Problem to read Numeric data

Hi,
I'm struggling to get my numeric data to read in MVP.
I prepared the numeric format based on the instructions at https://github.com/XiaoleiLiuBio/MVP
It looks like this (Just a portion shown here. The total number of row/SNP is 52157 and total number of column/individual is 68), no header and row names:
NaN | NaN | NaN | NaN | NaN | NaN | NaN
0 | 0 | 0 | 0 | 2 | 0 | NaN
2 | 2 | 2 | 2 | 2 | 2 | 0
2 | 2 | 2 | 2 | 2 | 2 | 2
2 | 0 | 2 | 2 | 2 | 0 | 0
0 | NaN | 0 | 0 | 0 | 2 | 2
NaN | NaN | NaN | NaN | NaN | NaN | NaN
0 | 0 | 0 | 0 | 0 | 0 | 0
I understand that there shouldn't be NaN or missing data in numeric format. I don't know how to overcome this. Could you please help me?
I also got this error:
Error in big.matrix(nrow = numRows, ncol = createCols, type = type, dimnames = list(rowNames,  :
A big.matrix must have at least one row and one column
I used this code to try to fix:
fileNum=matrix(1:3546676,ncol=68)
But still got the same error.
May I ask what should I do?
Appreciate your help.
Many thanks.
Regards,
Tina

Hi, Tina
First, you should do quality control and impute before you do GWAS.
your numeric file is separated by ' | ', you need to replace it with tab. I always recommend using a text editor (VSCode, NotePad++, or whatever you like) to format your data when dealing with small data.
Then delete the mark with too high the miss rate(>10%) and the mark with the low MAF value(<0.05), and then impute it to make sure there is no missing in your genotype file.
I usually do quality control and imputation from files in Plink format or VCF format. I don't know if there is a tool to do quality control and impute on data in numeric format. There are two functions MVP.Data.impute and MVP.Data.QC in the devel branch, you can find a way to read your file into R, then refer to these two functions.
https://github.com/XiaoleiLiuBio/MVP/blob/370bf6bab733f03b970dc6a33c83850f43fbabd6/R/MVP.Data.r#L501
https://github.com/XiaoleiLiuBio/MVP/blob/370bf6bab733f03b970dc6a33c83850f43fbabd6/R/MVP.Data.r#L552
Haohao

Hi Haohao,
Many thanks for the suggestions, really appreciate it. Yes, I will do the
filtering and QC.
My only concern is that my SNP genotype data is in AA, BB and AB format. I
don't have the SNP allele information for each individual at each SNP
example CC, TT, AG, AT etc.
I'm manually converting the AA=0, BB=2 and AB=1 based on GAPIT user manual,
using Excel. The remaining NN has to be imputed but I don't know how to do
that so far, still looking at how to solve this problem.
Thank you very much for your help.
Cheers,
Tina Neik
PhD candidate (Batley Lab)
School of Biological Sciences (M092)
The University of Western Australia
Crawley WA 6009
Email<EMAIL_ADDRESS>Mobile: 04112 10 334
On Tue, 6 Nov 2018 at 12:03, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
First, you should do quality control and impute before you do GWAS.
your numeric file is separated by ' | ', you need to replace it with tab.
I always recommend using a text editor (VSCode, NotePad++, or whatever you
like) to format your data when dealing with small data.
Then delete the mark with too high the miss rate(>10%) and the mark with
the low MAF value(<0.05), and then impute it to make sure there is no
missing in your genotype file.
I usually do quality control and imputation from files in Plink format or
VCF format. I don't know if there is a tool to do quality control and
impute on data in numeric format. There are two functions MVP.Data.impute
and MVP.Data.QC in the devel branch, you can find a way to read your file
into R, then refer to these two functions.
https://github.com/XiaoleiLiuBio/MVP/blob/370bf6bab733f03b970dc6a33c83850f43fbabd6/R/MVP.Data.r#L501
https://github.com/XiaoleiLiuBio/MVP/blob/370bf6bab733f03b970dc6a33c83850f43fbabd6/R/MVP.Data.r#L552
Haohao
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Hi, Tina
Sorry to reply to you so late.
I recommend that you convert the original file to a format supported by Plink, such as .ped and .map(see https://www.cog-genomics.org/plink2/formats), so that you can use more software to do data preprocessing.
I show you my qc and impute practice.

qc with plink

plink --file <your_prefix> --geno 0.1 --maf 0.05 --recode vcf --out mydata.qc

impute with beagle

java -Xmx8g -jar beagle.28Sep18.793.jar gt=mydata.qc.vcf out=mydata.qc.imp

you can download beagle from https://faculty.washington.edu/browning/beagle/beagle.html

qc with plink again

plink --vcf mydata.qc.imp --maf 0.05 --make-bed --out mydata.clean

Then you get the clean data  in bfile format, MVP can use it.
If there is anything I have not stated, you can point out that we can discuss it again.
Haohao

Many thanks, Haohao for the suggestions. I will try them out and let you
know if it works.
Meanwhile, I also see that some people say we shouldn't be doing filtering
before imputation, like here:
"We recommend that you run imputation before stringent filtering "
https://groups.google.com/forum/#!topic/tassel/GUjzv78oEoo
"Additionally, you shouldn't be filtering before running find haplotypes.
All of the sites are informative, and should be included."
https://groups.google.com/forum/#!topic/tassel/7ve-vcv42z0
From the same person though.
May I ask, what are your thoughts on these?
Thank you.
Cheers,
Tina Neik
PhD candidate (Batley Lab)
School of Biological Sciences (M092)
The University of Western Australia
Crawley WA 6009
Email<EMAIL_ADDRESS>Mobile: 04112 10 334
On Wed, 7 Nov 2018 at 14:08, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
Sorry to reply to you so late.
I recommend that you convert the original file to a format supported by
Plink, such as .ped and .map(see
https://www.cog-genomics.org/plink2/formats), so that you can use more
software to do data preprocessing.
I show you my qc and impute practice.

qc with plink

plink --file <your_prefix> --geno 0.1 --maf 0.05 --recode vcf --out mydata.qc

impute with beagle

java -Xmx8g -jar beagle.28Sep18.793.jar gt=mydata.qc.vcf out=mydata.qc.imp
you can download beagle from
https://faculty.washington.edu/browning/beagle/beagle.html

qc with plink again

plink --vcf mydata.qc.imp --maf 0.05 --make-bed --out mydata.clean
Then you get the clean data in bfile format, MVP can use it.
If there is anything I have not stated, you can point out that we can
discuss it again.
Haohao
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Hi Haohao,
First of all, I can't seem to find a way to convert my SNP data (a portion
attached in this email) into .ped and .map format. I think it is because I
have AA, BB and AB as my genotype instead of AT, CG etc.
Unfortunately, I don't have access to the original SNP data (AT, CG etc).
I tried to use TASSEL to convert the attached SNP data into .ped and .map
format but it cannot recognise it as a genotype table.
Do you think you have a way to solve this problem?
I would appreciate if you could help me here, with this first step.
Thank you very much, in advance.
Cheers,
Tina Neik
PhD candidate (Batley Lab)
School of Biological Sciences (M092)
The University of Western Australia
Crawley WA 6009
Email<EMAIL_ADDRESS>Mobile: 04112 10 334
On Wed, 7 Nov 2018 at 14:58, Ting Neik<EMAIL_ADDRESS>wrote:

Many thanks, Haohao for the suggestions. I will try them out and let you
know if it works.
Meanwhile, I also see that some people say we shouldn't be doing filtering
before imputation, like here:
"We recommend that you run imputation before stringent filtering "
https://groups.google.com/forum/#!topic/tassel/GUjzv78oEoo
"Additionally, you shouldn't be filtering before running find haplotypes.
All of the sites are informative, and should be included."
https://groups.google.com/forum/#!topic/tassel/7ve-vcv42z0
From the same person though.
May I ask, what are your thoughts on these?
Thank you.
Cheers,
Tina Neik
PhD candidate (Batley Lab)
School of Biological Sciences (M092)
The University of Western Australia
Crawley WA 6009
Email<EMAIL_ADDRESS>Mobile: 04112 10 334
On Wed, 7 Nov 2018 at 14:08, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
Sorry to reply to you so late.
I recommend that you convert the original file to a format supported by
Plink, such as .ped and .map(see
https://www.cog-genomics.org/plink2/formats), so that you can use more
software to do data preprocessing.
I show you my qc and impute practice.

qc with plink

plink --file <your_prefix> --geno 0.1 --maf 0.05 --recode vcf --out mydata.qc

impute with beagle

java -Xmx8g -jar beagle.28Sep18.793.jar gt=mydata.qc.vcf out=mydata.qc.imp
you can download beagle from
https://faculty.washington.edu/browning/beagle/beagle.html

qc with plink again

plink --vcf mydata.qc.imp --maf 0.05 --make-bed --out mydata.clean
Then you get the clean data in bfile format, MVP can use it.
If there is anything I have not stated, you can point out that we can
discuss it again.
Haohao
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Hi, Tina
I think it's hard to find the optimal threshold for quality control, depending on your propensity to analyze. Often you need to try multiple thresholds when analyzing. I usually use a slightly stricter quality control. If I don't detect the signal, I will relax the quality control condition a bit.

MAF
I usually use --maf 0.01 first, but I noticed that you only have 68 individuals, so I suggest you use strict conditions like --maf 0.05 or --maf 0.1. When a certain type of sample is too small, it cannot represent the population. So the smaller your sample size, the more stringent quality control you need for MAF.
And the mark with small MAF value will have low filling accuracy. So I usually filter out these marker before filling.

Bouwman A C, Veerkamp R F. Consequences of splitting whole-genome sequencing effort over multiple breeds on imputation accuracy[J]. BMC Genetics, BioMed Central, 2014, 15(1): 105.

missing
For the same batch of data, markers with too high a miss rate are usually considered to be of low quality, and are more likely to be filtered out in order to avoid false positives.

In short, I think that the value of quality control conditions depends largely on your data. The more lenient the quality control conditions, the higher the probability of detecting a signal and the higher the false positive.
Haohao

Can you simply replace 'B' with 'T' to make TASSEL recognize your data? What the original SNP does not affect the analysis of GWAS.

Hi Haohao,
Many thanks for the suggestion.
I tried replacing 'B' with 'T' but still TASSEL cannot recognise my data.
Attached is the print screen of the error.
I really don't know what's going on.
Appreciate your input.
Thank you very much in advance.
Cheers,
Tina Neik
PhD candidate (Batley Lab)
School of Biological Sciences (M092)
The University of Western Australia
Crawley WA 6009
Email<EMAIL_ADDRESS>Mobile: 04112 10 334
On Thu, 8 Nov 2018 at 02:45, haohao<EMAIL_ADDRESS>wrote:

Can you simply replace 'B' with 'T' to make TASSEL recognize your data?
What the original SNP does not affect the analysis of GWAS.
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Many thanks, Haohao.
Sorry, I think I didn't frame my question clearly. My question was, do we
do QC before imputing the missing SNP?
I read from other discussion that we should first impute the missing SNP,
then only do QC.
What are your thoughts?
Thank you.
Cheers,
Tina Neik
PhD candidate (Batley Lab)
School of Biological Sciences (M092)
The University of Western Australia
Crawley WA 6009
Email<EMAIL_ADDRESS>Mobile: 04112 10 334
On Thu, 8 Nov 2018 at 02:41, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
I think it's hard to find the optimal threshold for quality control,
depending on your propensity to analyze. Often you need to try multiple
thresholds when analyzing. I usually use a slightly stricter quality
control. If I don't detect the signal, I will relax the quality control
condition a bit.

MAF
I usually use --maf 0.01 first, but I noticed that you only have 68
individuals, so I suggest you use strict conditions like --maf 0.05 or --maf
0.1. When a certain type of sample is too small, it cannot represent
the population. So the smaller your sample size, the more stringent quality
control you need for MAF.
And the mark with small MAF value will have low filling accuracy. So
I usually filter out these marker before filling.

[image: image]
https://user-images.githubusercontent.com/9551197/48152086-ef0cd900-e2fd-11e8-9fcd-6eaaeeef8ac9.png
Bouwman A C, Veerkamp R F. Consequences of splitting whole-genome
sequencing effort over multiple breeds on imputation accuracy[J]. BMC
Genetics, BioMed Central, 2014, 15(1): 105.

missing
For the same batch of data, markers with too high a miss rate are
usually considered to be of low quality, and are more likely to be filtered
out in order to avoid false positives.

