71.5 : 2 27.1 : 3
## 76 : 2 27.6 : 3
## 79.1 : 2 28.6 : 3
## (Other):82 (Other):78dist7<-rep("Kolkata",nrow(kolp))
kolm<-cbind(dist7,kolp)
mal<-csvxtract("Malda.csv")
summary(mal)## Length Class Mode
## NUM 7931 -none- numericmalp<-csvxxx("Malda.csv")
summary(malp)## age hb hba0 hba2 hbf
## 14 :156 11.7 : 71 86.9 : 36 2.7 :145 0.3 :336
## 12 :154 11 : 58 87.2 : 32 2.6 :144 0.4 :231
## 13 :146 11.9 : 58 87.4 : 25 2.5 :143 0.2 :214
## 15 :130 10.4 : 51 87.5 : 25 2.9 :102 0.5 :105
## 11 : 96 12 : 50 87.1 : 24 2.8 :101 0.6 : 68
## (Other):450 10.7 : 49 81.7 : 23 2.4 : 83 (Other):178
## NA's : 1 (Other):796 (Other):968 (Other):415 NA's : 1
## mcv mch s5
## 81.6 : 50 25.1 : 63 F:460
## 81.4 : 47 24.9 : 57 M:673
## 80 : 44 25.6 : 54
## 81.7 : 41 25.3 : 51
## 81 : 35 24.6 : 45
## 81.9 : 33 24.7 : 42
## (Other):883 (Other):821dist8<-rep("Malda",nrow(malp))
malm<-cbind(dist8,malp)
n.pgs<-csvxtract("N 24 Pgs.csv")
summary(n.pgs)## Length Class Mode
## NUM 1554 -none- numericn.pgsp<-csvxxx("N 24 Pgs.csv")
summary(n.pgsp)## age hb hba0 hba2 hbf
## 13 :38 11.9 : 14 87.7 : 17 2.7 :51 0.2 :82
## 11 :29 11.1 : 13 87.5 : 15 2.8 :39 0.3 :41
## 12 :28 11.6 : 13 87.3 : 13 2.6 :28 0.1 :38
## 10 :23 11.7 : 12 87.8 : 13 2.5 :21 0.4 :19
## 8 :21 11 : 10 87.6 : 12 2.9 :20 0.5 :17
## 9 :21 11.8 : 9 87.4 : 11 3 :16 0.6 : 9
## (Other):62 (Other):151 (Other):141 (Other):47 (Other):16
## mcv mch s5
## 67.2 : 5 21.3 : 10 F: 28
## 69.1 : 4 21.2 : 8 M:194
## 69.2 : 4 21.5 : 7
## 69.5 : 4 21.7 : 7
## 70.5 : 4 22.2 : 7
## 76.7 : 4 21.4 : 6
## (Other):197 (Other):177dist9<-rep("N 24 Pgs",nrow(n.pgsp))
n.pgsm<-cbind(dist9,n.pgsp)
w.med<-csvxtract("W_Medinipurrr.csv")
summary(w.med)## Length Class Mode
## NUM 4452 -none- numericw.medp<-csvxxx("W_Medinipurrr.csv")
summary(w.medp)## age hb hba0 hba2 hbf
## 10 : 77 11.8 : 35 2.8 : 32 2.8 : 65 0.3 :123
## 8 : 54 11.5 : 30 83.4 : 23 2.6 : 64 0.2 :121
## 13 : 50 12.1 : 30 2.9 : 22 2.7 : 64 0.5 : 84
## 15 : 48 11.3 : 27 2.7 : 19 2.9 : 51 0.6 : 78
## 9 : 48 11.6 : 27 83.2 : 19 3 : 45 0.4 : 54
## (Other):358 11.2 : 26 83.3 : 19 0.2 : 39 0.7 : 31
## NA's : 1 (Other):461 (Other):502 (Other):308 (Other):145
## mcv mch s5
## 74.5 : 10 21.2 : 20 F:356
## 71.4 : 9 21.1 : 18 M:280
## 75.1 : 9 21.9 : 18
## 67 : 8 21.3 : 17
## 68.7 : 8 20.8 : 15
## 70.5 : 8 23.6 : 14
## (Other):584 (Other):534dist10<-rep("W Mednipur",nrow(w.medp))
w.medm<-cbind(dist10,w.medp)
coo<-csvxtract("Coochbeharr.csv")
summary(coo)## Length Class Mode
## NUM 189 -none- numericcoop<-csvxxx("Coochbeharr.csv")
summary(coop)## age hb hba0 hba2 hbf mcv
## 11:3 10.8 : 2 84.5 : 3 3.4 :5 0 :13 72.4 : 3
## 12:3 11.2 : 2 84.6 : 3 3.2 :4 0.2 : 3 69.1 : 2
## 13:9 11.5 : 2 83.4 : 2 3.3 :3 0.4 : 3 73.1 : 2
## 14:7 11.6 : 2 84.2 : 2 3.5 :3 0.3 : 2 61.7 : 1
## 15:3 12.6 : 2 25.4 : 1 3.6 :3 0.1 : 1 64.7 : 1
## 16:2 12.9 : 2 5 : 1 3 :2 0.6 : 1 66.8 : 1
## (Other):15 (Other):15 (Other):7 (Other): 4 (Other):17
## mch s5
## 20.2 : 3 M:27
## 22.3 : 3
## 20.9 : 2
## 23.4 : 2
## 19 : 1
## 19.7 : 1
## (Other):15dist11<-rep("Coochbehar",nrow(coop))
coom<-cbind(dist11,coop)
