Vignette to Package impute.r
|
|
- Noel Lawrence
- 6 years ago
- Views:
Transcription
1 Vignette to Package impute.r Yvonne M. Badke Department of Animal Science Michigan State University East Lansing, Mi, USA badkeyvo@msu.edu Juan P. Steibel Departments of Animal Science, Fisheries and Wildlife Michigan State University East Lansing, Mi, USA October 25, 2012 Version Introduction impute.r is an R [7] package developed to reproduce imputation accuracy calculations presented in Badke et al. [1]. The package is build as an extension to the R package synbreed [8]. We expanded the functionality of synbreed to include genotype imputation and phasing using a reference panel of haplotypes, and subsets of tagsnp to impute all non-typed SNP. impute.r includes functions necessary to obtain and utilize the input/output of the BEAGLE software [2, 3], as well as performing the imputation using various options of the BEAGLE phasing algorithm. In addition, impute.r contains three functions that are able to generate input for the FESTA program [6] from a gpdata (package synbreed) object containing phased haplotypes. Functions for FESTA program formatting are detailed in a separate vignette: Conversion of data into FESTA format. In addition, the impute.r package includes functions to compute the accuracy measures reported in Badke et al. [1]. This guide provides users with step-by-step instructions to obtain the graphical output included in the publication [1] Input formats, recoding, and quality editing 2.1 Input formats Genotypes used in Badke et al. [1] were obtained from DNA samples of 889 Yorkshire sires and 96 animals in sire/dam/offspring trios, genotyped for all SNP (M=62,163) on the Illumina PorcineSNP60 Genotyping 1
2 BeadChip (Illumina Inc.) at a commercial laboratory (GeneSeek, a Neogen Company, Lincoln, NE). These genotypes were available in a long table format with one row per SNP/sample combination and two columns containing the observed alleles in character coding (A/G/T/C). We reformatted the original long-format data into a gpdata object (york_gpdata) that is provided as part of the impute.r package. The following data objects are released as part of the impute.r package: york_gpdata This file is a gpdata object containing 889 unrelated Yorkshire sires and 96 animals in sire/dam/offspring trios genotyped on SNP. Besides the raw genotypes in a data-frame with SNP in columns and individuals in rows (geno), the object contains a map, a phenotype file, and a pedigree acc_table Is a data-frame containing accuracies for three methods of tagsnp selection that will be used in section ref_size Is a data-frame containing SNP-wise imputation accuracies for all SNP on SSC14 for in- creasing reference panel size, as well as the minor allele frequency of each SNP. The data-frame will be used in section 4 to illustrate how to derive two Figures from Badke et al. [1] To install the package impute.r it is important that the packages epicalc, coda, and synbreed are preinstalled. This can be achieved using the following code: install.packages("epicalc") install.packages("synbreed") install.packages("coda") The york_gpdata object contains the following data-frames (for further detail on how to create a gpdata object please refer to [8]): geno is a data frame with samples organized in rows (identified by row-names) and SNP organized in columns (identified by column-names). The genotype entries can be either in numerical format, as counts of the minor allele, or in character formatting pheno is a data frame with samples organized in rows and traits organized in columns. To use the impute function it is necessary that pheno contains at least one column (sample) with entries trio or random to identify whether the sample is part of a trio and that it should be phased as such, or if the sample is not part of a trio and it should be phased as unrelated to all other samples map is a data frame with one row for each marker and two columns (named chr and pos). The first column identifies the chromosome (numeric or character but not factor) and second column the 2
3 position on the chromosome in centi Morgan or the physical distance relative to the reference sequence in base-pairs. Unique row-names indicate the marker names which should match with marker names in geno pedigree is an object of the class pedigree in synbreed, that can be obtained using the function create.pedigree. create.pedigree requires a vector of sample IDs, a vector identifying the first 57 parent, a vector identify the second parent, and a vector containing the sex of each animal. The user can further specify, if create.pedigree should infer the generation of each animal, or provide a vector identifying the generation of each sample, and if create.pedigree should add ancestors to the pedigree that did not occur in the sample vector Quality editing Animals for this study have been previously cleaned such that all given input only contains data for animals with genotypes available for more than 90% of the SNP, leaving 889 sires and 96 trio animals. To obtain a york_gpdata object was created using create.gpdata from synbreed [8]. Please refer to the manual and vignette of synbreed for more detail on how to use this function. The provided york_gpdata contains uncleaned genotypes, with only those SNP removed that have not been called in any study sample. further process the data we will use the codegeno to clean the data and recode it into numeric format: library(impute.r) library(epicalc) library(synbreed) library(coda) data(york_gpdata) which.heter<-function(x){substr(x,1,1)!=substr(x,3,3)} # applying codegeno for recoding / quality editing york_cleaned<-codegeno(york_gpdata,impute=false, replace.value=null, maf=0.05, nmiss=0.10, label.heter=which.heter, keep.identical=true, verbose=true, print.report=false) To The gpdata object input_cl now contains only SNP with genotypes available for more than 90% of samples, SNP with minor allele frequency (MAF) larger than 5% and alleles have been recoded into numeric counts of the B allele (0,1,2). synbreed identifies the B allele based on MAF such that if data is assembled from several sources it is not advisable to create different gpdata objects due to the fact that small differences in the MAF between the data sources provided could lead to opposite recoding. Using this input we can use the function impute, as explained below, to perform all phasing and genotype imputation that is necessary to reproduce results presented in Badke et al. [1]. 3