In short, I think that the value of quality control conditions depends
largely on your data. The more lenient the quality control conditions, the
higher the probability of detecting a signal and the higher the false
positive.
Haohao
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Thank you, Haohao.
Do you have any idea how I can overcome the replacement of 'B' with 'T' but
still didn't work problem?
Many thanks in advance.
Cheers,
Tina
On Thu, 8 Nov 2018 at 13:23, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
Sorry, my bad English make you confused. Regarding whether you should do
qc before impute, my answer is "YES, you should to control maf and missing
rate before impute."
Because marker with low maf and high missing rate are more error-prone
when imputing.
Haohao
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Hi, Tina
If your data is organized into a 0, 1, 2 matrix that can be read directly into R, you can follow the following code to get a set of bfile formatted data accepted by plink.
# get bed file.
> suppressMessages(library(MVP))
> geno <- as.matrix(read.table('data.txt', sep='|'))
> geno
      V1  V2  V3  V4  V5  V6  V7
[1,] NaN NaN NaN NaN NaN NaN NaN
[2,]   0   0   0   0   2   0 NaN
[3,]   2   2   2   2   2   2   0
[4,]   2   2   2   2   2   2   2
[5,]   2   0   2   2   2   0   0
[6,]   0 NaN   0   0   0   2   2
[7,] NaN NaN NaN NaN NaN NaN NaN
[8,]   0   0   0   0   0   0   0
> geno[is.nan(geno)] <- NA
> geno
     V1 V2 V3 V4 V5 V6 V7
[1,] NA NA NA NA NA NA NA
[2,]  0  0  0  0  2  0 NA
[3,]  2  2  2  2  2  2  0
[4,]  2  2  2  2  2  2  2
[5,]  2  0  2  2  2  0  0
[6,]  0 NA  0  0  0  2  2
[7,] NA NA NA NA NA NA NA
[8,]  0  0  0  0  0  0  0
> write_bfile(as.big.matrix(geno)@address, 'plink')

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
# get fam file.
> pheno <- read.table('pheno.txt', header=T)
> pheno
  ID Pheno1
1  1   51.9
2  2   49.8
3  3   30.1
4  4   45.4
5  5   35.4
6  6   40.2
7  7   49.1
> fam <- cbind(pheno$ID, pheno$ID, 0, 0, 0, pheno$Pheno1)
> write.table(fam, 'plink.fam', quote = F, row.names = F, col.names = F, sep = '\t')

# get bim file.
> map <- read.table('map.txt', header=T)
> map
  SNP Chr  Pos
1 rs1   1 1001
2 rs2   1 2019
3 rs3   1 3221
4 rs4   1 4400
5 rs5   1 5550
6 rs6   1 6190
7 rs7   1 7990
8 rs8   1 9999
> bim <- cbind(map[, 2], map[, 1], 0, map[, 3], 0, 0)
> write.table(bim, paste0(out, '.bim'), quote = F, row.names = F, col.names = F, sep = '\t')

you will can get plink.bed, plink.fam, plink.bim. now you can use plink --bfile plink to access your data.
Cheers,
Haohao

Hi Haohao,
I tried MVP again using my original SNP genotype data in AA, AB and BB
format and it works! It can be read in MVP and I think MVP will do the
numeric conversion automatically because I see this: [1] "Output numeric
genotype..."
First, may I ask why is there an error at the end of the run? What does it
mean?
Rerun with Debug
Error in filebacked.big.matrix(nrow = nrow, ncol = ncol, type = type,  :
A big.matrix must have at least one row and one column
Second, why is the number of individuals reduced from 68 to 65?
It became like this in Rlm7mvp.hmp.phe:
Taxa Rlm7 X X.1 X.2 X.3
Darmor 4 NA NA NA NA
AgSpectrum 4 NA NA NA NA
Yudal 4 NA NA NA NA
Surpass400_024DH 4 NA NA NA NA
Argyle 4 NA NA NA NA
Taparoo 4 NA NA NA NA
Skipton 4 NA NA NA NA
Monty_028DH 4 NA NA NA NA
Dunkeld 4 NA NA NA NA
Telfer 4 NA NA NA NA
While it should be like this in the original
data Phenotype_Rlm7_68LinesAll.txt:
Taxa Rlm7
Atenzo_1 4
Atenzo_2 4
Atenzo_3 1
Atenzo_4 1
DKExpertise_1 1
DKExpertise_2 1
Andesson_1 1
DKExstorm_1 1
Excel_1 1
Anisse_1 1
Any suggestions will be most appreciated. Thank you very much.

MVP.Data(fileHMP =
"~/Dropbox/Rlm7_SNP_68lines_forNumericAll_forTASSEL.hmp.txt", filePhe =
"~/Dropbox/Phenotype_Rlm7_68LinesAll.txt", sep.hmp = "\t", sep.phe = "\t",
SNP.effect="Add", fileKin = FALSE, filePC = FALSE, out = "Rlm7mvp.hmp")
[1] "Preparing data for MVP..."
[1] "Preparation for PHENOTYPE data is done!"
[1] "Output numeric genotype..."
[1] "File: ~/Dropbox/Rlm7_SNP_68lines_forNumericAll_forTASSEL.hmp.txt ;
Total markers:  52099  finished!"
[1] "Preparation for numeric data is done!"
[1] "Output mvp genotype..."
[1] "MVP data prepration accomplished successfully!"
genotype <- attach.big.matrix("Rlm7mvp.hmp.geno.desc")
phenotype <- read.table("Rlm7mvp.hmp.phe",head=TRUE)
map <- read.table("Rlm7mvp.hmp.map" , head = TRUE)
for(i in 2:ncol(phenotype)){

imMVP <- MVP(

    phe=phenotype[, c(1, i)],

    geno=genotype,

    map=map,

    #K=Kinship,

    #CV.GLM=Covariates,

    #CV.MLM=Covariates,

    #CV.FarmCPU=Covariates,

    nPC.GLM=3,

    nPC.MLM=3,

    nPC.FarmCPU=3,

    perc=1,

    priority="speed",

    ncpus=10,

    vc.method="EMMA",

    maxLoop=10,

    method.bin="FaST-LMM",#"FaST-LMM","EMMA", "static"

    #permutation.threshold=TRUE,

    #permutation.rep=100,

    threshold=0.05,

    method=c("GLM", "MLM")

)}

#--------------------------------------------Welcome to
MVP---------------------------------------------#
A Memory-efficient, Visualization-enhanced, and
Parallel-accelerated Tool For GWAS           #
Version: 1.0.1
                         #

Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,
Shuhong Zhao, and Xiaolei Liu #
Contact<EMAIL_ADDRESS>                         #

#-------------------------------------------------------------------------------------------------------#
[1] "Input data has 63 individuals, 52099 markers"
[1] "Principal Component Analysis Start..."
means for first 10 snps:
[1] 0 0 0 0 0 0 0 0 0 0
[1] "GWAS Start..."
[1] "General Linear Model (GLM) Start ..."
[1] "Mixed Linear Model (MLM) Start ..."
[1] "Calculating Kinship..."
[1] "Variance components..."
[1] "Variance Components Estimation is Done!"
[1] "Eigen-Decomposition..."
[1] "Eigen-Decomposition is Done!"
[1] "Significance Level: 9.59711318835294e-07"
[1] "Visualization Start..."
[1] "Phenotype distribution Plotting..."
[1] "PCA plot2d..."
[1] "PCA plot3d..."
Writing 'MVP.3D090.png'
Loading required namespace: magick
Writing 'MVP.3D045.png'
[1] "SNP_Density Plotting..."
[1] "Circular_Manhattan Plotting Rlm7.GLM..."
[1] "Circular_Manhattan Plotting Rlm7.MLM..."
[1] "Rectangular_Manhattan Plotting Rlm7.GLM..."
[1] "Rectangular_Manhattan Plotting Rlm7.MLM..."
[1] "Q_Q Plotting Rlm7.GLM..."
[1] "Q_Q Plotting Rlm7.MLM..."
[1] "Multracks_Rectangular Plotting Rlm7.GLM..."
[1] "Multracks_Rectangular Plotting Rlm7.MLM..."
[1] "Multraits_Rectangular Plotting..."
[1] "Multracks_QQ Plotting Rlm7.GLM..."
[1] "Multracks_QQ Plotting Rlm7.MLM..."
[1] "Multraits_QQ Plotting Rlm7.GLM..."
[1] "Multraits_QQ Plotting Rlm7.MLM..."
#-------------------------------------------MVP
ACCOMPLISHED--------------------------------------------#
#--------------------------------------------Welcome to
MVP---------------------------------------------#
A Memory-efficient, Visualization-enhanced, and
Parallel-accelerated Tool For GWAS           #
Version: 1.0.1
                         #

Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,
Shuhong Zhao, and Xiaolei Liu #
Contact<EMAIL_ADDRESS>                         #

#-------------------------------------------------------------------------------------------------------#
Show Traceback
Rerun with Debug
Error in filebacked.big.matrix(nrow = nrow, ncol = ncol, type = type,  :
A big.matrix must have at least one row and one column
Cheers,
Tina Neik
PhD candidate (Batley Lab)
School of Biological Sciences (M092)
The University of Western Australia
Crawley WA 6009
Email<EMAIL_ADDRESS>Mobile: 04112 10 334
On Thu, 8 Nov 2018 at 21:24, Ting Neik<EMAIL_ADDRESS>wrote:

Thank you very much, Haohao.
I will need some time to test this out and will update you on the
progress.
Thank you.
Cheers,
Tina
On Thu, 8 Nov 2018 at 17:09, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
If your data is organized into a 0, 1, 2 matrix that can be read directly
into R, you can follow the following code to get a set of bfile formatted
data accepted by plink.
get bed file.> suppressMessages(library(MVP))> geno <- as.matrix(read.table('data.txt', sep='|'))> geno
  V1  V2  V3  V4  V5  V6  V7

[1,] NaN NaN NaN NaN NaN NaN NaN
[2,]   0   0   0   0   2   0 NaN
[3,]   2   2   2   2   2   2   0
[4,]   2   2   2   2   2   2   2
[5,]   2   0   2   2   2   0   0
[6,]   0 NaN   0   0   0   2   2
[7,] NaN NaN NaN NaN NaN NaN NaN
[8,]   0   0   0   0   0   0   0> geno[is.nan(geno)] <- NA> geno
V1 V2 V3 V4 V5 V6 V7
[1,] NA NA NA NA NA NA NA
[2,]  0  0  0  0  2  0 NA
[3,]  2  2  2  2  2  2  0
[4,]  2  2  2  2  2  2  2
[5,]  2  0  2  2  2  0  0
[6,]  0 NA  0  0  0  2  2
[7,] NA NA NA NA NA NA NA
[8,]  0  0  0  0  0  0  0> write_bfile(as.big.matrix(geno)@address, 'plink')
0%   10   20   30   40   50   60   70   80   90   100%|----|----|----|----|----|----|----|----|----|----|***************************************************# get fam file.> pheno <- read.table('pheno.txt', header=T)> pheno
ID Pheno11  1   51.92  2   49.83  3   30.14  4   45.45  5   35.46  6   40.27  7   49.1> fam <- cbind(pheno$ID, pheno$ID, 0, 0, 0, pheno$Pheno1)> write.table(fam, 'plink.fam', quote = F, row.names = F, col.names = F, sep = '\t')
get bim file.> map <- read.table('map.txt', header=T)> map
SNP Chr  Pos1 rs1   1 10012 rs2   1 20193 rs3   1 32214 rs4   1 44005 rs5   1 55506 rs6   1 61907 rs7   1 79908 rs8   1 9999> bim <- cbind(map[, 2], map[, 1], 0, map[, 3], 0, 0)> write.table(bim, paste0(out, '.bim'), quote = F, row.names = F, col.names = F, sep = '\t')
you will can get plink.bed, plink.fam, plink.bim. now you can use plink
--bfile plink to access your data.
Cheers,
Haohao
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Hi, Tina
Yes, MVP can read data from hapmap format, it should work fine.