how<-csvxtract("Howrahh.csv")
summary(how)## Length Class Mode
## NUM 315 -none- numerichowp<-csvxxx("Howrahh.csv")
summary(howp)## age hb hba0 hba2 hbf mcv
## 11:10 12.4 : 4 87.2 : 4 2.6 :8 0.1: 6 71.2 : 2
## 12: 9 11.7 : 3 88 : 4 2.7 :7 0.2:16 73.2 : 2
## 13: 4 13.4 : 3 87 : 3 2.9 :7 0.3:15 88.1 : 2
## 14:12 10.6 : 2 87.4 : 3 3.1 :7 0.4: 2 62.9 : 1
## 15: 6 11 : 2 87.6 : 3 2.8 :4 0.5: 2 67.9 : 1
## 16: 2 11.2 : 2 88.2 : 3 2.5 :3 0.6: 3 68.9 : 1
## 17: 2 (Other):29 (Other):25 (Other):9 0.8: 1 (Other):36
## mch s5
## 26.4 : 3 F: 8
## 21.2 : 2 M:37
## 21.3 : 2
## 22.1 : 2
## 22.5 : 2
## 22.6 : 2
## (Other):32dist12<-rep("Howrah",nrow(howp))
howm<-cbind(dist12,howp)
jal<-csvxtract("Jalpaiguri.csv")
summary(jal)## Length Class Mode
## NUM 4655 -none- numericjalp<-csvxxx("Jalpaiguri.csv")
summary(jalp)## age hb hba0 hba2 hbf
## 8 : 99 11.4 : 35 85 : 39 2.9 : 98 0.2 :188
## 10 : 91 11.3 : 29 82 : 35 2.8 : 93 0.3 : 84
## 6 : 61 12 : 27 83 : 32 2.7 : 78 0 : 73
## 9 : 56 12.1 : 27 89 : 26 3 : 66 0.4 : 69
## 5 : 54 11.8 : 26 84 : 24 3.1 : 60 0.9 : 36
## 13 : 47 12.3 : 25 88 : 24 (Other):265 0.1 : 31
## (Other):257 (Other):496 (Other):485 NA's : 5 (Other):184
## mcv mch s5
## 70.5 : 10 22.5 : 18 F:264
## 71.3 : 10 23 : 18 M:401
## 71.4 : 10 22.7 : 17
## 71.7 : 10 22.6 : 16
## 71.1 : 8 23.1 : 16
## 68.7 : 7 21.6 : 15
## (Other):610 (Other):565dist13<-rep("Jalpaiguri",nrow(jalp))
jalm<-cbind(dist13,jalp)
mur<-csvxtract("Murshidabadd.csv")
summary(mur)## Length Class Mode
## NUM 2520 -none- numericmurp<-csvxxx("Murshidabadd.csv")
summary(murp)## age hb hba0 hba2 hbf
## 13 : 49 11 : 15 86.8 : 15 2.7 : 49 0.3 :100
## 14 : 44 10.2 : 14 87 : 13 3 : 43 0.2 : 97
## 15 : 44 10.7 : 12 87.1 : 13 2.9 : 40 0.4 : 54
## 12 : 40 13.9 : 12 87.3 : 13 2.6 : 38 0.5 : 27
## 16 : 39 10 : 11 87.5 : 13 2.8 : 35 0.9 : 14
## 11 : 36 11.1 : 10 87.8 : 12 3.1 : 32 (Other): 67
## (Other):108 (Other):286 (Other):281 (Other):123 NA's : 1
## mcv mch s5
## 80 : 10 24 : 11 F: 81
## 82.9 : 7 25.3 : 11 M:279
## 81.6 : 6 25.9 : 8
## 81.7 : 6 21.1 : 7
## 82.5 : 6 25.1 : 7
## 90.4 : 5 25.4 : 7
## (Other):320 (Other):309dist14<-rep("Murshidabad",nrow(murp))
murm<-cbind(dist14,murp)
##biplot sir er function diye hoye ja6ae
maspar<-rbind(ali$NUM,ban$NUM,bir$NUM,bur$NUM,d.dinaj$NUM,e.med$NUM,kol$NUM,mal$NUM,n.pgs$NUM,w.med$NUM,coo$NUM,how$NUM,jal$NUM,mur$NUM)
masr1<-na.omit(maspar)
#ik<-is.na(maspar)
pca<-prcomp(masr1,scale= TRUE)
biplot(pca)
#ij<-is.na(masr1)
masterfin<-rbind(alip,banp,birp,burp,d.dinajp,e.medp,kolp,malp,n.pgsp,w.medp,coop,howp,jalp,murp)
masterfina<-na.omit(masterfin)
masterfinal<-data.frame(masterfina)
colnames(masterfinal)<-c("AGE","HB","HBA0","HBA2","HBF","MCV","MCH","SEX")