4 86 3 Haplotype phasing and genotype imputation using impute Four different phasing and genotype imputation scenarios were used in Badke et al. [1]. In this section we detail how the gpdata object developed in section 2 can be used with the function impute to obtain the desired output for all four scenarios. First we introduce the function impute and all necessary arguments that need to be provided and second we show examples for all four scenarios The impute function The impute function is structurally similar to the codegeno function provided by synbreed [8]. However, codegeno does not implement estimation of phase using sire/dam/offspring trios using the BEAGLE trio input option, and it does not facilitate the use of a reference panel of haplotypes. As a result codegeno can impute randomly missing genotypes for a set of samples/snp (subsets can be obtained using discard.markers or discard.individuals), but it does not support the imputation of high density genotypes from a set of tagsnp. While using much of the original structure of codegeno we added these options to impute. Usage of impute and specification of necessary arguments: impute(gpdata, all_animals=true, animals=c(), all_snp=true, snp=c(), beagle_method=c("trio", "unrelated", "pairs"), reference=false, ref_panel=null, showbeagleoutput=false, nsamples=4, niterations=10, mem=6000) gpdata is a gpdata object as detailed above containing the data frames geno, pheno, map, and pedigree. All these data frames are assembled as specified by synbreed with one column identifying the sample as either random or trio in the pheno data frame all_animals logical, should all samples in geno be imputed, default is TRUE animals a vector containing the IDs of animals (as found in row names of pheno and geno) that should be imputed, if all_animals=false. If all_animals=true any input to this vector will be ignored all_snp logical, should all SNP in geno be imputed, default is TRUE snp 4
5 a vector containing the IDs of SNP that should be used for imputation if all_snp=false. If all_snp=true any input to this vector will be ignored. of geno) beagle_method a character string indicating the beagle method that should be used. impute takes inputs "trio", indicating that the trio procedure in BEAGLE should be used, "unrelated" indicating that no pedigree relation between animals should be assumed for imputation, and "pairs" indicating that the BEAGLE procedure to impute parent-offspring data should be used reference logical, should a reference panel be used to impute SNP/samples in geno, default is FALSE ref_panel 127 if reference=true this is a data frame of reference haplotypes. SNP will be in the columns and identified by column names and haplotypes will be in the rows. This data frame is expected to contain characters A and B to identify the alleles. Row names can be used to identify the individual the haplotype is sampled from, but they are not required showbeagleoutput logical, should the BEAGLE output during the imputation be printed on the screed, default is FALSE nsamples numeric, identifies the number of haplotype pairs to sample for each individual during each iteration of the BEAGLE phasing algorithm. The default is nsamples=4 as specified in [2] niterations positive even integer giving the number of iterations of the phasing algorithm. If an odd integer is specified, the next even integer is used. The default is niterations=10 as specified in [2] mem numeric, is the number of Megabytes of memory available. The default is mem=6000 allowing BEAGLE to use a maximum of 6GB of RAM Phasing of a reference panel of haplotypes from a trio design BEAGLE [3] has a special option allowing the user to provide genotypes from sire/dam/offspring trios for phasing. The resulting sire/dam haplotypes are suitable as a reference panel of haplotypes for imputation based on low density SNP panels. The provided family file can be used to identify those animals that are 5
6 part of a sire/dam/offspring trio and provide a vector containing the IDs of these animals as input for the animal argument of the impute function: trio<-rownames(york_cleaned$geno) [as.data.frame(york_cleaned$pheno)$sample=="trio"] scen1<-impute(york_cleaned, all_animals=false, animals=trio, all_snp=true, beagle_method="trio", reference=false, showbeagleoutput=true) The output of this application of the impute function is a list containing: 1) scen1$gpimputed, a gpdata object including imputed allelic dosages of all SNP/sample combinations in the geno data frame and, 2) scen1$ref, a data frame with SNP in the columns and haplotypes of the sires and dams in the input data in the rows. The data frame of haplotypes has two rows per sample. The second object returned by this function (scen1$ref) can be used as reference panel for future imputations as ref_panel=scen1$ref Imputation of randomly missing genotypes and phasing of unrelated individuals When there is no previous reference panel and samples are not presented in trios BEAGLE, can still estimate phase and impute missing data [3]. The following code applies the impute function to such a case for all animals labeled as randomly sampled from the sire population: sires<-rownames(york_cleaned$geno)[as.data.frame(york_cleaned$pheno)$sample=="random"] scen2<-impute(york_cleaned, all_animals=false, animals=sires, all_snp=true, beagle_method="unrelated", reference=false, showbeagleoutput=true) The output of this imputation is identical to the output described above, only that these haplotypes were obtained from unrelated individuals Imputation of randomly missing genotypes and phasing of unrelated individuals using a reference panel of haplotypes The result of this application of impute is similar to the one in section 3.3, only that in this case the haplotypes from the first phasing run are used as reference panel for imputation: sires<-rownames(york_cleaned$geno)[as.data.frame(york_cleaned$pheno)$sample=="random"] scen3<-impute(york_cleaned, all_animals=false, animals=sires, all_snp=true, beagle_method="unrelated", reference=true, ref_panel=scen1$ref, showbeagleoutput=true) 6
7 Imputation of unrelated individuals genotyped for a subset of SNP (tagsnp) using a reference panel of high density haplotypes In this case impute uses a list of tagsnp in a dataset and a reference panel of high density haplotypes derived from high density genotypes (scen1). data(tagsnp) scen4<-impute(york_cleaned, all_animals=false, animals=paste(sires), all_snp=false, snp=tagsnp, beagle_method="unrelated", reference=true, ref_panel=scen1[[2]], showbeagleoutput=true) The resulting gpdata object contains the data frame geno with the imputed allelic dosage, that can be used to estimate accuracy of imputation through comparison with the input data Estimation of accuracy of imputed genotypes Accuracy of imputation can be measured as 1) the proportion of correctly imputed alleles, 2) the correlation between observed and imputed allelic dosage, or 3) the proportion of correctly imputed alleles adjusted for MAF. The proportion of correctly imputed alleles can be obtained by either counting the difference between the observed allelic dosage and the inferred allelic dosage or by counting the difference between the observed allelic dosage and the posterior expectation of the allelic dosage: IA = 1 M N i i=1 j=1 g ij ĝ ij (1) 2 M N i i= where g ij is the observed allelic dosage of the i th SNP in the j th individual, ĝ ij is the corresponding posterior expected/inferred allelic dosage obtained from BEAGLE output, M is the total number of imputed SNP, and N i is the number of individuals with called genotypes for the i th SNP. However, recent research has pointed out, that quantifying imputation accuracy as the proportion of correctly imputed alleles is biased by the MAF of the imputed SNP [4, 5]. To obtain a measure of imputation accuracy that is unbiased by MAF we used the correlation between observed and imputed allelic dosage [1]. To estimate imputation accuracy in the following examples we used original cleaned gpdata object input_cl (section 2.2) to run the following example on SSC18 using a previously devised list of tagsnp: # discard markers in gpdata that are not on chr 18 york_gpdata<-discard.markers(york_cleaned, which=rownames(york_cleaned$map)[!york_cleaned$map$chr=="18"]) idx<-tagsnp%in%colnames(york_gpdata$geno) tagsnp<-tagsnp[idx] # making a reference panel of trios 7