First, may I ask why is there an error at the end of the run? What does it
mean?

This problem seems to be caused by the separator.  You originally had only one phenotype, but after the conversion, 4 were generated. Rlm7 has been successfully executed. The second phenotype is all NA, so the program has an error when generating an empty big.matrix.

why is the number of individuals reduced from 68 to 65

Please check your Rlm7mvp.hmp.phe file. The Rlm7 column may have several missing values and the missing values will be deleted.

Is it write_file?
write_bfile is a function in the MVP development version that you can download from the devel branch in Github.

Cheers,
Haohao

Hi Haohao,
I tried to re-run MVP, this time imputing the phenotype data for the two
individuals in the phenotype file, so I have complete 68 phenotype data and
68 genotype data with no missing data.
This time there are 63 individuals. May I ask, where did the 5 individuals
go?
Also, a new error exists:
Warning messages:
1: In hist.default(phe[, i], plot = FALSE, breaks = Breaks, xlab = "",  :
arguments 'freq', 'col', 'main', 'xlab', 'ylab', '...' are not made use of
2: In dir.create("PCA plot3D") : 'PCA plot3D' already exists
What does this error mean?
Good thing is that now the .hmp.numeric is consistent with the Rlm7A one
(see attached Rlm7B). I think the .hmp.numeric data is influenced by the
phenotype data because once I corrected the separator in the phenotype data
as I did in my previous email, the .hmp.numeric data also changed as shown
in the previous screenshots Rlm7 and Rlm7A. Now Rlm7B is the same with
Rlm7A.
#--------------------------------------------Welcome to
MVP---------------------------------------------#
A Memory-efficient, Visualization-enhanced, and
Parallel-accelerated Tool For GWAS           #
Version: 1.0.1
                         #

Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,
Shuhong Zhao, and Xiaolei Liu #
Contact<EMAIL_ADDRESS>                         #

#-------------------------------------------------------------------------------------------------------#
[1] "Input data has 63 individuals, 52099 markers"
[1] "Principal Component Analysis Start..."
means for first 10 snps:
[1] 0 0 0 0 0 0 0 0 0 0
[1] "GWAS Start..."
[1] "General Linear Model (GLM) Start ..."
[1] "Mixed Linear Model (MLM) Start ..."
[1] "Calculating Kinship..."
[1] "Variance components..."
[1] "Variance Components Estimation is Done!"
[1] "Eigen-Decomposition..."
[1] "Eigen-Decomposition is Done!"
[1] "Significance Level: 9.59711318835294e-07"
[1] "Visualization Start..."
[1] "Phenotype distribution Plotting..."
[1] "PCA plot2d..."
[1] "PCA plot3d..."
Writing 'MVP.3D090.png'
Writing 'MVP.3D045.png'
[1] "SNP_Density Plotting..."
[1] "Circular_Manhattan Plotting Rlm7.GLM..."
[1] "Circular_Manhattan Plotting Rlm7.MLM..."
[1] "Rectangular_Manhattan Plotting Rlm7.GLM..."
[1] "Rectangular_Manhattan Plotting Rlm7.MLM..."
[1] "Q_Q Plotting Rlm7.GLM..."
[1] "Q_Q Plotting Rlm7.MLM..."
[1] "Multracks_Rectangular Plotting Rlm7.GLM..."
[1] "Multracks_Rectangular Plotting Rlm7.MLM..."
[1] "Multraits_Rectangular Plotting..."
[1] "Multracks_QQ Plotting Rlm7.GLM..."
[1] "Multracks_QQ Plotting Rlm7.MLM..."
[1] "Multraits_QQ Plotting Rlm7.GLM..."
[1] "Multraits_QQ Plotting Rlm7.MLM..."
#-------------------------------------------MVP
ACCOMPLISHED--------------------------------------------#
Warning messages:
1: In hist.default(phe[, i], plot = FALSE, breaks = Breaks, xlab = "",  :
arguments 'freq', 'col', 'main', 'xlab', 'ylab', '...' are not made use of
2: In dir.create("PCA plot3D") : 'PCA plot3D' already exists
Cheers,
Tina
On Fri, 9 Nov 2018 at 12:24, Ting Neik<EMAIL_ADDRESS>wrote:

Thanks Haohao.
First, I managed to fix the separator problem. Thank you for this tip. Now
the phenotype file is correct with only one phenotype that is Rlm7 with 2
missing values. So I would expect 66 individuals. How come It is showing 61
invididuals with a new error:
Error in SetElements.bm(x, i, j, value) :
Matrix dimensions do not agree with big.matrix instance set size.
#--------------------------------------------Welcome to
MVP---------------------------------------------#
A Memory-efficient, Visualization-enhanced, and
Parallel-accelerated Tool For GWAS           #
Version: 1.0.1
                         #

Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,
Shuhong Zhao, and Xiaolei Liu #
Contact<EMAIL_ADDRESS>                           #

#-------------------------------------------------------------------------------------------------------#
[1] "Input data has 61 individuals, 52099 markers"
[1] "Principal Component Analysis Start..."
means for first 10 snps:
[1] 0 0 0 0 0 0 0 0 0 0
Show Traceback
Rerun with Debug
Error in SetElements.bm(x, i, j, value) :
Matrix dimensions do not agree with big.matrix instance set size.
Second, how come the .hmp.numeric output is different each time I run MVP?
Please see attached screenshot of Rlm7 and Rlm7A.hmp.numeric files
Thank you again in advance for your help.
Cheers,
Tina
On Fri, 9 Nov 2018 at 11:56, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
Yes, MVP can read data from hapmap format, it should work fine.
First, may I ask why is there an error at the end of the run? What does it
mean?
This problem seems to be caused by the separator. You originally had only
one phenotype, but after the conversion, 4 were generated. Rlm7 has been
successfully executed. The second phenotype is all NA, so the program has
an error when generating an empty big.matrix.
why is the number of individuals reduced from 68 to 65
Please check your Rlm7mvp.hmp.phe file. The Rlm7 column may have several
missing values and the missing values will be deleted.
Is it write_file?
write_bfile is a function in the MVP development version that you can
download from the devel branch in Github.
Cheers,
Haohao
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Hi, Tina

where did the 5 individuals go?

Individuals with phenotype or genotype deletion will be filtered, and if your phenotype is ok, check your genotype file.

What does this error mean?

These are just some warnings, not errors. Some parameters are ignored when plotting the phenotype distribution plot, and you have ever run MVP so the folder for the PCA 3D plot already exists.
Haohao

Thank you, Haohao.
I apologise for the all the long questions. I just can't get it. Really
appreciate all your help and support and of course, patience. Thank you
very much.
My genotype file is a normal .hmp.txt file, has all 68 individuals, with
either AA, AB, or BB genotype and missing calls represented as NN. The
X.hmp.geno.desc file shows:
new("big.matrix.descriptor", description = list(sharedType = "FileBacked",
filename = "Rlm7B.hmp.geno.bin", dirname = "/Users/tinaneik/",

totalRows = 52099L, totalCols = 63L, rowOffset = c(0, 52099

), colOffset = c(0, 63), nrow = 52099, ncol = 63, rowNames = NULL,

colNames = NULL, type = "char", separated = FALSE))

I check again the genotype data but can't find anything unusual. I just
added '#' to the 'rs' in the first column and did the same to the 6th
column 'assembly'. And this time, I get a different X.hmp.geno.desc output:
new("big.matrix.descriptor", description = list(sharedType = "FileBacked",
filename = "Rlm7C.hmp.geno.bin", dirname = "/Users/tinaneik/",

totalRows = 52099L, totalCols = 65L, rowOffset = c(0, 52099

), colOffset = c(0, 65), nrow = 52099, ncol = 65, rowNames = NULL,

colNames = NULL, type = "char", separated = FALSE))

May I ask what is happening? What did MVP filter from the totalCols that I
can't get full 68 individuals? They look alright to me in the genotype
file.
Appreciate your help here. Thank you so much.
Cheers,
Tina
On Fri, 9 Nov 2018 at 16:13, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
where did the 5 individuals go?
Individuals with phenotype or genotype deletion will be filtered, and if
your phenotype is ok, check your genotype file.
What does this error mean?
These are just some warnings, not errors. Some parameters are ignored when
plotting the phenotype distribution plot, and you have ever run MVP so the
folder for the PCA 3D plot already exists.
Haohao
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Hi, Tina
Sorry to reply to your message so late. We are very happy that you use our software and we will improve the way MVP works to reduce the occurrence of these problems. Please feel free to ask your question.
From your reply, your original file may not be fully supported by MVP. I am not sure what went wrong. Could you please share data files with me? I will check it and tell you why the MVP is not working properly.
Haohao
Email<EMAIL_ADDRESS>

I think the files were too big. I attached the compressed version here:
https://www.dropbox.com/s/eq1awhj87nfi9s8/Archive.zip?dl=0
Please let me know if you can access them?
Thank you.
Cheers,
Tina
On Sun, 11 Nov 2018 at 20:37, Ting Neik<EMAIL_ADDRESS>wrote:

Thanks, Haohao.
Here are the files. Many thanks for the help.
Hope we manage to find out the problem.
Hear from you soon, thank you,
Cheers,
Tina
On Sun, 11 Nov 2018 at 15:11, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
Sorry to reply to your message so late. We are very happy that you use
our software and we will improve the way MVP works to reduce the occurrence
of these problems. Please feel free to ask your question.
From your reply, your original file may not be fully supported by MVP. I
am not sure what went wrong. Could you please share data files with me? I
will check it and tell you why the MVP is not working properly.
Haohao
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Hi, Tina
I have received your data, but I have time to check this data this afternoon and I will reply you today.
Haohao

Hi, Tina
I have checked the data, as you said there is nothing unusual here. And then I rearranged the data with the development version of MVP and got the expected results.(see bellow log) The MVP.Data module was upgraded in the development version, I guess there are some bugs in the previous version. If I find the bug, I will let you know.
This is the result: ForTina.zip
You can continue your analysis with the mvp.imp.* files in the archive.
And log:
> suppressMessages(library(MVP))
> MVP.Data(fileHMP='Rlm7_SNP_68lines_forNumericAll_forTASSEL_ForHaohao.hmp.txt',
+ filePhe='Phenotype_Rlm7_68LinesAllA_ForHaohao.txt')
Preparing data for MVP...
Reading file...
inds: 68	markers:52099

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Preparation for GENOTYPE data is done within 1s
Preparation for Genotype File is done!
Preparation for PHENOTYPE data is Done within 0s
Imputing...
Impute Genotype File is done!
[1] "Principal Component Analysis Start..."
Preparation for PC matrix is done!
Calculate KINSHIP using Vanraden method...
Preparation for Kinship matrix is done!
MVP data prepration accomplished successfully!