gclass5<-masterfinal$SEX
library("ggbiplot")## Loading required package: ggplot2## Loading required package: scales## Loading required package: gridg<-ggbiplot(pca,obs.scale = 0.3,var.scale = 0.3,groups = gclass5 ,ellipse = TRUE,circle = TRUE,pc.biplot = TRUE)
g<-g+theme(legend.direction = 'horizontal',legend.position = 'top')
print(g)
distm<-rbind(alim,banm,birm,burm,d.dinajm,e.medm,kolm,malm,n.pgsm,w.medm,coom,howm,jalm,murm)
distma<-na.omit(distm)
distmas<-data.frame(distma)
colnames(distmas)<-c("DIST","AGE","HB","HBA0","HBA2","HBF","MCV","MCH","SEX")
gclass6<-distmas$DIST
library("ggbiplot")
g<-ggbiplot(pca,obs.scale = 0.3,var.scale = 0.3,groups = gclass6 ,ellipse = TRUE,circle = TRUE,pc.biplot = TRUE)
g<-g+theme(legend.direction = 'horizontal',legend.position = 'top')
print(g)
library("ggbiplot")
g<-ggbiplot(pca,choices = 1:2,obs.scale = 0.3,var.scale = 0.3,groups = gclass5 ,ellipse = TRUE,circle = TRUE,pc.biplot = TRUE,alpha = 1, var.axes = TRUE)
g<-g+xlim(-5,5)
g<-g+ylim(-5,5)
g<-g+theme(legend.direction = 'horizontal',legend.position = 'top')
print(g)## Warning: Removed 46 rows containing missing values (geom_point).
library("ggbiplot")
g<-ggbiplot(pca,choices = 1:2,obs.scale = 0.3,var.scale = 0.3,groups = gclass6 ,ellipse = TRUE,circle = TRUE,pc.biplot = TRUE,alpha = 1, var.axes = TRUE)
g<-g+xlim(-5,5)
g<-g+ylim(-5,5)
g<-g+theme(legend.direction = 'horizontal',legend.position = 'top')
print(g)## Warning: Removed 46 rows containing missing values (geom_point).
##CONCLUSION * The biplot shows that mcv, mch and hb are very much related to each other.Hence they are contribiting factors. * Age has very less co-relation to all other components and does not directly affects any component to a large extent. * Hbf is likely to be a independent component of the analysis. * From the ggbiplot which seperates components on the distinction of male and female patients the components seem to be likely be the same as * the biplot without much variance except the fact that the female popuplation have a high correlation of mcv to mch than male. * Also the age becomes more important in case of females with aspect of all the components correlation.
The most distinct factor here are the outliers that are found which may be either or considered as very special scenarios having very rare conditions for both the male and female conditions.
- Further this project could be extended to focus on finding this outliers or rare clinically important conditions because they are purely independent of each other.
- For the time being this project concludes with the fact that mcv,mch and hb are dependent variables and hbf is the most independent variable component.
Also age and sex(M/F) are moderately significant for the other components except females have a tendency to have more mcv and mch with increase in age as suggested from the ggbiplot.