8 trios<-impute(york_gpdata, all_animals=false, animals=trio, all_snp=true, beagle_method="trio", reference=false, showbeagleoutput=true) # imputing from the tagsnp for all sires imp<-impute(york_gpdata, all_animals=false, animals=sires, all_snp=false, snp=tagsnp, beagle_method="unrelated", reference=true, ref_panel=trios$ref, showbeagleoutput=true) # discarding the observed trio individuals prior to estimating accuracy obs_sires<-discard.individuals(york_gpdata, which=trio) # applying the accuracy estimation function using the observed genotypes and the imputed genotypes acc_out<-accuracy_summary(gpobserved=obs_sires, gpimputed=imp$gpimputed, tagsnp=tagsnp, HPD=0.95) 218 The function accuracy_summary returns average accuracy, SNP-specific accuracy, and individual-specific 219 accuracy, as well as several summary measures of imputation accuracy. The first object returned by accuracy_summary is summary_acc_ia, which is a data-frame of summary measures of imputation accuracy estimated as the proportion of correctly imputed alleles. acc_out$summary_acc_ia # total Sample SNP #Min # # # #Max #mean #HPD-lowerbound #HPD-upperbound The second object returned by accuracy_summary is summary_acc_r2, which is a data-frame of summary measures of imputation accuracy estimated as the correlation between observed and imputed allelic dosage. dim(acc_out$summary_acc_r2) # [1] 8 2 acc_out$summary_acc_r2 Sample SNP #Min # # # #Max #mean #HPD-lowerbound #HPD-upperbound The third object returned by accuracy_summary is individual_acc, which is a data-frame of individual imputation accuracy measured as both the proportion of correctly imputed alleles and the correlation between observed and imputed allelic dosage. dim(acc_out$individual_acc) # [1]
9 head(acc_out$individual_acc) # SampleID IA R2 #York_Sid_1 York_Sid_ #York_Sid_10 York_Sid_ #York_Sid_100 York_Sid_ #York_Sid_101 York_Sid_ #York_Sid_102 York_Sid_ #York_Sid_103 York_Sid_ summary(acc_out$individual_acc[,2:3]) # IA R2 # Min. : Min. : # 1st Qu.: st Qu.: # Median : Median : # Mean : Mean : # 3rd Qu.: rd Qu.: # Max. : Max. : The fourth object returned by accuracy_summary is snp_acc, which is a data-frame of SNP imputation accuracy measured as both the proportion of correctly imputed alleles and the correlation between observed and imputed allelic dosage. dim(acc_out$snp_acc) # [1] head(acc_out$snp_acc) # SNP IA R2 #MARC MARC #H3GA H3GA #MARC MARC summary(acc_out$snp_acc[,2:3]) # IA R2 # Min. : Min. : # 1st Qu.: st Qu.: # Median : Median : # Mean : Mean : # 3rd Qu.: rd Qu.: # Max. : Max. : The fifth object returned by accuracy_summary is snp_measures, which is a data-frame of SNP MAF estimated from the observed allele frequencies and the scaled chromosomal location of each SNP. dim(acc_out$snp_measures) # [1] head(acc_out$snp_measures) # SNP MAF scaled_position #MARC MARC #H3GA H3GA #MARC MARC #CASI CASI #H3GA H3GA #ASGA ASGA summary(acc_out$snp_measures[,2]) # Min. 1st Qu. Median Mean 3rd Qu. Max. # The sixth object returned by accuracy_summary is acc_mat, which is a data-frame of the proportion of 9
10 correctly imputed alleles for each genotype. Rows correspond to SNP and columns to individuals. dim(acc_out$acc_mat) # [1] acc_out$acc_mat[1:5,1:5] # MARC H3GA MARC CASI H3GA #York_Sid_ #York_Sid_ #York_Sid_ #York_Sid_ #York_Sid_ Visualization of imputation accuracy 312 In this section we provide code to obtain the figures published in Badke et al. [1]: Accuracy of Imputation by the scaled chromosomal location of imputed SNP To investigate whether there is a difference in SNP wise imputation accuracy as a function of chromosomal location we plot the estimated accuracy by the scaled location of each SNP. This plot can be build from the output data obtained from the accuracy_summary function that we applied in 3.2. The object acc_out contains the results of accuracy_summary. In addition, we added the weighted mean average and the overall average accuracy to the plot. The graphical output can be seen in Figure 1. # opening the pdf to which the plot will be written pdf("accuracy_by_density.pdf") # obtaining the loess smoother to plot the weighted mean average pred<-loess(acc_out$snp_acc[,2]~acc_out$snp_measures[,3]) # open a plot window of the right dimensions # the accuracy/scaled location are taken from the acc_out object rendered in 3.2 plot(acc_out$snp_acc[,2]~acc_out$snp_measures[,3], main="accuracy of Imputation by the scaled chromosomal location of imputed SNP", xlab="scaled chromosome position", ylab="imputation accuracy", ylim=c(0,1)) # insert a horizontal line representing the average accuracy abline(h=mean(acc_out$snp_acc[,2]), col="green") # inserting the weighted mean average estimated using a loess smoother points(pred$x, pred$fitted, col="red", pch=18) dev.off() Accuracy of imputation by MAF of the SNP Figures 2 contains a plot of SNP wise imputation accuracy as a function of MAF, estimated as the square correlation between observed and imputed allelic dosage. Estimates of accuracy and MAF used in this plot can be obtained from the objects available in acc_out obtained in section 3.2. In addition, we added the weighted mean average accuracy into the plot to assess if there is an obvious pattern of accuracy across all minor allele frequencies. # obtain color coding by density - darker color=more data density in that area 10
11 Accuracy of Imputation by the scaled chromosomal location of imputed SNP Scaled chromosome position Imputation accuracy Figure 1: Accuracy of Imputation by the scaled chromosomal location of imputed SNP colors<-denscols(acc_out$snp_acc[,3]~acc_out$snp_measures[,2]) 340 pdf("accuracy_by_maf.pdf") 341 # obtaining the loess smoother to plot the weighted mean average 342 pred<-loess(acc_out$snp_acc[,3]~acc_out$snp_measures[,2]) 343 # open a plot window of the right dimensions 344 # the accuracy/scaled location are taken from the acc_out object rendered in plot(acc_out$snp_acc[,3]~acc_out$snp_measures[,2], 346 main="accuracy of Imputation by SNP MAF", 347 xlab="maf", ylab=expression( Accuracy R ^2), 348 ylim=c(0,1), pch=20, col=colors) 349 # inserting the weighted mean average estimated using a loess smoother 350 points(pred$x, pred$fitted, col="red", pch=18) 351 dev.off() Accuracy of Imputation by SNP MAF MAF Accuracy R 2 Figure 2: Accuracy of Imputation by SNP MAF 3. Accuracy of Imputation by tagsnp density and selection method 353 In the paper accompanying this package several methods for tagsnp selection were compared across a