Haohao

Many thanks, Haohao.
What do I do if I want to run another set of data? Do I uninstall my
current MVP package? Where can I get the development version of MVP to
re-install? Is this the latest version?
Thank you.
Cheers,
Tina
On Mon, 12 Nov 2018 at 15:32, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
I have checked the data, as you said there is nothing unusual here. And
then I rearranged the data with the development version of MVP and got the
expected results.(see bellow log) The MVP.Data module was upgraded in the
development version, I guess there are some bugs in the previous version.
If I find the bug, I will let you know.
This is the result: ForTina.zip
https://github.com/XiaoleiLiuBio/MVP/files/2570892/ForTina.zip
You can continue your analysis with the mvp.imp.* files in the archive.
And log:

suppressMessages(library(MVP))
MVP.Data(fileHMP='Rlm7_SNP_68lines_forNumericAll_forTASSEL_ForHaohao.hmp.txt',

filePhe='Phenotype_Rlm7_68LinesAllA_ForHaohao.txt')
Preparing data for MVP...
Reading file...
inds: 68	markers:52099

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|

Preparation for GENOTYPE data is done within 1s
Preparation for Genotype File is done!
Preparation for PHENOTYPE data is Done within 0s
Imputing...
Impute Genotype File is done!
[1] "Principal Component Analysis Start..."
Preparation for PC matrix is done!
Calculate KINSHIP using Vanraden method...
Preparation for Kinship matrix is done!
MVP data prepration accomplished successfully!
Haohao
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Thanks, Haohao.
Can you please let me know when it is stable and ready to be downloaded and
use?
Thank you.
Cheers,
On Mon, 12 Nov 2018 at 18:26, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
Yes, the latest version of MVP can be downloaded from Github's devel
branch. It is unstable and we may have some updates in a few days.
You can install it as follows

Download https://github.com/XiaoleiLiuBio/MVP/archive/devel.zip
Start R input:

devtools::install_local("MVP-devel")
Haohao
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Hi, Tina
We've planned a lot of things to do in the MVP 2.0 release, including some new models, more complete fault tolerance mechanisms, and more visualization. It may be released early next year. We will be very grateful for any suggestions.
Cheers,
Haohao

Hi Haohao,
I followed your instructions to install "MVP-devel" on my MacBook High
Sierra but failed.
Do you think it would be easier to fix the bug in the previous version of
MVP so I can still run it, or is it easier to fix the installation of
MVP-devel below? I'm stuck.
Appreciate your help.
Thank you.
Cheers,
Tina

devtools::install_local("MVP-devel")
Installing MVP
'/Library/Frameworks/R.framework/Resources/bin/R' --no-site-file
--no-environ
--no-save --no-restore --quiet CMD INSTALL  \

'/private/var/folders/qd/1n0yvng123n1sf9cmbzvsf6r0000gn/T/RtmpztBGrb/file46f230031bb/MVP-devel'
\
--library='/Library/Frameworks/R.framework/Versions/3.5/Resources/library'

--install-tests

installing source package ‘MVP’ ...
** libs
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include"
-DNDEBUG
-I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include"
-I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/RcppProgress/include"
-I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/BH/include"
-I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/bigmemory/include"
-I/usr/local/include  -fopenmp  -fPIC  -Wall -g -O2 -c RcppExports.cpp -o
RcppExports.o
clang: error: unsupported option '-fopenmp'
make: *** [RcppExports.o] Error 1
ERROR: compilation failed for package ‘MVP’
removing
‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/MVP’
restoring previous
‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/MVP’
Installation failed: Command failed (1)

Cheers,
Tina Neik
PhD candidate (Batley Lab)
School of Biological Sciences (M092)
The University of Western Australia
Crawley WA 6009
Email<EMAIL_ADDRESS>Mobile: 04112 10 334
On Mon, 12 Nov 2018 at 18:26, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
Yes, the latest version of MVP can be downloaded from Github's devel
branch. It is unstable and we may have some updates in a few days.
You can install it as follows

Download https://github.com/XiaoleiLiuBio/MVP/archive/devel.zip
Start R input:

devtools::install_local("MVP-devel")
Haohao
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Hi Haohao,
I managed to solve the MVP-devel installation problem by changing the
~/.R/Makevars
I tried running MVP-devel. But even this version gave the same problem as
before:
new("big.matrix.descriptor", description = list(sharedType = "FileBacked",
filename = "mvp.hmp.geno.bin", dirname = "/Users/tinaneik/",

totalRows = 52099L, totalCols = 65L, rowOffset = c(0, 52099

), colOffset = c(0, 65), nrow = 52099, ncol = 65, rowNames = NULL,

colNames = NULL, type = "char", separated = FALSE))

It should be 68 individuals.
What should I do?
Cheers,
Tina
On Mon, 12 Nov 2018 at 22:32, Ting Neik<EMAIL_ADDRESS>wrote:

Hi Haohao,
I followed your instructions to install "MVP-devel" on my MacBook High
Sierra but failed.
Do you think it would be easier to fix the bug in the previous version of
MVP so I can still run it, or is it easier to fix the installation of
MVP-devel below? I'm stuck.
Appreciate your help.
Thank you.
Cheers,
Tina

devtools::install_local("MVP-devel")
Installing MVP
'/Library/Frameworks/R.framework/Resources/bin/R' --no-site-file
--no-environ
--no-save --no-restore --quiet CMD INSTALL  \

'/private/var/folders/qd/1n0yvng123n1sf9cmbzvsf6r0000gn/T/RtmpztBGrb/file46f230031bb/MVP-devel'
\
--library='/Library/Frameworks/R.framework/Versions/3.5/Resources/library'

--install-tests

installing source package ‘MVP’ ...
** libs
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include"
-DNDEBUG
-I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include"
-I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/RcppProgress/include"
-I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/BH/include"
-I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/bigmemory/include"
-I/usr/local/include  -fopenmp  -fPIC  -Wall -g -O2 -c RcppExports.cpp -o
RcppExports.o
clang: error: unsupported option '-fopenmp'
make: *** [RcppExports.o] Error 1
ERROR: compilation failed for package ‘MVP’
removing
‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/MVP’
restoring previous
‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/MVP’
Installation failed: Command failed (1)

Cheers,
Tina Neik
PhD candidate (Batley Lab)
School of Biological Sciences (M092)
The University of Western Australia
Crawley WA 6009
Email<EMAIL_ADDRESS>Mobile: 04112 10 334
On Mon, 12 Nov 2018 at 18:26, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
Yes, the latest version of MVP can be downloaded from Github's devel
branch. It is unstable and we may have some updates in a few days.
You can install it as follows

Download https://github.com/XiaoleiLiuBio/MVP/archive/devel.zip
Start R input:

devtools::install_local("MVP-devel")
Haohao
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You are receiving this because you authored the thread.
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https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-437829562,
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.

Got it. The name of the individuals cannot be in character, must be in S1,
S2, S3 etc like what I provided for you.
I managed to get 68L, but another error again. Can you please help me?
Thank you.

imMVP <- MVP(

phe=phenotype,

geno=genotype,

map=map,

#K=Kinship,

#CV.GLM=Covariates,

#CV.MLM=Covariates,

#CV.FarmCPU=Covariates,

nPC.GLM=5,

nPC.MLM=3,

nPC.FarmCPU=3,

perc=1,

priority="speed",

#ncpus=10,

vc.method="EMMA",

maxLoop=10,

method.bin="FaST-LMM",#"FaST-LMM","EMMA", "static"

#permutation.threshold=TRUE,

#permutation.rep=100,

threshold=0.05,

method=c("GLM", "MLM")

)
Error in MVP.Version(TRUE) : could not find function "MVP.Version"

Cheers,
Tina
On Mon, 12 Nov 2018 at 23:30, Ting Neik<EMAIL_ADDRESS>wrote:

Hi Haohao,
I managed to solve the MVP-devel installation problem by changing the
~/.R/Makevars
I tried running MVP-devel. But even this version gave the same problem as
before:
new("big.matrix.descriptor", description = list(sharedType = "FileBacked",
filename = "mvp.hmp.geno.bin", dirname = "/Users/tinaneik/",

totalRows = 52099L, totalCols = 65L, rowOffset = c(0, 52099

), colOffset = c(0, 65), nrow = 52099, ncol = 65, rowNames = NULL,

colNames = NULL, type = "char", separated = FALSE))

It should be 68 individuals.
What should I do?
Cheers,
Tina
On Mon, 12 Nov 2018 at 22:32, Ting Neik<EMAIL_ADDRESS>wrote:

Hi Haohao,
I followed your instructions to install "MVP-devel" on my MacBook High
Sierra but failed.
Do you think it would be easier to fix the bug in the previous version of
MVP so I can still run it, or is it easier to fix the installation of
MVP-devel below? I'm stuck.
Appreciate your help.
Thank you.
Cheers,
Tina

devtools::install_local("MVP-devel")
Installing MVP
'/Library/Frameworks/R.framework/Resources/bin/R' --no-site-file
--no-environ
--no-save --no-restore --quiet CMD INSTALL  \

'/private/var/folders/qd/1n0yvng123n1sf9cmbzvsf6r0000gn/T/RtmpztBGrb/file46f230031bb/MVP-devel'
\
--library='/Library/Frameworks/R.framework/Versions/3.5/Resources/library'

--install-tests

installing source package ‘MVP’ ...
** libs
clang++ -std=gnu++11
-I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG
-I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include"
-I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/RcppProgress/include"
-I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/BH/include"
-I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/bigmemory/include"
-I/usr/local/include  -fopenmp  -fPIC  -Wall -g -O2 -c RcppExports.cpp -o
RcppExports.o
clang: error: unsupported option '-fopenmp'
make: *** [RcppExports.o] Error 1
ERROR: compilation failed for package ‘MVP’
removing
‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/MVP’
restoring previous
‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/MVP’
Installation failed: Command failed (1)

Cheers,
Tina Neik
PhD candidate (Batley Lab)
School of Biological Sciences (M092)
The University of Western Australia
Crawley WA 6009
Email<EMAIL_ADDRESS>Mobile: 04112 10 334
On Mon, 12 Nov 2018 at 18:26, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
Yes, the latest version of MVP can be downloaded from Github's devel
branch. It is unstable and we may have some updates in a few days.
You can install it as follows

Download https://github.com/XiaoleiLiuBio/MVP/archive/devel.zip
Start R input:

devtools::install_local("MVP-devel")
Haohao
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-437829562,
or mute the thread
https://github.com/notifications/unsubscribe-auth/Al8lkNaG7wR4IzdgiZu-p3feucA2B3xNks5uuUzBgaJpZM4YOb4u
.

Hi Haohao,
The installation website for Linux is not working:
https://github.com/XiaoleiLiuBio/MVP/releases/download/v1.0.1/MVP_offline_1.0.1.zip
Cheers,
Tina
On Tue, 13 Nov 2018 at 00:51, Ting Neik<EMAIL_ADDRESS>wrote:

Got it. The name of the individuals cannot be in character, must be in S1,
S2, S3 etc like what I provided for you.
I managed to get 68L, but another error again. Can you please help me?
Thank you.

imMVP <- MVP(

phe=phenotype,

geno=genotype,

map=map,

#K=Kinship,

#CV.GLM=Covariates,

#CV.MLM=Covariates,

#CV.FarmCPU=Covariates,

nPC.GLM=5,

nPC.MLM=3,

nPC.FarmCPU=3,

perc=1,

priority="speed",

#ncpus=10,

vc.method="EMMA",

maxLoop=10,

method.bin="FaST-LMM",#"FaST-LMM","EMMA", "static"

#permutation.threshold=TRUE,

#permutation.rep=100,

threshold=0.05,

method=c("GLM", "MLM")

)
Error in MVP.Version(TRUE) : could not find function "MVP.Version"

Cheers,
Tina
On Mon, 12 Nov 2018 at 23:30, Ting Neik<EMAIL_ADDRESS>wrote:

Hi Haohao,
I managed to solve the MVP-devel installation problem by changing the
~/.R/Makevars
I tried running MVP-devel. But even this version gave the same problem as
before:
new("big.matrix.descriptor", description = list(sharedType =
"FileBacked",
filename = "mvp.hmp.geno.bin", dirname = "/Users/tinaneik/",

totalRows = 52099L, totalCols = 65L, rowOffset = c(0, 52099

), colOffset = c(0, 65), nrow = 52099, ncol = 65, rowNames = NULL,

colNames = NULL, type = "char", separated = FALSE))

It should be 68 individuals.
What should I do?
Cheers,
Tina
On Mon, 12 Nov 2018 at 22:32, Ting Neik<EMAIL_ADDRESS>wrote:

Hi Haohao,
I followed your instructions to install "MVP-devel" on my MacBook High
Sierra but failed.
Do you think it would be easier to fix the bug in the previous version
of MVP so I can still run it, or is it easier to fix the installation of
MVP-devel below? I'm stuck.
Appreciate your help.
Thank you.
Cheers,
Tina

devtools::install_local("MVP-devel")
Installing MVP
'/Library/Frameworks/R.framework/Resources/bin/R' --no-site-file
--no-environ
--no-save --no-restore --quiet CMD INSTALL  \

'/private/var/folders/qd/1n0yvng123n1sf9cmbzvsf6r0000gn/T/RtmpztBGrb/file46f230031bb/MVP-devel'
\
--library='/Library/Frameworks/R.framework/Versions/3.5/Resources/library'

--install-tests

installing source package ‘MVP’ ...
** libs
clang++ -std=gnu++11
-I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG
-I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include"
-I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/RcppProgress/include"
-I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/BH/include"
-I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/bigmemory/include"
-I/usr/local/include  -fopenmp  -fPIC  -Wall -g -O2 -c RcppExports.cpp -o
RcppExports.o
clang: error: unsupported option '-fopenmp'
make: *** [RcppExports.o] Error 1
ERROR: compilation failed for package ‘MVP’
removing
‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/MVP’
restoring previous
‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/MVP’
Installation failed: Command failed (1)

Cheers,
Tina Neik
PhD candidate (Batley Lab)
School of Biological Sciences (M092)
The University of Western Australia
Crawley WA 6009
Email<EMAIL_ADDRESS>Mobile: 04112 10 334
On Mon, 12 Nov 2018 at 18:26, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
Yes, the latest version of MVP can be downloaded from Github's devel
branch. It is unstable and we may have some updates in a few days.
You can install it as follows

Download https://github.com/XiaoleiLiuBio/MVP/archive/devel.zip
Start R input:

devtools::install_local("MVP-devel")
Haohao
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-437829562,
or mute the thread
https://github.com/notifications/unsubscribe-auth/Al8lkNaG7wR4IzdgiZu-p3feucA2B3xNks5uuUzBgaJpZM4YOb4u
.

