Installation failed: error in running command when use devtools::install_github("bmansfeld/QTLseqr") to Install

[bmansfeld]

Sorry i can't help with out more information. I would recommend installing the latest version of R and Rstudio. Ben

[haohui123]

ThanK you very much

------------------ 原始邮件 ------------------ 发件人: "bmansfeld/QTLseqr" <notifications@github.com>; 发送时间: 2021年3月9日(星期二) 凌晨4:55 收件人: "bmansfeld/QTLseqr"<QTLseqr@noreply.github.com>; 抄送: "杨好慧"<475889337@qq.com>;"Author"<author@noreply.github.com>; 主题: Re: [bmansfeld/QTLseqr] Installation failed: error in running command when use devtools::install_github("bmansfeld/QTLseqr") to Install (#42)

Closed #42.

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[haohui123]

library("QTLseqr") > #指定文件的目录 > setwd("/data/pub/yanghh/R") > #Set sample and file names > #Set sample and file names > HighBulk <- "QT4Rpool" > LowBulk <- "QT4Spool" > file <- "demo.20QT4reformnoscaffold.table" > #Choose which chromosomes will be included in the analysis (i.e. exclude smaller contigs) > Chroms <- paste0(rep("R0", 9), 1:9) > #Import SNP data from file > df <-

• importFromGATK(
• file = file,
• highBulk = HighBulk,
• lowBulk = LowBulk,
• chromList = Chroms
• ) erSNPs( SNPset = df, refAlleleFreq = 0.20, minTotalDepth = 100, maxTotalDepth = 400, minSampleDepth = 40, minGQ = 99 |==================================================================| 100% 166 MB Removing the following chromosomes:  > #Filter SNPs based on some criteria > df_filt <-
• filterSNPs(
• SNPset = df,
• refAlleleFreq = 0.20,
• minTotalDepth = 100,
• maxTotalDepth = 400,
• minSampleDepth = 40,
• minGQ = 99
• ) Filtering by reference allele frequency: 0.2 <= REF_FRQ <= 0.8 ...Filtered 219647 SNPs Filtering by total sample read depth: Total DP >= 100 ...Filtered 2283058 SNPs Filtering by total sample read depth: Total DP <= 400 ...Filtered 4 SNPs Filtering by per sample read depth: DP >= 40 ...Filtered 7647 SNPs Filtering by Genotype Quality: GQ >= 99 ...Filtered 2102 SNPs Original SNP number: 2614834, Filtered: 2512458, Remaining: 102376 > #Run G' analysis > df_filt <- runGprimeAnalysis(SNPset = df_filt,
• windowSize = 1e6,
• outlierFilter = "deltaSNP") Counting SNPs in each window... , intervals = c(95, 99) )Calculating tricube smoothed delta SNP index... Calculating G and G' statistics... Using deltaSNP-index to filter outlier regions with a threshold of 0.1 Estimating the mode of a trimmed G prime set using the 'modeest' package... Calculating p-values... > #Run QTLseq analysis > df_filt <- runQTLseqAnalysis(
• SNPset = df_filt,
• windowSize = 1e6,
• popStruc = "F2",
• bulkSize = c(30, 30),
• replications = 10000,
• intervals = c(95, 99)
• ) Counting SNPs in each window... Calculating tricube smoothed delta SNP index... Returning the following two sided confidence intervals: 95, 99 Variable 'depth' not defined, using min and max depth from data: 40-194 Assuming bulks selected from F2 population, with 30 and 30 individuals per bulk. Simulating 10000 SNPs with reads at each depth: 40-194 Keeping SNPs with >= 0.3 SNP-index in both simulated bulks Joining, by = "tricubeDP" > #Plot > plotQTLStats(
• SNPset = df_filt,
• var = "Gprime",
• plotThreshold = TRUE,
• q = 0.01
• ) A_QTL.csv" ) > plotQTLStats(
• SNPset = df_filt,
• var = "deltaSNP",
• plotIntervals = TRUE) > #export summary CSV > getQTLTable(
• SNPset = df_filt,
• alpha = 0.01,
• export = TRUE,
• fileName = "my_BSA_QTL.csv"
• )   CHROM qtl    start      end   length nSNPs avgSNPs_Mb peakDeltaSNP 1   R05   1   359111  2707785  2348674  1348        574    0.3715301 2   R08   2   481924  1690113  1208189   166        137    0.4497638 3   R08   3  2315373  3233279   917906    70         76    0.5995933 4   R08   4  5575282 21288165 15712883  2731        174    0.6366040 5   R08   5 27365953 27493493   127540     2         16    0.3348856   posPeakDeltaSNP avgDeltaSNP maxGprime posMaxGprime meanGprime  sdGprime 1         1892524   0.3644229  17.80104      1892524   16.73321 0.7904796 2         1317676   0.4175901  19.03438      1280127   18.07673 1.0102488 3         2997114   0.5769150  18.61783      2622530   17.06866 0.7511737 4         6880353   0.4678844  32.52107      6885361   22.88308 3.2228024 5        27493493   0.3001423  19.11753     27493493   17.21068 2.6966858         AUCaT     meanPval    meanQval 1   3430379.4 2.550512e-04 0.007391167 2   3010778.9 1.655678e-04 0.005890071 3   1997053.7 2.254567e-04 0.006930349 4 132628359.7 5.871937e-05 0.003031114 5    253383.5 2.591057e-04 0.007304724 >