12 variety of tagsnp densities [1]. Since only one density of tagsnp was explored in the example above we have provided a small table with imputation accuracy for several densities of tagsnp estimated for all three methods of tagsnp selection, with the corresponding graphical output shown in Figure 3: # Accuracy of imputation by tagsnp density data("acc_table") head(acc_table) # Number.of.SNP r2.threshold FESTA BEAGLE n_even evenly.spaced # # # # NA # NA # NA pdf("accuracy_by_tagsnpdensity.pdf") tab<-acc_table # open an empty plot with the correct dimensions plot(0, pty="n", main="accuracy by density", xlab="number of tagsnp", ylab="accuracy of Imputation", ylim=c(0,1), xlim=c(0,max(tab[,1])+50)) # add points for the results of statistical tagsnp selection points(tab[,3]~tab[,1], type="p", col="black", pch=19) # add points for the results of predictive tagsnp selection points(tab[,6]~tab[,5], type="p", col="red", pch=15) # add points for the results of evenly spaced tagsnp points(tab[,4]~tab[,1], type="p", col="darkgreen", pch=17) # add a legend legend(x="bottomright", pch=c(19, 15, 17), bty="n", legend=c("statistical selection", "evenly spaced", "predictive selection"), col=c("black","red","darkgreen")) dev.off() Accuracy by density Accuracy of Imputation statistical selection evenly spaced predictive selection number of tagsnp Figure 3: Accuracy of Imputation by tagsnp density and selection method 384 This code can be adjusted to obtain figures similar to Figures 1 and 2 in Badke et. al [1] Accuracy of imputation by reference panel size Badke et al. [1] also investigated the effect of increasing the number of reference haplotypes. To obtain 12
13 a larger reference panel the available 889 Yorkshire sires split into 200 validation animals and 689 animals that were added to stepwise increase the number of reference haplotypes. Since the example detailed in 3.2 only included one imputation we provided a file containing imputation accuracy for a random sample of 2000 SNP for a variety of reference panels (accuracy_by_ref_size.txt). data(ref_size) # extracting the number of reference animals from column names n_ref<-as.numeric(sub("x", "",colnames(ref_size[,2:7])))*2 # average accuracy for reference panel sizes - each column corresponds to a panel size avg_acc<-colmeans(ref_size[,2:7]) pdf("accuracy_by_refsize.pdf") # plot accuracy by number of reference haplotypes plot(avg_acc~n_ref, type="p", main="accuracy by Reference panel size", ylab=expression( Accuracy R ^2), xlab="number of reference haplotypes", xlim=c(0,max(n_ref)), ylim=c(0,1), pch=20) dev.off() Accuracy by Reference panel size Accuracy R Number of reference haplotypes Figure 4: Accuracy of Imputation by reference panel size Supplementary Figures 1 & 2 The supplementary Figures 1 and 2 provided by Badke et al. [1] show the weighted mean average accuracy as a function of MAF and the chromosomal location for a variety of reference panel sizes, to illustrate how increasing the number of reference haplotypes affects overall SNP accuracy, but especially imputation accuracy of SNP with previously below average accuracy. Example code to obtain a figure of that particular type can be found below for the graphical output that can be see in Figure 5 pdf("accuracy_by_refsize_weighted.pdf") # specifying colors for all sizes of the reference panel cols<-c("black", "red", "blue", "orange", "magenta", "darkgreen") # open an empty plot of the correct dimensions plot(0, type="n", main="accuracy of Imputation by increasing reference panel size", ylab=expression( Accuracy R ^2), xlab="scaled chromosomal location", ylim=c(0,1), xlim=c(0,1)) 13
14 # adding all 7 columns as points to the plot for (i in 1:length(n_ref)) { # estimating the weighted mean average using a loess smoother pred<-loess(ref_size[,i+1]~ref_size[,8]) # adding the points points(pred$x, pred$fitted, col=cols[i], pch=18, cex=0.25) } # including a legend to the plot legend(x="bottomright", pch=18, bty="n", legend=paste(n_ref, " reference haplotypes", sep= ), col=cols) dev.off() Accuracy of Imputation by increasing reference panel size Accuracy R reference haplotypes 256 reference haplotypes 512 reference haplotypes 1024 reference haplotypes 1200 reference haplotypes 1378 reference haplotypes scaled chromosomal location Figure 5: Accuracy of Imputation by increasing reference panel size 425 References [1] Yvonne M Badke, Ronald O Bates, Catherine W Ernst, Clint Schwab, Justin Fix, and Juan P Steibel. TagSNP selection and imputation accuracy using a reduced size haplotype panel in swine. submitted, [2] Brian L Browning. Documentation of BEAGLE 3.3.1, [3] Brian L Browning and Sharon R Browning. A Unified Approach to Genotype Imputation and Haplotype-Phase Inference for Large Data Sets of Trios and Unrelated Individuals. Am J Hum Genet, 84(2): , January [4] B. J. Hayes, P. J. Bowman, H. D. Daetwyler, J. W. Kijas, and J. H. J. van der Werf. Accuracy of genotype imputation in sheep breeds. Anim Genet, 43(1):72 80, February [5] John M. Hickey, Jose Crossa, Raman Babu, and Gustavo de los Campos. Factors Affecting the Accuracy of Genotype Imputation in Populations from Several Maize Breeding Programs. Crop Science, 52(2):654, [6] Z S Qin, S Gopalakrishnan, and G R Abecasis. An efficient comprehensive search algorithm for tagsnp selection using linkage disequilibrium criteria. Bioinformatics, 22(2): , January
15 438 [7] The R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria, [8] Valentin Wimmer, Theresa Albrecht, Hans-Jürgen Auinger, and Chris-Carolin Schön. synbreed: A framework for the analysis of genomic prediction data using R. Bioinformatics, 28(15):2086 7,
Accuracy of imputation using the most common sires as reference population in layer chickens
Heidaritabar et al. BMC Genetics (2015) 16:101 DOI 10.1186/s12863-015-0253-5 RESEARCH ARTICLE Open Access Accuracy of imputation using the most common sires as reference population in layer chickens Marzieh
More informationComparing performance of modern genotype imputation methods in different ethnicities
Comparing performance of modern genotype imputation methods in different ethnicities Nab Raj Roshyara 1,2, Katrin Horn 1, Holger Kirsten 1,2,3, Peter Ahnert 1,2 and Markus Scholz 1,2 1. Institute for Medical
More informationPredicting Wine Quality
March 8, 2016 Ilker Karakasoglu Predicting Wine Quality Problem description: You have been retained as a statistical consultant for a wine co-operative, and have been asked to analyze these data. Each
More informationConsequences of splitting whole-genome sequencing effort over multiple breeds on imputation accuracy
Bouwman and Veerkamp BMC Genetics 2014, 15:105 RESEARCH ARTICLE Open Access Consequences of splitting whole-genome sequencing effort over multiple breeds on imputation accuracy Aniek C Bouwman * and Roel
More informationAccuracy of genome-wide imputation in Braford and Hereford beef cattle
Piccoli et al. BMC Genetics (2014) 15:157 DOI 10.1186/s12863-014-0157-9 RESEARCH ARTICLE Open Access Accuracy of genome-wide imputation in Braford and Hereford beef cattle Mario L Piccoli 1,2,3, José Braccini
More informationImputing rare variants in families using a two-stage approach
The Author(s) BMC Proceedings 2016, 10(Suppl 7):48 DOI 10.1186/s12919-016-0032-y BMC Proceedings PROCEEDINGS Open Access Imputing rare variants in families using a two-stage approach Samantha Lent *, Xuan
More informationAccuracy of imputation to whole-genome sequence data in Holstein Friesian cattle
van Binsbergen et al. Genetics Selection Evolution 2014, 46:41 Genetics Selection Evolution RESEARCH Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle Rianne van Binsbergen
More informationEFFECT OF TOMATO GENETIC VARIATION ON LYE PEELING EFFICACY TOMATO SOLUTIONS JIM AND ADAM DICK SUMMARY
EFFECT OF TOMATO GENETIC VARIATION ON LYE PEELING EFFICACY TOMATO SOLUTIONS JIM AND ADAM DICK 2013 SUMMARY Several breeding lines and hybrids were peeled in an 18% lye solution using an exposure time of
More information1. Expressed in billions of real dollars, seasonally adjusted, annual rate.