Thanks Haohao,
Please see my response in blue.
Cheers,
Tina
On Tue, 13 Nov 2018 at 12:10, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
I am very sorry that you have encountered so many problems when using our
software.

Mac default Clang does not support OpenMP, you need to download
Clang from https://cran.r-project.org/bin/macosx/tools/. Or just like
you did, disable OpenMP.

I just corrected the  ~/.R/Makevars. Didn't make any changes to the
OpenMP.

I'm using Windows on office desktop, Mac at home.

If you replace S1-S68 with the individual names:

Carousy Trilogies MPT Trisivil 92071_IB1832 Asgora NVT Poddy YHUA Bayrus
Velkra X1267 X1268 Deakon Anges Polly Serene Archie Tame Schook 528DH Dingy
Telton Thumpy Oscar HY99 Heyol T6 Jackel_1 Jackel_2 Jackel_3 Jackel_4 Garond
NDB7 Amazon Tair Bold Margin Tomero Wayine P569 PO1548 ATRBanjo Avand_1
Avand_2 Avand_3 Avand_4 Bell_1 Bell_2 Highlands_1 Highlands_2 Highlands_3
Highlands_4 Torn_1 Torn_2 Cambrial_1 Putty_2 Putty_1 Pollen_2 GCV_1 GCV_2
Gale_1 Gale_2 <IP_ADDRESS>_1 CT_1 NKR_1 NKR_2 Extreme_1
It would give 65 or lesser individuals instead of 68
But if you give S1-S68 as individual name, then it will read as 68L, but
later on give error on "attach.big.matrix" matrix row and column doesn't
match.

The MVP.Version function just prints some welcome messages. I first
encountered this error, maybe the MVP was not installed correctly.

I got stuck at MVP.Version on my macbook using the MVP-devel version. So I
uninstalled and use the 1.0.1 version on my Macbook.

Still cannot run because it says the "attach.big.matrix" matrix row and
column doesn't match.

Is 1.0.1 instead of v1.0.1, I will fix it on the README page
https://github.com/XiaoleiLiuBio/MVP/releases/download/1.0.1/MVP_offline_1.0.1.zip

Thanks

Haohao
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-438128479,
or mute the thread
https://github.com/notifications/unsubscribe-auth/Al8lkM_nGyE7QlkjAF7bvhNAvXboH90uks5uukY6gaJpZM4YOb4u
.

Hi Haohao,
I installed the MVP 1.0.1 using Linux command and managed to run it
successfully on my office desktop.
I used my original file with the individual names (not S1-S68). Still I
don't know where the 3 individuals go.
But I'm happy because it finally worked. If you know how did MVP do the
filtering of removing individuals, please let me know.
Thank you very much.

MVP.Data(fileHMP="Rlm7_SNP_68lines_forNumericAll_forTASSEL.hmp.txt",
filePhe="Phenotype_Rlm7_68LinesAllA.txt", sep.hmp="\t", sep.phe="\t",
SNP.effect="Add", out="Rlm7MVP", fileKin=FALSE, filePC=FALSE)
[1] "Preparing data for MVP..."
[1] "Preparation for PHENOTYPE data is done!"
[1] "Output numeric genotype..."
[1] "File: Rlm7_SNP_68lines_forNumericAll_forTASSEL.hmp.txt ; Total
markers:  52099  finished!"
[1] "Preparation for numeric data is done!"
[1] "Output mvp genotype..."
[1] "MVP data prepration accomplished successfully!"
genotype <- attach.big.matrix("Rlm7MVP.geno.desc")
phenotype <- read.table("Rlm7MVP.phe", head=TRUE)
map <- read.table("Rlm7MVP.map", head=TRUE)
imMVP <- MVP(

phe=phenotype,

geno=genotype,

map=map,

#K=Kinship,

#CV.GLM=Covariates,

#CV.MLM=Covariates,

#CV.FarmCPU=Covariates,

nPC.GLM=5,

nPC.MLM=3,

nPC.FarmCPU=3,

perc=1,

priority="speed",

#ncpus=10,

vc.method="EMMA",

maxLoop=10,

method.bin="FaST-LMM",#"FaST-LMM","EMMA", "static"

#permutation.threshold=TRUE,

#permutation.rep=100,

threshold=0.05,

method=c("GLM", "MLM", "FarmCPU")

)
#--------------------------------------------Welcome to
MVP---------------------------------------------#

A Memory-efficient, Visualization-enhanced, and
Parallel-accelerated Tool For GWAS           #
Version: 1.0.1
                        #

Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,
Shuhong Zhao, and Xiaolei Liu #
Contact<EMAIL_ADDRESS>                        #

#-------------------------------------------------------------------------------------------------------#
[1] "Input data has 65 individuals, 52099 markers"
[1] "Principal Component Analysis Start ..."
means for first 10 snps:
[1] 0 0 0 0 0 0 0 0 0 0
[1] "GWAS Start..."
[1] "General Linear Model (GLM) Start ..."
|---------------------------------------------------------------------->100.00%
[1] "Mixed Linear Model (MLM) Start ..."
[1] "Calculating Kinship..."
[1] "Variance components..."
[1] "Variance Components Estimation is Done!"
[1] "Eigen-Decomposition..."
[1] "Eigen-Decomposition is Done!"
|---------------------------------------------------------------------->100.00%
[1] "FarmCPU Start ..."
[1] "Current loop: 1 out of maximum of 10"
[1] "seqQTN"
NULL
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 3
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 2 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1] 5.000000e+05 6.000000e+00 8.169581e+01 1.100854e-01 1.485997e-01
[1] 5.000000e+06 6.000000e+00 8.169581e+01 1.100854e-01 1.485997e-01
[1] 5.000000e+07 6.000000e+00 8.153343e+01 1.213692e-01 1.482407e-01
[1] "seqQTN"
[1]  8745 43885  6808  5801 35234 25329
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 9
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 3 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1] 5.000000e+05 6.000000e+00 3.152053e+01 7.503536e-02 6.789480e-02
[1] 5.000000e+06 6.000000e+00 3.152053e+01 7.503536e-02 6.789480e-02
[1] 5.000000e+07 6.000000e+00 3.152053e+01 7.503536e-02 6.789480e-02
[1] "seqQTN"
[1]  8745  6808   704 15175 43885  5801 25329 35234
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 11
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 4 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1]  5.000000e+05  6.000000e+00 -1.993368e+01  1.797932e-01  2.689142e-02
[1]  5.000000e+06  6.000000e+00 -1.993368e+01  1.797932e-01  2.689142e-02
[1] 5.000000e+07 6.000000e+00 8.335373e+00 1.090235e-02 5.399971e-02
[1] "seqQTN"
[1]  8745   704  6808 15175 13531 43885 35234 25329  5801
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 12
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 5 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1]  5.000000e+05  6.000000e+00 -2.022484e+01  1.634129e-01  2.701198e-02
[1]  5.000000e+06  6.000000e+00 -2.022484e+01  1.634129e-01  2.701198e-02
[1]  5.000000e+07  6.000000e+00 -1.020703e+01  3.247892e-04  4.820299e-02
[1] "seqQTN"
[1]  8745  6808   704 15175 13531 35234 43885 25329  5801
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 12
|---------------------------------------------------------------------->100.00%
[1] "Significance Level: 9.59711318835294e-07"
[1] "Visualization Start..."
[1] "Phenotype distribution Plotting..."
[1] "PCA plot2d..."
[1] "SNP_Density Plotting..."
[1] "Circular_Manhattan Plotting Rlm7.GLM..."
[1] "Circular_Manhattan Plotting Rlm7.MLM..."
[1] "Circular_Manhattan Plotting Rlm7.FarmCPU..."
[1] "Rectangular_Manhattan Plotting Rlm7.GLM..."
[1] "Rectangular_Manhattan Plotting Rlm7.MLM..."
[1] "Rectangular_Manhattan Plotting Rlm7.FarmCPU..."
[1] "Q_Q Plotting Rlm7.GLM..."
[1] "Q_Q Plotting Rlm7.MLM..."
[1] "Q_Q Plotting Rlm7.FarmCPU..."
[1] "Multracks_Rectangular Plotting Rlm7.GLM..."
[1] "Multracks_Rectangular Plotting Rlm7.MLM..."
[1] "Multracks_Rectangular Plotting Rlm7.FarmCPU..."
[1] "Multraits_Rectangular Plotting..."
[1] "Multracks_QQ Plotting Rlm7.GLM..."
[1] "Multracks_QQ Plotting Rlm7.MLM..."
[1] "Multracks_QQ Plotting Rlm7.FarmCPU..."
[1] "Multraits_QQ Plotting Rlm7.GLM..."
[1] "Multraits_QQ Plotting Rlm7.MLM..."
[1] "Multraits_QQ Plotting Rlm7.FarmCPU..."
#-------------------------------------------MVP
ACCOMPLISHED--------------------------------------------#

Cheers,
Tina
On Tue, 13 Nov 2018 at 12:53, Ting Neik<EMAIL_ADDRESS>wrote:

Thanks Haohao,
Please see my response in blue.
Cheers,
Tina
On Tue, 13 Nov 2018 at 12:10, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
I am very sorry that you have encountered so many problems when using our
software.

Mac default Clang does not support OpenMP, you need to download
Clang from https://cran.r-project.org/bin/macosx/tools/. Or just like
you did, disable OpenMP.

I just corrected the  ~/.R/Makevars. Didn't make any changes to the
OpenMP.

I'm using Windows on office desktop, Mac at home.

If you replace S1-S68 with the individual names:

Carousy Trilogies MPT Trisivil 92071_IB1832 Asgora NVT Poddy YHUA Bayrus
Velkra X1267 X1268 Deakon Anges Polly Serene Archie Tame Schook 528DH
Dingy Telton Thumpy Oscar HY99 Heyol T6 Jackel_1 Jackel_2 Jackel_3
Jackel_4 Garond NDB7 Amazon Tair Bold Margin Tomero Wayine P569 PO1548
ATRBanjo Avand_1 Avand_2 Avand_3 Avand_4 Bell_1 Bell_2 Highlands_1
Highlands_2 Highlands_3 Highlands_4 Torn_1 Torn_2 Cambrial_1 Putty_2
Putty_1 Pollen_2 GCV_1 GCV_2 Gale_1 Gale_2 <IP_ADDRESS>_1 CT_1 NKR_1 NKR_2
Extreme_1
It would give 65 or lesser individuals instead of 68
But if you give S1-S68 as individual name, then it will read as 68L, but
later on give error on "attach.big.matrix" matrix row and column doesn't
match.

The MVP.Version function just prints some welcome messages. I
first encountered this error, maybe the MVP was not installed correctly.

I got stuck at MVP.Version on my macbook using the MVP-devel version. So
I uninstalled and use the 1.0.1 version on my Macbook.

Still cannot run because it says the "attach.big.matrix" matrix row and
column doesn't match.

Is 1.0.1 instead of v1.0.1, I will fix it on the README page
https://github.com/XiaoleiLiuBio/MVP/releases/download/1.0.1/MVP_offline_1.0.1.zip

Thanks

Haohao
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-438128479,
or mute the thread
https://github.com/notifications/unsubscribe-auth/Al8lkM_nGyE7QlkjAF7bvhNAvXboH90uks5uukY6gaJpZM4YOb4u
.

