bethany reads

GSM1462461,GSM1462462,GSM1462463,GSM1462464,GSM1462465,GSM1462466,GSM1462467,GSM1462468,GSM1462469,GSM1462470,GSM1589798,GSM1589799
BCL6_REP1_ChIPSeq,BCL6_REP1_INPUT,H3K4me1_REP1_ChIPSeq,H3K4me1_REP2_ChIPSeq,H3K4me3_REP1_ChIPSeq,H3K4me3_REP2_ChIPSeq,H3K27ac_REP1_ChIPSeq,H3K27ac_REP2_ChIPSeq,INPUT1_ChIPSeq,INPUT2_ChIPSeq,BCL6_REP2_ChIPSeq,BCL6_REP2_INPUT
200,200,200,200,200,200,200,200,200,200,200,200

[zfu@login-0-0 src]$ python mergeAllLowCoverageRegions.py Sample_9-1 ../02.Mapping/9-1_coverage_cutoff10_merged.tsv ../../Merge.LowCoverage.tsv ../../00.RawData/R6_Feature.bed
../../Merge.LowCoverage.tsv  do not exist!
Sample_9-1     2174     36149

[zfu@login-0-0 src]$
[zfu@login-0-0 src]$ python mergeAllGaps.py Sample_9-1 ../02.Mapping/9-1_sorted_0Coverage.bedGraph ../../Merged.Gaps.tsv ../../00.RawData/R6_Feature.bed
../../Merged.Gaps.tsv  do not exist!
Traceback (most recent call last):
  File "mergeAllGaps.py", line 61, in <module>
    if (start >= Feature_Hash[gene][0]) and (start <= Feature_Hash[gene][1]):
NameError: name 'start' is not defined
[zfu@login-0-0 src]$
[zfu@login-0-0 src]$ python mergeSNPs.py Sample_9-1 ../03.Freebayes/9-1_snpInfo.tsv ../../Merge.SNPs.tsv ../../00.RawData/R6_Feature.bed 'gi|15902044|ref|NC_003098.1|:'
../../Merge.SNPs.tsv  do not exist!
Sample_9-1     41857

[zfu@login-0-0 src]$
[zfu@login-0-0 src]$ python mergeSNPs.py Sample_9-1 ../03.Freebayes/9-1_snpInfo.filtered.tsv ../../Merge.SNPs.filtered.tsv ../../00.RawData/R6_Feature.bed 'gi|15902044|ref|NC_003098.1|:'
../../Merge.SNPs.filtered.tsv  do not exist!
Sample_9-1     11255

[zfu@login-0-0 src]$ python mergeAllGaps.py Sample_9-1 ../02.Mapping/9-1_sorted_0Coverage.bedGraph ../../Merged.Gaps.tsv ../../00.RawData/R6_Feature.bed
../../Merged.Gaps.tsv  do exist!
Sample_9-1     1004     177935

Loglinear for Drinking Data (Reshape the original MBdrink dataframe)

> library(icda)
> data(MBdrink)
> MBdrink
EI SN TF JP Drink Count
1 E S T J Often 10
2 E S T P Often 8
3 E S F J Often 5
4 E S F P Often 7
5 E S T J Rarely 67
6 E S T P Rarely 34
7 E S F J Rarely 101
8 E S F P Rarely 72
9 E N T J Often 3
10 E N T P Often 2
11 E N F J Often 4
12 E N F P Often 15
13 E N T J Rarely 20
14 E N T P Rarely 16
15 E N F J Rarely 27
16 E N F P Rarely 65
17 I S T J Often 17
18 I S T P Often 3
19 I S F J Often 6
20 I S F P Often 4
21 I S T J Rarely 123
22 I S T P Rarely 49
23 I S F J Rarely 132
24 I S F P Rarely 102
25 I N T J Often 1
26 I N T P Often 5
27 I N F J Often 1
28 I N F P Often 6
29 I N T J Rarely 12
30 I N T P Rarely 30
31 I N F J Rarely 30
32 I N F P Rarely 73
> library(reshape2)
> New.MBdrink <- dcast(MBdrink, EI + SN + TF + JP ~ Drink, sum)
Using Count as value column: use value.var to override.
> New.MBdrink$total <- New.MBdrink$Rarely + New.MBdrink$Often
> New.MBdrink
EI SN TF JP Rarely Often total
1 E S T J 67 10 77
2 E S T P 34 8 42
3 E S F J 101 5 106
4 E S F P 72 7 79
5 E N T J 20 3 23
6 E N T P 16 2 18
7 E N F J 27 4 31
8 E N F P 65 15 80
9 I S T J 123 17 140
10 I S T P 49 3 52
11 I S F J 132 6 138
12 I S F P 102 4 106
13 I N T J 12 1 13
14 I N T P 30 5 35
15 I N F J 30 1 31
16 I N F P 73 6 79
> Drink.Indep <- glm(total ~ EI + SN + TF + JP, family=poisson, data=New.MBdrink)
> summary(Drink.Indep)