ROUTPUT -- Real GNP/GDP 1. Expressed in billions of real dollars, seasonally adjusted, annual rate. 2. First Monthly Vintage: 1965:M11 First Quarterly Vintage: 1965:Q4 3. First Observation: 1947:Q1 4.
More informationAWRI Refrigeration Demand Calculator
AWRI Refrigeration Demand Calculator Resources and expertise are readily available to wine producers to manage efficient refrigeration supply and plant capacity. However, efficient management of winery
More informationImputation of multivariate continuous data with non-ignorable missingness
Imputation of multivariate continuous data with non-ignorable missingness Thais Paiva Jerry Reiter Department of Statistical Science Duke University NCRN Meeting Spring 2014 May 23, 2014 Thais Paiva, Jerry
More informationImproving Capacity for Crime Repor3ng: Data Quality and Imputa3on Methods Using State Incident- Based Repor3ng System Data
Improving Capacity for Crime Repor3ng: Data Quality and Imputa3on Methods Using State Incident- Based Repor3ng System Data July 31, 2014 Justice Research and Statistics Association 720 7th Street, NW,
More informationInstruction (Manual) Document
Instruction (Manual) Document This part should be filled by author before your submission. 1. Information about Author Your Surname Your First Name Your Country Your Email Address Your ID on our website
More informationMissing value imputation in SAS: an intro to Proc MI and MIANALYZE
Victoria SAS Users Group November 26, 2013 Missing value imputation in SAS: an intro to Proc MI and MIANALYZE Sylvain Tremblay SAS Canada Education Copyright 2010 SAS Institute Inc. All rights reserved.
More informationMultiple Imputation for Missing Data in KLoSA
Multiple Imputation for Missing Data in KLoSA Juwon Song Korea University and UCLA Contents 1. Missing Data and Missing Data Mechanisms 2. Imputation 3. Missing Data and Multiple Imputation in Baseline
More informationSTA Module 6 The Normal Distribution
STA 2023 Module 6 The Normal Distribution Learning Objectives 1. Explain what it means for a variable to be normally distributed or approximately normally distributed. 2. Explain the meaning of the parameters
More informationSTA Module 6 The Normal Distribution. Learning Objectives. Examples of Normal Curves
STA 2023 Module 6 The Normal Distribution Learning Objectives 1. Explain what it means for a variable to be normally distributed or approximately normally distributed. 2. Explain the meaning of the parameters
More informationWhere in the Genome is the Flax b1 Locus?
Where in the Genome is the Flax b1 Locus? Kayla Lindenback 1 and Helen Booker 2 1,2 Plant Sciences Department, University of Saskatchewan, Saskatoon, SK S7N 5A8 2 Crop Development Center, University of
More informationRelease Letter. Trufa
Release Letter Trufa 4.1.16 2016-04-22 Content 1 Summary... 3 2 What s New?... 3 2.1 Business Drivers Dependency Wheel... 3 2.2 Raw Data Synchronization Facility... 4 3 Prerequisites... 6 3.1 Trufa Access
More informationHandling Missing Data. Ashley Parker EDU 7312
Handling Missing Data Ashley Parker EDU 7312 Presentation Outline Types of Missing Data Treatments for Handling Missing Data Deletion Techniques Listwise Deletion Pairwise Deletion Single Imputation Techniques
More informationActivity 10. Coffee Break. Introduction. Equipment Required. Collecting the Data
. Activity 10 Coffee Break Economists often use math to analyze growth trends for a company. Based on past performance, a mathematical equation or formula can sometimes be developed to help make predictions
More informationWP Board 1054/08 Rev. 1
WP Board 1054/08 Rev. 1 9 September 2009 Original: English E Executive Board/ International Coffee Council 22 25 September 2009 London, England Sequencing the genome for enhanced characterization, utilization,
More informationWine-Tasting by Numbers: Using Binary Logistic Regression to Reveal the Preferences of Experts
Wine-Tasting by Numbers: Using Binary Logistic Regression to Reveal the Preferences of Experts When you need to understand situations that seem to defy data analysis, you may be able to use techniques
More informationBarista Document Output Object
Barista Document Output Object Description The Barista Document Output Object provides the ability to create and display reports outside of Barista in standalone mode. Note: The Barista environment used
More informationNVIVO 10 WORKSHOP. Hui Bian Office for Faculty Excellence BY HUI BIAN
NVIVO 10 WORKSHOP Hui Bian Office for Faculty Excellence BY HUI BIAN 1 CONTACT INFORMATION Email: bianh@ecu.edu Phone: 328-5428 Temporary Location: 1413 Joyner library Website: http://core.ecu.edu/ofe/statisticsresearch/
More informationTips for Writing the RESULTS AND DISCUSSION:
Tips for Writing the RESULTS AND DISCUSSION: 1. The contents of the R&D section depends on the sequence of procedures described in the Materials and Methods section of the paper. 2. Data should be presented
More informationGail E. Potter, Timo Smieszek, and Kerstin Sailer. April 24, 2015
Supplementary Material to Modelling workplace contact networks: the effects of organizational structure, architecture, and reporting errors on epidemic predictions, published in Network Science Gail E.