Hi Haohao,
I can't get past this step because of the same error. This is for a new
dataset for 176 individuals.
#--------------------------------------------Welcome to
MVP---------------------
------------------------#
A Memory-efficient, Visualization-enhanced, and
Parallel-accelerated                                   Tool For GWAS

Version: 1.0.1
                                                          #

Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,
Shuhon                                  g Zhao, and Xiaolei Liu #
Contact<EMAIL_ADDRESS>                                                          #

#-------------------------------------------------------------------------------
------------------------#
[1] "Input data has 173 individuals, 52099 markers"
[1] "Principal Component Analysis Start ..."
means for first 10 snps:
[1] 0 0 0 0 0 0 0 0 0 0
Error in SetElements.bm(x, i, j, value) :
Matrix dimensions do not agree with big.matrix instance set size.

Cheers,
Tina
On Tue, 13 Nov 2018 at 15:04, Ting Neik<EMAIL_ADDRESS>wrote:

Hi Haohao,
I installed the MVP 1.0.1 using Linux command and managed to run it
successfully on my office desktop.
I used my original file with the individual names (not S1-S68). Still I
don't know where the 3 individuals go.
But I'm happy because it finally worked. If you know how did MVP do the
filtering of removing individuals, please let me know.
Thank you very much.

MVP.Data(fileHMP="Rlm7_SNP_68lines_forNumericAll_forTASSEL.hmp.txt",
filePhe="Phenotype_Rlm7_68LinesAllA.txt", sep.hmp="\t", sep.phe="\t",
SNP.effect="Add", out="Rlm7MVP", fileKin=FALSE, filePC=FALSE)
[1] "Preparing data for MVP..."
[1] "Preparation for PHENOTYPE data is done!"
[1] "Output numeric genotype..."
[1] "File: Rlm7_SNP_68lines_forNumericAll_forTASSEL.hmp.txt ; Total
markers:  52099  finished!"
[1] "Preparation for numeric data is done!"
[1] "Output mvp genotype..."
[1] "MVP data prepration accomplished successfully!"
genotype <- attach.big.matrix("Rlm7MVP.geno.desc")
phenotype <- read.table("Rlm7MVP.phe", head=TRUE)
map <- read.table("Rlm7MVP.map", head=TRUE)
imMVP <- MVP(

phe=phenotype,

geno=genotype,

map=map,

#K=Kinship,

#CV.GLM=Covariates,

#CV.MLM=Covariates,

#CV.FarmCPU=Covariates,

nPC.GLM=5,

nPC.MLM=3,

nPC.FarmCPU=3,

perc=1,

priority="speed",

#ncpus=10,

vc.method="EMMA",

maxLoop=10,

method.bin="FaST-LMM",#"FaST-LMM","EMMA", "static"

#permutation.threshold=TRUE,

#permutation.rep=100,

threshold=0.05,

method=c("GLM", "MLM", "FarmCPU")

)
#--------------------------------------------Welcome to
MVP---------------------------------------------#

A Memory-efficient, Visualization-enhanced, and
Parallel-accelerated Tool For GWAS           #
Version: 1.0.1
                          #

Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,
Shuhong Zhao, and Xiaolei Liu #
Contact<EMAIL_ADDRESS>                          #

#-------------------------------------------------------------------------------------------------------#
[1] "Input data has 65 individuals, 52099 markers"
[1] "Principal Component Analysis Start ..."
means for first 10 snps:
[1] 0 0 0 0 0 0 0 0 0 0
[1] "GWAS Start..."
[1] "General Linear Model (GLM) Start ..."
|---------------------------------------------------------------------->100.00%
[1] "Mixed Linear Model (MLM) Start ..."
[1] "Calculating Kinship..."
[1] "Variance components..."
[1] "Variance Components Estimation is Done!"
[1] "Eigen-Decomposition..."
[1] "Eigen-Decomposition is Done!"
|---------------------------------------------------------------------->100.00%
[1] "FarmCPU Start ..."
[1] "Current loop: 1 out of maximum of 10"
[1] "seqQTN"
NULL
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 3
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 2 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1] 5.000000e+05 6.000000e+00 8.169581e+01 1.100854e-01 1.485997e-01
[1] 5.000000e+06 6.000000e+00 8.169581e+01 1.100854e-01 1.485997e-01
[1] 5.000000e+07 6.000000e+00 8.153343e+01 1.213692e-01 1.482407e-01
[1] "seqQTN"
[1]  8745 43885  6808  5801 35234 25329
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 9
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 3 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1] 5.000000e+05 6.000000e+00 3.152053e+01 7.503536e-02 6.789480e-02
[1] 5.000000e+06 6.000000e+00 3.152053e+01 7.503536e-02 6.789480e-02
[1] 5.000000e+07 6.000000e+00 3.152053e+01 7.503536e-02 6.789480e-02
[1] "seqQTN"
[1]  8745  6808   704 15175 43885  5801 25329 35234
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 11
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 4 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1]  5.000000e+05  6.000000e+00 -1.993368e+01  1.797932e-01  2.689142e-02
[1]  5.000000e+06  6.000000e+00 -1.993368e+01  1.797932e-01  2.689142e-02
[1] 5.000000e+07 6.000000e+00 8.335373e+00 1.090235e-02 5.399971e-02
[1] "seqQTN"
[1]  8745   704  6808 15175 13531 43885 35234 25329  5801
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 12
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 5 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1]  5.000000e+05  6.000000e+00 -2.022484e+01  1.634129e-01  2.701198e-02
[1]  5.000000e+06  6.000000e+00 -2.022484e+01  1.634129e-01  2.701198e-02
[1]  5.000000e+07  6.000000e+00 -1.020703e+01  3.247892e-04  4.820299e-02
[1] "seqQTN"
[1]  8745  6808   704 15175 13531 35234 43885 25329  5801
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 12
|---------------------------------------------------------------------->100.00%
[1] "Significance Level: 9.59711318835294e-07"
[1] "Visualization Start..."
[1] "Phenotype distribution Plotting..."
[1] "PCA plot2d..."
[1] "SNP_Density Plotting..."
[1] "Circular_Manhattan Plotting Rlm7.GLM..."
[1] "Circular_Manhattan Plotting Rlm7.MLM..."
[1] "Circular_Manhattan Plotting Rlm7.FarmCPU..."
[1] "Rectangular_Manhattan Plotting Rlm7.GLM..."
[1] "Rectangular_Manhattan Plotting Rlm7.MLM..."
[1] "Rectangular_Manhattan Plotting Rlm7.FarmCPU..."
[1] "Q_Q Plotting Rlm7.GLM..."
[1] "Q_Q Plotting Rlm7.MLM..."
[1] "Q_Q Plotting Rlm7.FarmCPU..."
[1] "Multracks_Rectangular Plotting Rlm7.GLM..."
[1] "Multracks_Rectangular Plotting Rlm7.MLM..."
[1] "Multracks_Rectangular Plotting Rlm7.FarmCPU..."
[1] "Multraits_Rectangular Plotting..."
[1] "Multracks_QQ Plotting Rlm7.GLM..."
[1] "Multracks_QQ Plotting Rlm7.MLM..."
[1] "Multracks_QQ Plotting Rlm7.FarmCPU..."
[1] "Multraits_QQ Plotting Rlm7.GLM..."
[1] "Multraits_QQ Plotting Rlm7.MLM..."
[1] "Multraits_QQ Plotting Rlm7.FarmCPU..."
#-------------------------------------------MVP
ACCOMPLISHED--------------------------------------------#

Cheers,
Tina
On Tue, 13 Nov 2018 at 12:53, Ting Neik<EMAIL_ADDRESS>wrote:

Thanks Haohao,
Please see my response in blue.
Cheers,
Tina
On Tue, 13 Nov 2018 at 12:10, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
I am very sorry that you have encountered so many problems when using
our software.

Mac default Clang does not support OpenMP, you need to download
Clang from https://cran.r-project.org/bin/macosx/tools/. Or just
like you did, disable OpenMP.

I just corrected the  ~/.R/Makevars. Didn't make any changes to the
OpenMP.

I'm using Windows on office desktop, Mac at home.

If you replace S1-S68 with the individual names:

Carousy Trilogies MPT Trisivil 92071_IB1832 Asgora NVT Poddy YHUA Bayrus
Velkra X1267 X1268 Deakon Anges Polly Serene Archie Tame Schook 528DH
Dingy Telton Thumpy Oscar HY99 Heyol T6 Jackel_1 Jackel_2 Jackel_3
Jackel_4 Garond NDB7 Amazon Tair Bold Margin Tomero Wayine P569 PO1548
ATRBanjo Avand_1 Avand_2 Avand_3 Avand_4 Bell_1 Bell_2 Highlands_1
Highlands_2 Highlands_3 Highlands_4 Torn_1 Torn_2 Cambrial_1 Putty_2
Putty_1 Pollen_2 GCV_1 GCV_2 Gale_1 Gale_2 <IP_ADDRESS>_1 CT_1 NKR_1 NKR_2
Extreme_1
It would give 65 or lesser individuals instead of 68
But if you give S1-S68 as individual name, then it will read as 68L, but
later on give error on "attach.big.matrix" matrix row and column doesn't
match.

The MVP.Version function just prints some welcome messages. I
first encountered this error, maybe the MVP was not installed correctly.

I got stuck at MVP.Version on my macbook using the MVP-devel version. So
I uninstalled and use the 1.0.1 version on my Macbook.

Still cannot run because it says the "attach.big.matrix" matrix row and
column doesn't match.

Is 1.0.1 instead of v1.0.1, I will fix it on the README page
https://github.com/XiaoleiLiuBio/MVP/releases/download/1.0.1/MVP_offline_1.0.1.zip

Thanks

Haohao
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Hi, Tina
This seems to be because some individuals have been mistakenly identified. The log show that only 173 individuals have been identified. Have you replaced the individual with S1, S2, S3...?
Haohao

Thanks Haohao.
I replaced the individual names to S1, S2, S3 etc and get it to run
completely, now I know the problem of the error
"Error in SetElements.bm(x, i, j, value) :
Matrix dimensions do not agree with big.matrix instance set size."
is because naming of individuals have to be in S1, S2, S3 format. But I
still don't understand why it is 173 individuals, not 176, just like before
missing 3 individuals.
Do you know what is the reason?
Appreciate your help.
Thank you.
#--------------------------------------------Welcome to
MVP---------------------------------------------#
A Memory-efficient, Visualization-enhanced, and
Parallel-accelerated Tool For GWAS           #
Version: 1.0.1
                         #

Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,
Shuhong Zhao, and Xiaolei Liu #
Contact<EMAIL_ADDRESS>                         #