Call:
glm(formula = total ~ EI + SN + TF + JP, family = poisson, data = New.MBdrink)

Deviance Residuals:
Min 1Q Median 3Q Max
-4.3550 -2.1182 -1.0628 0.8506 5.7457

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 4.17712 0.07054 59.216 < 2e-16 ***
EII 0.26439 0.06226 4.246 2.17e-05 ***
SNN -0.87008 0.06765 -12.861 < 2e-16 ***
TFF 0.48551 0.06355 7.640 2.17e-14 ***
JPP -0.12971 0.06185 -2.097 0.036 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for poisson family taken to be 1)

Null deviance: 399.94 on 15 degrees of freedom
Residual deviance: 135.87 on 11 degrees of freedom
AIC: 238.7

Number of Fisher Scoring iterations: 4

R code of GLMs for Court Data (Poisson Distribution)

> library(icda)
> data(wafers)
> wafers.loglin <- glm(defects ~ trt + thickness, family = poisson(link="log"),data=wafers)
> summary(wafers.loglin)

Call:
glm(formula = defects ~ trt + thickness, family = poisson(link = "log"),
data = wafers)

Deviance Residuals:
Min 1Q Median 3Q Max
-1.2952 -0.6785 -0.2688 0.6776 1.6307

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.7177 0.1602 10.719 < 2e-16 ***
trtB 0.5878 0.1764 3.332 0.000861 ***
thicknesshigh -0.2296 0.1701 -1.349 0.177246
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for poisson family taken to be 1)

Null deviance: 27.857 on 19 degrees of freedom
Residual deviance: 14.435 on 17 degrees of freedom
AIC: 94.517

Number of Fisher Scoring iterations: 4

> anova(wafers.loglin, test="Chisq")

Analysis of Deviance Table

Model: poisson, link: log

Response: defects
Terms added sequentially (first to last)

Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 19 27.857
trt 1 11.5894 18 16.268 0.0006633 ***
thickness 1 1.8326 17 14.435 0.1758239
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


> wafCI.LR <- confint(wafers.loglin)
> wafCI.LR
2.5 % 97.5 %
(Intercept) 1.3903205 2.0193444
trtB 0.2469096 0.9400962
thicknesshigh -0.5659614 0.1025576
> wafCI.Wald <- confint.default(wafers.loglin)
> wafCI.Wald
2.5 % 97.5 %
(Intercept) 1.4035823 2.0317207
trtB 0.2420819 0.9334915
thicknesshigh -0.5630535 0.1039046

no PUPILTime in java.library.path

Exception in thread "main" java.lang.UnsatisfiedLinkError: no PUPILTime in java.library.path

If you see the above error information you should check your LD_LIBRARY_PATH:

export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}\:${PUPIL_PATH}/lib

Make Sander.PUPIL

if you see this error message

make: *** No rule to make target `mdread.PUPIL.o', needed by `sander.PUPIL'. Stop.

then do

make depend

and then

make SANDER.PUPIL

Java compile issue in installing pupil 2.0 at HPC

HPC submit nodes do not have enough memory to carry out JAVA VM, therefore we need to do:

SSH -Y TEST04