More informationWhy PAM Works. An In-Depth Look at Scoring Matrices and Algorithms. Michael Darling Nazareth College. The Origin: Sequence Alignment
Why PAM Works An In-Depth Look at Scoring Matrices and Algorithms Michael Darling Nazareth College The Origin: Sequence Alignment Scoring used in an evolutionary sense Compare protein sequences to find
More informationError rate for imputation from the Illumina BovineSNP50 chip to the Illumina BovineHD chip
Schrooten et al. Genetics Selection Evolution 2014, 46:10 Genetics Selection Evolution RESEARCH Open Access Error rate for imputation from the Illumina BovineSNP50 chip to the Illumina BovineHD chip Chris
More informationPSYC 6140 November 16, 2005 ANOVA output in R
PSYC 6140 November 16, 2005 ANOVA output in R Type I, Type II and Type III Sums of Squares are displayed in ANOVA tables in a mumber of packages. The car library in R makes these available in R. This handout
More informationThe Wild Bean Population: Estimating Population Size Using the Mark and Recapture Method
Name Date The Wild Bean Population: Estimating Population Size Using the Mark and Recapture Method Introduction: In order to effectively study living organisms, scientists often need to know the size of
More informationDecision making with incomplete information Some new developments. Rudolf Vetschera University of Vienna. Tamkang University May 15, 2017
Decision making with incomplete information Some new developments Rudolf Vetschera University of Vienna Tamkang University May 15, 2017 Agenda Problem description Overview of methods Single parameter approaches
More informationMissing Data: Part 2 Implementing Multiple Imputation in STATA and SPSS. Carol B. Thompson Johns Hopkins Biostatistics Center SON Brown Bag 4/24/13
Missing Data: Part 2 Implementing Multiple Imputation in STATA and SPSS Carol B. Thompson Johns Hopkins Biostatistics Center SON Brown Bag 4/24/13 Overview Reminder Steps in Multiple Imputation Implementation
More informationBiocides IT training Vienna - 4 December 2017 IUCLID 6
Biocides IT training Vienna - 4 December 2017 IUCLID 6 Biocides IUCLID training 2 (18) Creation and update of a Biocidal Product Authorisation dossier and use of the report generator Background information
More informationMapping and Detection of Downy Mildew and Botrytis bunch rot Resistance Loci in Norton-based Population
Mapping and Detection of Downy Mildew and Botrytis bunch rot Resistance Loci in Norton-based Population Chin-Feng Hwang, Ph.D. State Fruit Experiment Station Darr College of Agriculture Vitis aestivalis-derived
More informationwine 1 wine 2 wine 3 person person person person person
1. A trendy wine bar set up an experiment to evaluate the quality of 3 different wines. Five fine connoisseurs of wine were asked to taste each of the wine and give it a rating between 0 and 10. The order
More informationLevel 2 Mathematics and Statistics, 2016
91267 912670 2SUPERVISOR S Level 2 Mathematics and Statistics, 2016 91267 Apply probability methods in solving problems 9.30 a.m. Thursday 24 November 2016 Credits: Four Achievement Achievement with Merit
More informationDELAWARE COMPENSATION RATING BUREAU, INC. Proposed Excess Loss (Pure Premium) Factors
Exhibit 17b As Filed COMPENSATION RATING BUREAU, INC. Proposed Excess Loss (Pure Premium) Factors Proposed excess loss (pure premium) factors are shown on the last page of the attachment together with
More informationPackage cdltools. August 1, 2016
Package cdltools August 1, 2016 Title Tools to Download and Work with USDA Cropscape Data Version 0.11 Date 2016-07-26 Author Lu Chen and Jonathan Lisic Maintainer Jonathan Lisic
More informationMissing Data Methods (Part I): Multiple Imputation. Advanced Multivariate Statistical Methods Workshop
Missing Data Methods (Part I): Multiple Imputation Advanced Multivariate Statistical Methods Workshop University of Georgia: Institute for Interdisciplinary Research in Education and Human Development
More informationA Comparison of Approximate Bayesian Bootstrap and Weighted Sequential Hot Deck for Multiple Imputation
A Comparison of Approximate Bayesian Bootstrap and Weighted Sequential Hot Deck for Multiple Imputation Darryl V. Creel RTI International 1 RTI International is a trade name of Research Triangle Institute.
More informationUnit of competency Content Activity. Element 1: Organise coffee workstation n/a n/a. Element 2: Select and grind coffee beans n/a n/a
SITHFAB005 Formative mapping Formative mapping SITHFAB005 Prepare and serve espresso coffee Unit of competency Content Activity Element 1: Organise coffee workstation n/a n/a 1.1 Complete mise en place
More informationEffect of Breed on Palatability of Dry-Cured Ham. S.J. Wells, S.J. Moeller, H.N. Zerby, K.M. Irvin
Effect of Breed on Palatability of Dry-Cured Ham S.J. Wells, S.J. Moeller, H.N. Zerby, K.M. Irvin Abstract: The objective of the study was to assess the impact of genetic background (treatment) on palatability
More informationFlexible Imputation of Missing Data
Chapman & Hall/CRC Interdisciplinary Statistics Series Flexible Imputation of Missing Data Stef van Buuren TNO Leiden, The Netherlands University of Utrecht The Netherlands crc pness Taylor &l Francis
More informationWhat Makes a Cuisine Unique?
What Makes a Cuisine Unique? Sunaya Shivakumar sshivak2@illinois.edu ABSTRACT There are many different national and cultural cuisines from around the world, but what makes each of them unique? We try to
More informationBiocides IT training Helsinki - 27 September 2017 IUCLID 6
Biocides IT training Helsinki - 27 September 2017 IUCLID 6 Biocides IT tools training 2 (18) Creation and update of a Biocidal Product Authorisation dossier and use of the report generator Background information
More informationStructures of Life. Investigation 1: Origin of Seeds. Big Question: 3 rd Science Notebook. Name:
3 rd Science Notebook Structures of Life Investigation 1: Origin of Seeds Name: Big Question: What are the properties of seeds and how does water affect them? 1 Alignment with New York State Science Standards
More informationAJAE Appendix: Testing Household-Specific Explanations for the Inverse Productivity Relationship
AJAE Appendix: Testing Household-Specific Explanations for the Inverse Productivity Relationship Juliano Assunção Department of Economics PUC-Rio Luis H. B. Braido Graduate School of Economics Getulio
More informationTRTP and TRTA in BDS Application per CDISC ADaM Standards Maggie Ci Jiang, Teva Pharmaceuticals, West Chester, PA
PharmaSUG 2016 - Paper DS14 TRTP and TRTA in BDS Application per CDISC ADaM Standards Maggie Ci Jiang, Teva Pharmaceuticals, West Chester, PA ABSTRACT CDSIC ADaM Implementation Guide v1.1 (IG) [1]. has
More informationLabor Requirements and Costs for Harvesting Tomatoes. Zhengfei Guan, 1 Feng Wu, and Steven Sargent University of Florida
Labor Requirements and Costs for ing Tomatoes Zhengfei Guan, 1 Feng Wu, and Steven Sargent University of Florida Introduction Florida accounted for 30% to 40% of all commercially produced fresh-market
More informationEsri Demographic Data Release Notes: Israel
Introduction The Esri demographic dataset for Israel provides key population and household attributes for use in a variety of applications. Release notes provide information such as the attribute list,
More informationObjective: Decompose a liter to reason about the size of 1 liter, 100 milliliters, 10 milliliters, and 1 milliliter.
NYS COMMON CORE MATHEMATICS CURRICULUM Lesson 9 3 2 Lesson 9 Objective: Decompose a liter to reason about the size of 1 liter, 100 milliliters, 10 milliliters, and 1 milliliter. Suggested Lesson Structure
More informationMissing Data Treatments
Missing Data Treatments Lindsey Perry EDU7312: Spring 2012 Presentation Outline Types of Missing Data Listwise Deletion Pairwise Deletion Single Imputation Methods Mean Imputation Hot Deck Imputation Multiple
More informationCAUTION!!! Do not eat anything (Skittles, cylinders, dishes, etc.) associated with the lab!!!