#-------------------------------------------------------------------------------------------------------#
[1] "Input data has 173 individuals, 52099 markers"
[1] "Principal Component Analysis Start..."
means for first 10 snps:
[1] 0 0 0 0 0 0 0 0 0 0
[1] "GWAS Start..."
[1] "General Linear Model (GLM) Start ..."
[1] "Mixed Linear Model (MLM) Start ..."
[1] "Calculating Kinship..."
[1] "Variance components..."
[1] "Variance Components Estimation is Done!"
[1] "Eigen-Decomposition..."
[1] "Eigen-Decomposition is Done!"
[1] "FarmCPU Start ..."
[1] "Current loop: 1 out of maximum of 10"
[1] "seqQTN"
NULL
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 3
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 2 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1] 5.000000e+05 9.000000e+00 3.480488e+02 8.435696e-02 3.780617e-01
[1] 5.000000e+06 9.000000e+00 3.480488e+02 8.435696e-02 3.780617e-01
[1] 5.000000e+07 9.000000e+00 3.714787e+02 7.304528e-02 4.418982e-01
[1] "seqQTN"
[1] 13719  6805 26349  8745 12938 42787  9954 20568  7037
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 12
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 3 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1] 5.000000e+05 9.000000e+00 2.220364e+02 8.107669e-02 1.804395e-01
[1] 5.000000e+06 9.000000e+00 2.137317e+02 7.631238e-02 1.698363e-01
[1] 5.000000e+07 9.000000e+00 2.258661e+02 7.450450e-02 1.832515e-01
[1] "seqQTN"
[1] 25867 24506 24459 34889 44391  9954 13719 42787  7037  8745 20568
6805 12938 26349
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 17
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 4 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1] 5.000000e+05 9.000000e+00 1.565068e+02 4.534805e-02 1.232688e-01
[1] 5.000000e+06 9.000000e+00 1.565068e+02 4.534805e-02 1.232688e-01
[1] 5.000000e+07 9.000000e+00 1.418569e+02 1.328677e-01 1.087829e-01
[1] "seqQTN"
[1] 34889 42787  8745  7037  9954  3926 49355 31345 12826 44391 24459
20568 26349  6805
[15] 13719 12938 24506
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 20
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 5 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1] 5.000000e+05 9.000000e+00 9.824032e+01 2.221593e-02 9.009003e-02
[1] 5.000000e+06 9.000000e+00 9.824032e+01 2.221593e-02 9.009003e-02
[1] 5.000000e+07 9.000000e+00 9.706491e+01 1.120900e-02 9.153457e-02
[1] "seqQTN"
[1] 34889 42787  7037 49355  8745 12826 44391 24459  3686  3926  9954
31345  6805 13719
[15] 26349 12938 24506
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 20
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 6 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1] 5.000000e+05 9.000000e+00 7.204580e+01 5.812775e-04 8.626922e-02
[1] 5.000000e+06 9.000000e+00 7.204580e+01 5.812775e-04 8.626922e-02
[1] 5.000000e+07 9.000000e+00 6.110482e+01 7.504327e-03 7.484952e-02
[1] "seqQTN"
[1] 34889 42787 12826  8745 44391  3686  7037 49355  9954  3926 51954
24459 31345 13719
[15] 26349  6805 12938 24506
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 21
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 7 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1] 5.000000e+05 9.000000e+00 3.804992e+01 4.775745e-04 7.087834e-02
[1] 5.000000e+06 9.000000e+00 3.804992e+01 4.775745e-04 7.087834e-02
[1] 5.000000e+07 9.000000e+00 3.834452e+01 4.775745e-04 7.087834e-02
[1] "seqQTN"
[1] 34889 42787 44391 49355  3686  7037 12826  8745  9954 51954  3926
24459 13719 31345
[15]  6805 26349 12938 24506
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 21
|---------------------------------------------------------------------->100.00%
[1] "Significance Level: 9.59711318835294e-07"
[1] "Visualization Start..."
[1] "Phenotype distribution Plotting..."
[1] "PCA plot2d..."
[1] "PCA plot3d..."
Writing 'MVP.3D090.png'
Loading required namespace: magick
Writing 'MVP.3D045.png'
[1] "SNP_Density Plotting..."
[1] "Circular_Manhattan Plotting Rlm7.GLM..."
[1] "Circular_Manhattan Plotting Rlm7.MLM..."
[1] "Circular_Manhattan Plotting Rlm7.FarmCPU..."
[1] "Rectangular_Manhattan Plotting Rlm7.GLM..."
[1] "Rectangular_Manhattan Plotting Rlm7.MLM..."
[1] "Rectangular_Manhattan Plotting Rlm7.FarmCPU..."
[1] "Q_Q Plotting Rlm7.GLM..."
[1] "Q_Q Plotting Rlm7.MLM..."
[1] "Q_Q Plotting Rlm7.FarmCPU..."
[1] "Multracks_Rectangular Plotting Rlm7.GLM..."
[1] "Multracks_Rectangular Plotting Rlm7.MLM..."
[1] "Multracks_Rectangular Plotting Rlm7.FarmCPU..."
[1] "Multraits_Rectangular Plotting..."
[1] "Multracks_QQ Plotting Rlm7.GLM..."
[1] "Multracks_QQ Plotting Rlm7.MLM..."
[1] "Multracks_QQ Plotting Rlm7.FarmCPU..."
[1] "Multraits_QQ Plotting Rlm7.GLM..."
[1] "Multraits_QQ Plotting Rlm7.MLM..."
[1] "Multraits_QQ Plotting Rlm7.FarmCPU..."
#-------------------------------------------MVP
ACCOMPLISHED--------------------------------------------#
There were 50 or more warnings (use warnings() to see the first 50)
Cheers,
Tina
On Wed, 14 Nov 2018 at 21:00, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
This seems to be because some individuals have been mistakenly identified.
The log show that only 173 individuals have been identified. Have you
replaced the individual with S1, S2, S3...?
Haohao
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Here is the .geno.desc
It says ncol=176?
new("big.matrix.descriptor", description = list(sharedType = "FileBacked",
filename = "Rlm7MVP176.geno.bin", dirname = "/Users/tinaneik/",

totalRows = 52099L, totalCols = 176L, rowOffset = c(0, 52099

), colOffset = c(0, 176), nrow = 52099, ncol = 176, rowNames = NULL,

colNames = NULL, type = "char", separated = FALSE))

Cheers,
Tina
On Wed, 14 Nov 2018 at 21:41, Ting Neik<EMAIL_ADDRESS>wrote:

Thanks Haohao.
I replaced the individual names to S1, S2, S3 etc and get it to run
completely, now I know the problem of the error
"Error in SetElements.bm(x, i, j, value) :
Matrix dimensions do not agree with big.matrix instance set size."
is because naming of individuals have to be in S1, S2, S3 format. But I
still don't understand why it is 173 individuals, not 176, just like before
missing 3 individuals.
Do you know what is the reason?
Appreciate your help.
Thank you.
#--------------------------------------------Welcome to
MVP---------------------------------------------#
A Memory-efficient, Visualization-enhanced, and
Parallel-accelerated Tool For GWAS           #
Version: 1.0.1
                         #

Authors: Lilin Yin, Haohao Zhang, Zhiwu Zhang, Xinyun Li, Xiaohui Yuan,
Shuhong Zhao, and Xiaolei Liu #
Contact<EMAIL_ADDRESS>                           #

#-------------------------------------------------------------------------------------------------------#
[1] "Input data has 173 individuals, 52099 markers"
[1] "Principal Component Analysis Start..."
means for first 10 snps:
[1] 0 0 0 0 0 0 0 0 0 0
[1] "GWAS Start..."
[1] "General Linear Model (GLM) Start ..."
[1] "Mixed Linear Model (MLM) Start ..."
[1] "Calculating Kinship..."
[1] "Variance components..."
[1] "Variance Components Estimation is Done!"
[1] "Eigen-Decomposition..."
[1] "Eigen-Decomposition is Done!"
[1] "FarmCPU Start ..."
[1] "Current loop: 1 out of maximum of 10"
[1] "seqQTN"
NULL
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 3
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 2 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1] 5.000000e+05 9.000000e+00 3.480488e+02 8.435696e-02 3.780617e-01
[1] 5.000000e+06 9.000000e+00 3.480488e+02 8.435696e-02 3.780617e-01
[1] 5.000000e+07 9.000000e+00 3.714787e+02 7.304528e-02 4.418982e-01
[1] "seqQTN"
[1] 13719  6805 26349  8745 12938 42787  9954 20568  7037
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 12
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 3 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1] 5.000000e+05 9.000000e+00 2.220364e+02 8.107669e-02 1.804395e-01
[1] 5.000000e+06 9.000000e+00 2.137317e+02 7.631238e-02 1.698363e-01
[1] 5.000000e+07 9.000000e+00 2.258661e+02 7.450450e-02 1.832515e-01
[1] "seqQTN"
[1] 25867 24506 24459 34889 44391  9954 13719 42787  7037  8745 20568
6805 12938 26349
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 17
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 4 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1] 5.000000e+05 9.000000e+00 1.565068e+02 4.534805e-02 1.232688e-01
[1] 5.000000e+06 9.000000e+00 1.565068e+02 4.534805e-02 1.232688e-01
[1] 5.000000e+07 9.000000e+00 1.418569e+02 1.328677e-01 1.087829e-01
[1] "seqQTN"
[1] 34889 42787  8745  7037  9954  3926 49355 31345 12826 44391 24459
20568 26349  6805
[15] 13719 12938 24506
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 20
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 5 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1] 5.000000e+05 9.000000e+00 9.824032e+01 2.221593e-02 9.009003e-02
[1] 5.000000e+06 9.000000e+00 9.824032e+01 2.221593e-02 9.009003e-02
[1] 5.000000e+07 9.000000e+00 9.706491e+01 1.120900e-02 9.153457e-02
[1] "seqQTN"
[1] 34889 42787  7037 49355  8745 12826 44391 24459  3686  3926  9954
31345  6805 13719
[15] 26349 12938 24506
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 20
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 6 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1] 5.000000e+05 9.000000e+00 7.204580e+01 5.812775e-04 8.626922e-02
[1] 5.000000e+06 9.000000e+00 7.204580e+01 5.812775e-04 8.626922e-02
[1] 5.000000e+07 9.000000e+00 6.110482e+01 7.504327e-03 7.484952e-02
[1] "seqQTN"
[1] 34889 42787 12826  8745 44391  3686  7037 49355  9954  3926 51954
24459 31345 13719
[15] 26349  6805 12938 24506
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 21
|---------------------------------------------------------------------->100.00%
[1] "Current loop: 7 out of maximum of 10"
[1] "Optimizing Pseudo QTNs..."
[1] 5.000000e+05 9.000000e+00 3.804992e+01 4.775745e-04 7.087834e-02
[1] 5.000000e+06 9.000000e+00 3.804992e+01 4.775745e-04 7.087834e-02
[1] 5.000000e+07 9.000000e+00 3.834452e+01 4.775745e-04 7.087834e-02
[1] "seqQTN"
[1] 34889 42787 44391 49355  3686  7037 12826  8745  9954 51954  3926
24459 13719 31345
[15]  6805 26349 12938 24506
[1] "scanning..."
[1] "number of covariates in current loop is:"
[1] 21
|---------------------------------------------------------------------->100.00%
[1] "Significance Level: 9.59711318835294e-07"
[1] "Visualization Start..."
[1] "Phenotype distribution Plotting..."
[1] "PCA plot2d..."
[1] "PCA plot3d..."
Writing 'MVP.3D090.png'
Loading required namespace: magick
Writing 'MVP.3D045.png'
[1] "SNP_Density Plotting..."
[1] "Circular_Manhattan Plotting Rlm7.GLM..."
[1] "Circular_Manhattan Plotting Rlm7.MLM..."
[1] "Circular_Manhattan Plotting Rlm7.FarmCPU..."
[1] "Rectangular_Manhattan Plotting Rlm7.GLM..."
[1] "Rectangular_Manhattan Plotting Rlm7.MLM..."
[1] "Rectangular_Manhattan Plotting Rlm7.FarmCPU..."
[1] "Q_Q Plotting Rlm7.GLM..."
[1] "Q_Q Plotting Rlm7.MLM..."
[1] "Q_Q Plotting Rlm7.FarmCPU..."
[1] "Multracks_Rectangular Plotting Rlm7.GLM..."
[1] "Multracks_Rectangular Plotting Rlm7.MLM..."
[1] "Multracks_Rectangular Plotting Rlm7.FarmCPU..."
[1] "Multraits_Rectangular Plotting..."
[1] "Multracks_QQ Plotting Rlm7.GLM..."
[1] "Multracks_QQ Plotting Rlm7.MLM..."
[1] "Multracks_QQ Plotting Rlm7.FarmCPU..."
[1] "Multraits_QQ Plotting Rlm7.GLM..."
[1] "Multraits_QQ Plotting Rlm7.MLM..."
[1] "Multraits_QQ Plotting Rlm7.FarmCPU..."
#-------------------------------------------MVP
ACCOMPLISHED--------------------------------------------#
There were 50 or more warnings (use warnings() to see the first 50)
Cheers,
Tina
On Wed, 14 Nov 2018 at 21:00, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
This seems to be because some individuals have been mistakenly
identified. The log show that only 173 individuals have been identified.
Have you replaced the individual with S1, S2, S3...?
Haohao
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Reply to this email directly, view it on GitHub
https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-438653786,
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.