Physical Science Period: Name: Skittle Lab: Conversion Factors Date: CAUTION!!! Do not eat anything (Skittles, cylinders, dishes, etc.) associated with the lab!!! Estimate: Make an educated guess about
More information1. Continuing the development and validation of mobile sensors. 3. Identifying and establishing variable rate management field trials
Project Overview The overall goal of this project is to deliver the tools, techniques, and information for spatial data driven variable rate management in commercial vineyards. Identified 2016 Needs: 1.
More informationYelp Chanllenge. Tianshu Fan Xinhang Shao University of Washington. June 7, 2013
Yelp Chanllenge Tianshu Fan Xinhang Shao University of Washington June 7, 2013 1 Introduction In this project, we took the Yelp challenge and generated some interesting results about restaurants. Yelp
More informationConfectionary sunflower A new breeding program. Sun Yue (Jenny)
Confectionary sunflower A new breeding program Sun Yue (Jenny) Sunflower in Australia Oilseed: vegetable oil, margarine Canola, cotton seeds account for >90% of oilseed production Sunflower less competitive
More informationMBA 503 Final Project Guidelines and Rubric
MBA 503 Final Project Guidelines and Rubric Overview There are two summative assessments for this course. For your first assessment, you will be objectively assessed by your completion of a series of MyAccountingLab
More informationNorth America Ethyl Acetate Industry Outlook to Market Size, Company Share, Price Trends, Capacity Forecasts of All Active and Planned Plants
North America Ethyl Acetate Industry Outlook to 2016 - Market Size, Company Share, Price Trends, Capacity Forecasts of All Active and Planned Plants Reference Code: GDCH0416RDB Publication Date: October
More information5 Populations Estimating Animal Populations by Using the Mark-Recapture Method
Name: Period: 5 Populations Estimating Animal Populations by Using the Mark-Recapture Method Background Information: Lincoln-Peterson Sampling Techniques In the field, it is difficult to estimate the population
More informationDirections for Menu Worksheet. General Information:
Directions for Menu Worksheet Welcome to the FNS Menu Worksheet, a tool designed to assist School Food Authorities (SFAs) in demonstrating that each of the menus meets the new meal pattern for the National
More informationChapter V SUMMARY AND CONCLUSION
Chapter V SUMMARY AND CONCLUSION Coffea is economically the most important genus of the family Rubiaceae, producing the coffee of commerce. Coffee of commerce is obtained mainly from Coffea arabica and
More informationLaboratory Performance Assessment. Report. Analysis of Pesticides and Anthraquinone. in Black Tea
Laboratory Performance Assessment Report Analysis of Pesticides and Anthraquinone in Black Tea May 2013 Summary This laboratory performance assessment on pesticides in black tea was designed and organised
More informationStructural Reforms and Agricultural Export Performance An Empirical Analysis
Structural Reforms and Agricultural Export Performance An Empirical Analysis D. Susanto, C. P. Rosson, and R. Costa Department of Agricultural Economics, Texas A&M University College Station, Texas INTRODUCTION
More informationReasons for the study
Systematic study Wittall J.B. et al. (2010): Finding a (pine) needle in a haystack: chloroplast genome sequence divergence in rare and widespread pines. Molecular Ecology 19, 100-114. Reasons for the study
More informationARM4 Advances: Genetic Algorithm Improvements. Ed Downs & Gianluca Paganoni
ARM4 Advances: Genetic Algorithm Improvements Ed Downs & Gianluca Paganoni Artificial Intelligence In Trading, we want to identify trades that generate the most consistent profits over a long period of
More informationDATA MINING CAPSTONE FINAL REPORT
DATA MINING CAPSTONE FINAL REPORT ABSTRACT This report is to summarize the tasks accomplished for the Data Mining Capstone. The tasks are based on yelp review data, majorly for restaurants. Six tasks are
More informationBarista at a Glance BASIS International Ltd.
2007 BASIS International Ltd. www.basis.com Barista at a Glance 1 A Brewing up GUI Apps With Barista Application Framework By Jon Bradley lmost as fast as the Starbucks barista turns milk, java beans,
More informationGrower Summary TF 170. Plums: To determine the performance of 6 new plum varieties. Annual 2012
Grower Summary TF 170 Plums: To determine the performance of 6 new plum varieties Annual 2012 Disclaimer AHDB, operating through its HDC division seeks to ensure that the information contained within this
More informationBiologist at Work! Experiment: Width across knuckles of: left hand. cm... right hand. cm. Analysis: Decision: /13 cm. Name
wrong 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 right 72 71 70 69 68 67 66 65 64 63 62 61 60 59 58 57 56 55 54 53 52 score 100 98.6 97.2 95.8 94.4 93.1 91.7 90.3 88.9 87.5 86.1 84.7 83.3 81.9
More informationIntroduction Methods
Introduction The Allium paradoxum, common name few flowered leek, is a wild garlic distributed in woodland areas largely in the East of Britain (Preston et al., 2002). In 1823 the A. paradoxum was brought
More informationDOMESTIC MARKET MATURITY TESTING
DOMESTIC MARKET MATURITY TESTING 1.0 General NZ Avocado working with the Avocado Packer Forum and NZ Market Group has agreed a maturity standard for the 2018 season. NZ Avocado is implementing an early
More informationA New Approach for Smoothing Soil Grain Size Curve Determined by Hydrometer
International Journal of Geosciences, 2013, 4, 1285-1291 Published Online November 2013 (http://www.scirp.org/journal/ijg) http://dx.doi.org/10.4236/ijg.2013.49123 A New Approach for Smoothing Soil Grain
More informationDirections for Menu Worksheet ***Updated 9/2/2014 for SY *** General Information:
Directions for Menu Worksheet ***Updated 9/2/2014 for SY 2014-15*** Welcome to the FNS Menu Worksheet, a tool designed to assist School Food Authorities (SFAs) in demonstrating that each of the menus meets
More informationInternet Appendix. For. Birds of a feather: Value implications of political alignment between top management and directors
Internet Appendix For Birds of a feather: Value implications of political alignment between top management and directors Jongsub Lee *, Kwang J. Lee, and Nandu J. Nagarajan This Internet Appendix reports
More informationKiwifruit Breeding & Genomics
Kiwifruit Breeding & Genomics Luis Gea, Breeding & Genomics Genetics for productivity» 3-4 years to flowering» 10 years breeding to production cycle» Progeny test + RRS» Backcross / inbreeding /sex Genetics
More informationActivity 2.3 Solubility test
Activity 2.3 Solubility test Can you identify the unknown crystal by the amount that dissolves in water? In Demonstration 2a, students saw that more salt is left behind than sugar when both crystals are
More informationTable Reservations Quick Reference Guide
Table Reservations Quick Reference Guide Date: November 15 Introduction This Quick Reference Guide will explain the procedures to create a table reservation from both Table Reservations and Front Desk.