Hi, Tina
I tried to replace S1, S2, S3... as Carousy, Trilogies, MPT... as you said yesterday. But without encountering errors, Could you please share the original name data with me? It will be very helpful in identifying this issue.
Haohao

Thanks Haohao.
The 68 individual names were the same as given. Do you mean you can run all
68 without any errors?
I can get it to read the individual name but can't fix the 3 missing
individuals.
"If you replace S1-S68 with the individual names:
Carousy Trilogies MPT Trisivil 92071_IB1832 Asgora NVT Poddy YHUA Bayrus
Velkra X1267 X1268 Deakon Anges Polly Serene Archie Tame Schook 528DH Dingy
Telton Thumpy Oscar HY99 Heyol T6 Jackel_1 Jackel_2 Jackel_3 Jackel_4 Garond
NDB7 Amazon Tair Bold Margin Tomero Wayine P569 PO1548 ATRBanjo Avand_1
Avand_2 Avand_3 Avand_4 Bell_1 Bell_2 Highlands_1 Highlands_2 Highlands_3
Highlands_4 Torn_1 Torn_2 Cambrial_1 Putty_2 Putty_1 Pollen_2 GCV_1 GCV_2
Gale_1 Gale_2 <IP_ADDRESS>_1 CT_1 NKR_1 NKR_2 Extreme_1
It would give 65 or lesser individuals instead of 68"
For my new dataset 176 individuals, I can't get it to read the individual
name but when I changed to S1-176, it can read but missing 3 individuals
again.
Cheers,
Tina
On Wed, 14 Nov 2018 at 23:10, haohao<EMAIL_ADDRESS>wrote:

Hi, Tina
I tried to replace S1, S2, S3... as Carousy, Trilogies, MPT... as you said
yesterday. But without encountering errors, Could you please share the
original name data with me? It will be very helpful in identifying this
issue.
Haohao
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You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/XiaoleiLiuBio/MVP/issues/28#issuecomment-438695405,
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.

Hi, Tina
Yes, I replaced them and worked fine.
> suppressMessages(library(MVP))
> MVP.Data(fileHMP='Rlm7_SNP_68lines_forNumericAll_forTASSEL_ForHaohao.hmp.txt', filePhe='Phenotype_Rlm7_68LinesAllA_ForHaohao.txt')
Preparing data for MVP...
Reading file...
inds: 68	markers:52099

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Preparation for GENOTYPE data is done within 1s
Preparation for Genotype File is done!
Preparation for PHENOTYPE data is Done within 0s
Imputing...
Impute Genotype File is done!
[1] "Principal Component Analysis Start..."
Preparation for PC matrix is done!
Calculate KINSHIP using Vanraden method...
Preparation for Kinship matrix is done!
MVP data prepration accomplished successfully!
> geno <- attach.big.matrix('mvp.imp.geno.desc')
> pheno <- read.table('mvp.phe', header=T)
> map <- read.table('mvp.map', header=T)
> imMVP <- MVP(
+     phe=pheno,
+     geno=geno,
+     map=map,
+     nPC.MLM=3,
+     perc=1,
+     priority="speed",
+     vc.method="EMMA",
+     threshold=0.05,
+     method=c("MLM")
+ )
====================== Welcome to MVP ======================
       A Memory-efficient, Visualization-enhanced, and
             Parallel-accelerated Tool For GWAS
                    __  __  __   __  ___
                   |  \/  | \ \ / / | _ \
                   | |\/| |  \ V /  |  _/
                   |_|  |_|   \_/   |_|     Version: 1.1.0
  Authors: Lilin Yin, Haohao Zhang, and Xiaolei Liu
  Contact<EMAIL_ADDRESS>============================================================
[1] "Input data has 68 individuals, 52099 markers"
[1] "Principal Component Analysis Start..."
[1] "GWAS Start..."
[1] "Mixed Linear Model (MLM) Start ..."
[1] "Calculating Kinship..."
[1] "Variance components..."
[1] "Variance Components Estimation is Done!"
[1] "Eigen-Decomposition..."
[1] "Eigen-Decomposition is Done!"
|-------------------------------------------------->100.00%
[1] "Significance Level: 9.59711318835294e-07"
[1] "Visualization Start..."
[1] "Phenotype distribution Plotting..."
[1] "PCA plot2d..."
[1] "The 3D PCA map has been temporarily disabled, and we will improve this feature in subsequent versions."
[1] "SNP_Density Plotting..."
[1] "Circular_Manhattan Plotting Rlm7.MLM..."
[1] "Rectangular_Manhattan Plotting Rlm7.MLM..."
[1] "Q_Q Plotting Rlm7.MLM..."


Fix UT coverage on codecov

Signed-off-by: Joshua Li<EMAIL_ADDRESS>Description
Fix UT coverage on codecov
Issues Resolved
[List any issues this PR will resolve]
Check List

[ ] New functionality includes testing.

[ ] All tests pass, including unit test, integration test and doctest

[ ] New functionality has been documented.

[ ] New functionality has javadoc added
[ ] New functionality has user manual doc added

[x] Commits are signed per the DCO using --signoff

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
For more information on following Developer Certificate of Origin and signing off your commits, please check here.

Codecov Report

Merging #40 (d413505) into main (ae57f49) will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##             main      #40   +/-   ##
=======================================
  Coverage   95.30%   95.30%
=======================================
  Files           5        5
  Lines         149      149
  Branches       45       45
=======================================
  Hits          142      142
  Misses          7        7

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Fixes #171, removes unused state.touch parameters

Fixes #171.
#171 is now crashing on android bootstrap when it receives the first wait without a position. I'll fix appium-android-bootstrap to accept them.
Please don't merge yet, I'll report back when this is fully functional. Let me know if you have any commentaries about this PR.

Thanks for doing this! How is the work on appium-android-bootstrap going? Anything I can help with?

Hi, I will do a PR for appium-android-bootstrap this weekend. I'll let you know if I have problems.

Hi @imurchie, I couldn't get the maven project to compile. I followed these instructions and got this warning when packaging :
[WARNING] The requested profile "4.4" could not be activated because it does not exist.
I later installed all the API versions and profiles (mvn clean ; mvn install) and got the same warning. Is something else needed for the project to locate the profiles?
mvn help:all-profiles shows no profiles available at the bootstrap directory. I am using maven 3.3.9.
mvn install log
mvn package -P 4.4 log
List of profiles at maven-android-sdk-deployer and bootstrap.
Thanks!

Weird.
I would just use ant build from the bootstrap directory,  instead of Maven directly.
You can also just run npm install from the root directory of appium-android-bootstrap.

I got compilation working and am doing some changes based on Android's InteractionController. Required changes were a bit larger than I had imagined, I will post back when I have results.

Hi,
PR at https://github.com/appium/appium-android-bootstrap/pull/30.

Can you bump the version of appium-android-bootstrap to a minimum of 2.7.5? Then we can get this going!

Done!

Awesome! Thank you!

Published in<EMAIL_ADDRESS>


Fix boolean all reduce bug

As title.

Fixes https://github.com/ml-explore/mlx-examples/issues/951 .


examples: notebooks: Add use-case example for 'evaluating model performance' and script to create tests.

The Use-case Notebook
Changed name to evaluating_model_performance so in the future we can squash tuning_models into this one.
I think moving_between_models should stay as it is since it also has to do with getting predictions of models interchangeably so it cannot be highlighted if squashed.
On the other hand, comparing model performance and tuning models will go hand in hand.
The test_notebooks script
My concern is that the current assertions are dependant upon variables in the notebook. It won't blow up but it won't be helpful if someone goes ahead and names variables different.
I could go ahead and collect all the cell stdouts (which would require handling errors if the cell isn't a code cell) into one output of the notebook, but then again, the stdout could also vary in the sense that maybe someone doesn't bother printing out "Accuracy:" before the accuracy which would mean that there is no way to find it in the output. So the stdouts wouldn't really be a solution.
A solution that comes to mind
We could maybe keep a template notebook, to maintain a standard and keep things uniform, in which we don't fill in the specifics but leave it for the user/contributor. like having the load() function empty to let the user fill in which model he wants and similarly with data and imports and everything. And at the end we have this Accuracy variable with our desired name or something like that and similarly with preds.
Other than that, the script seems to be working alright. Got the following log from some debugging statements showing which files the tests are created for and the directory they run in:
(base) hash1m@hash1m-ThinkPad-W520:~/dffml$ python3 -m unittest tests.test_notebooks
/home/hash1m/dffml
/home/hash1m/dffml/examples/notebooks/compare_model_performance.ipynb
/home/hash1m/dffml/examples/notebooks/moving_between_models.ipynb
/home/hash1m/dffml/tests/notebooks/data
./home/hash1m/dffml/tests/notebooks/data
.
----------------------------------------------------------------------
Ran 2 tests in 12.222s

OK

ping @pdxjohnny

Codecov Report

Merging #1127 (2b3aee0) into master (383d5b1) will increase coverage by 0.03%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master    #1127      +/-   ##
==========================================
+ Coverage   84.46%   84.50%   +0.03%
==========================================
  Files         152      153       +1
  Lines       10040    10065      +25
  Branches     1662     1663       +1
==========================================
+ Hits         8480     8505      +25
  Misses       1213     1213
  Partials      347      347

Impacted Files
Coverage Δ

tests/test_notebooks.py
100.00% <100.00%> (ø)

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Node Sass Could not find binding for your currrent environment linux 64-bit with Node 0.10.x

While running a meteor application using command meteor --settings settings.json getting following errors:
ERROR in Missing binding /home/bhimesh/Desktop/Sol/node_modules/node-sass/vendor/linux-x64-11/binding.node
Node Sass could not find a binding for your current environment: Linux 64-bit with Node 0.10.x

Found bindings for the following environments:
  - Linux 64-bit with Node.js 4.x

This usually happens because your environment has changed since running `npm install`.
Run `npm rebuild node-sass` to build the binding for your current environment.
 @ ./src/client/containers/user/UserContainer.scss 4:14-356 13:2-17:4 14:20-362

ERROR in Missing binding /home/bhimesh/Desktop/Sol/node_modules/node-sass/vendor/linux-x64-11/binding.node
Node Sass could not find a binding for your current environment: Linux 64-bit with Node 0.10.x

Found bindings for the following environments:
  - Linux 64-bit with Node.js 4.x

NPM version (npm -v): 2.15.9
Node version (node -v): 4.5.0
Node Process (node -p process.versions):

{ http_parser: '2.7.0',
  node: '4.5.0',
  v8: '<IP_ADDRESS>',
  uv: '1.9.1',
  zlib: '1.2.8',
  ares: '1.10.1-DEV',
  icu: '56.1',
  modules: '46',
  openssl: '1.0.2h' }

Node Platform (node -p process.platform): linux
Node architecture (node -p process.arch): x64
node-sass version (node -p "require('node-sass').info"):

node-sass   3.10.1  (Wrapper)   [JavaScript]
libsass     3.3.6   (Sass Compiler) [C/C++]

npm node-sass versions (npm ls node-sass):

<EMAIL_ADDRESS>

Node Sass could not find a binding for your current environment: Linux 64-bit with Node 0.10.x
Found bindings for the following environments:

Linux 64-bit with Node.js 4.x

This usually happens because your environment has changed since running npm install.
Run npm rebuild node-sass to build the binding for your current environment.

Did you try following the instructions in the error message? npm rebuild node-sass

@xzyfer I should have mentioned it earlier. I have tried all obvious ways including reinstalling, rebuilding and  resetting, removing node module nd reinstalling. Nothing seems to work.

Try doing a fresh install until https://github.com/sass/node-sass/issues/1766 lands.
rm -rf node_modules
npm install

Thanks @xzyfer your tip helped me!

it works for me

quite the obvious solution, somehow it didn't occur to me. Thanks either way!

Worked for me as well. Thanks.


merge package from arlac77/npm-package-template

package.json

chore(package<EMAIL_ADDRESS>chore(scripts): cover@#overwrite c8 --temp-directory build/coverage ava && c8 report -r lcov --temp-directory build/coverage
chore(package): add nyc from template
chore(package): set $.ava.require='esm' as in template
chore(package): set $.ava.files='tests/-test.js,tests/-test.mjs' as in template
chore(package): set $.ava.extensions='js,mjs' as in template

README.md

docs(README): update from template

:tada: This PR is included in version 5.0.0 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


Add a WiFi status LED

The My Baby's Got LED board has a status LED connected to IO14 on the ESP8266.  That should show the status of the WiFi network:

Solid for connected to WiFi
Flashing while trying to connect?
Fading up/down when the WLED-AP access point is active?

Doesn't do flashing while trying to connect, but will fade up and down with the AP and goes solid when connected.