More informationMissing Data Imputation Method Comparison in Ohio University Student Retention. Database. A thesis presented to. the faculty of
Missing Data Imputation Method Comparison in Ohio University Student Retention Database A thesis presented to the faculty of the Russ College of Engineering and Technology of Ohio University In partial
More informationA.P. Environmental Science. Partners. Mark and Recapture Lab addi. Estimating Population Size
Name A.P. Environmental Science Date Mr. Romano Partners Mark and Recapture Lab addi Estimating Population Size Problem: How can the population size of a mobile organism be measured? Introduction: One
More informationParent Self Serve Mobile
Parent Self Serve Mobile Parent Self Serve Mobile is the mobile web application version of TEAMS Parent Self Serve. Parent Self Serve Mobile honors the same configuration options that are used for the
More informationEffect of SPT Hammer Energy Efficiency in the Bearing Capacity Evaluation in Sands
Proceedings of the 2 nd World Congress on Civil, Structural, and Environmental Engineering (CSEE 17) Barcelona, Spain April 2 4, 2017 Paper No. ICGRE 123 ISSN: 2371-5294 DOI: 10.11159/icgre17.123 Effect
More informationMaximising Sensitivity with Percolator
Maximising Sensitivity with Percolator 1 Terminology Search reports a match to the correct sequence True False The MS/MS spectrum comes from a peptide sequence in the database True True positive False
More informationNotes on the Philadelphia Fed s Real-Time Data Set for Macroeconomists (RTDSM) Capacity Utilization. Last Updated: December 21, 2016
1 Notes on the Philadelphia Fed s Real-Time Data Set for Macroeconomists (RTDSM) Capacity Utilization Last Updated: December 21, 2016 I. General Comments This file provides documentation for the Philadelphia
More informationWhich of your fingernails comes closest to 1 cm in width? What is the length between your thumb tip and extended index finger tip? If no, why not?
wrong 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 right 66 65 64 63 62 61 60 59 58 57 56 55 54 53 52 51 50 49 score 100 98.5 97.0 95.5 93.9 92.4 90.9 89.4 87.9 86.4 84.8 83.3 81.8 80.3 78.8 77.3 75.8 74.2
More informationAdvancing Agriculture Grape Industry Development Program
2017-2018 Advancing Agriculture Grape Industry Development Program 1) Objectives: To provide assistance for the establishment of new or more productive vineyards. To assist with the adoption of new technologies
More information1.3 Box & Whisker Plots
1.3 Box & Whisker Plots Box and Whisker Plots or box plots, are useful for showing the distribution of values in a data set. The box plot below is an example. A box plot is constructed from the five-number
More informationFibonacci Numbers: How To Use Fibonacci Numbers To Predict Price Movements [Kindle Edition] By Glenn Wilson
Fibonacci Numbers: How To Use Fibonacci Numbers To Predict Price Movements [Kindle Edition] By Glenn Wilson If you are searching for a book by Glenn Wilson Fibonacci Numbers: How to Use Fibonacci Numbers
More informationCafeteria Ordering System, Release 1.0
Software Requirements Specification for Cafeteria Ordering System, Release 1.0 Version 1.0 approved Prepared by Karl Wiegers Process Impact November 4, 2002 Software Requirements Specification for Cafeteria
More informationA Note on a Test for the Sum of Ranksums*
Journal of Wine Economics, Volume 2, Number 1, Spring 2007, Pages 98 102 A Note on a Test for the Sum of Ranksums* Richard E. Quandt a I. Introduction In wine tastings, in which several tasters (judges)
More informationGLOSSARY Last Updated: 10/17/ KL. Terms and Definitions
GLOSSARY Last Updated: 10/17/2017 - KL Terms and Definitions Spacing 4ETa Zone(s) Background Drill Elevation Climate Soil Ecoregion 4 Recommended base spacing between containerized, cutting, plug or sprig
More informationNotes on the Philadelphia Fed s Real-Time Data Set for Macroeconomists (RTDSM) Indexes of Aggregate Weekly Hours. Last Updated: December 22, 2016
1 Notes on the Philadelphia Fed s Real-Time Data Set for Macroeconomists (RTDSM) Indexes of Aggregate Weekly Hours Last Updated: December 22, 2016 I. General Comments This file provides documentation for
More informationExperiment # Lemna minor (Duckweed) Population Growth
Experiment # Lemna minor (Duckweed) Population Growth Introduction Students will grow duckweed (Lemna minor) over a two to three week period to observe what happens to a population of organisms when allowed
More informationof Vitis vinifera using
Characterisation of the pan-genome of Vitis vinifera using Next Generation Sequencing Plant Biology Europe 2018 - June 18-21 - Copenhagen Gabriele Magris (gmagris@appliedgenomics.org) Genetic variation
More informationOF THE VARIOUS DECIDUOUS and
(9) PLAXICO, JAMES S. 1955. PROBLEMS OF FACTOR-PRODUCT AGGRE- GATION IN COBB-DOUGLAS VALUE PRODUCTIVITY ANALYSIS. JOUR. FARM ECON. 37: 644-675, ILLUS. (10) SCHICKELE, RAINER. 1941. EFFECT OF TENURE SYSTEMS
More information#611-7 Workbook REVIEW OF PERCOLATION TESTING PROCEDURES. After completing this chapter, you will be able to...
REVIEW OF PERCOLATION 7 TESTING PROCEDURES CHAPTER OBJECTIVES: After completing this chapter, you will be able to... Discuss the purpose of a percolation test. List the regulatory requirements for conducting
More informationDeveloping Machine-Harvestable Fresh Market Tomatoes; and other Highlights from the UF Breeding Program
Developing Machine-Harvestable Fresh Market Tomatoes; and other Highlights from the UF Breeding Program S.F. Hutton, J.W. Scott, B.M. Santos 813-633-4137 sfhutton@ufl.edu Comparison of once-over harvest
More informationGrapes of Class. Investigative Question: What changes take place in plant material (fruit, leaf, seed) when the water inside changes state?
Grapes of Class 1 Investigative Question: What changes take place in plant material (fruit, leaf, seed) when the water inside changes state? Goal: Students will investigate the differences between frozen,
More informationPackage frambgrowth. April 24, 2018
Package frambgrowth April 24, 2018 Version 0.1.0 Title Simulation of the Growth of Framboidal and Sunflower Pyrite Description Generation of theoretical size distributions of framboidal or sunflower pyrite.
More informationQuality of Canadian non-food grade soybeans 2014
ISSN 1705-9453 Quality of Canadian non-food grade soybeans 2014 Ann S. Puvirajah Chemist, Oilseed Services Contact: Ann S. Puvirajah Chemist, Oilseeds Services Tel: 204-983-3354 Email: ann.puvirajah@grainscanada.gc.ca
More information