ライフサイエンスコーパス: imputation

1 part B was PASI 90 at week 44 (non-responder imputation).

2 lyses such as haplotype phasing and genotype imputation.

3 tic information to allow genome-wide genetic imputation.

4 ired processing steps prior to and following imputation.

5 y genomics dataset collections without using imputation.

6 ch to minimize the feature selection bias on imputation.

7 patterns in the data, allowing for accurate imputation.

8 tion-to-treat (ITT) population with multiple imputation.

9 le method for trans-omics block missing data imputation.

10 s done using Affymetrix arrays, augmented by imputation.

11 unwanted bias towards expressed genes during imputation.

12 IBD configurations to substantially improve imputation.

13 esponding values in the combined panel after imputation.

14 nce patterns within families yielding better imputation.

15 sed using multilevel modelling with multiple imputation.

16 visualization, clustering, and denoising and imputation.

17 chnical effects, and provide a framework for imputation.

18 mputation, with imputation and adjusted with imputation.

19 story was accounted for through modeling and imputation.

20 de genotyping supplemented with high-density imputation.

21 putation, with imputation, and adjusted with imputation.

22 whole genome sequencing, and/or array-based imputation.

23 assigned treatment group using non-responder imputation.

24 captured by array-based genotyping and deep imputation.

25 follow-up, with 91.2% requiring model-based imputation.

26 g data were imputed with the use of multiple imputation.

27 Missing data were handled with multiple imputations.

28 e enoxaparin group (risk ratio with multiple imputation, 0.25; 95% confidence interval, 0.09 to 0.75;

29 After quality control and imputation, a linear regression-based association analys

30 nibus approach GMSimpute, to allow effective imputation accommodating different missing patterns.

31 Multiple imputation accounting for loss to follow-up yielded simi

32 We achieved average imputation accuracies higher than 0.924 for the commonly

33 rame of ~2 Mb containing 314 SNPs, we obtain imputation accuracy (r(2)) between 0.4 and 0.9 (median 0

34 ch achieved an average of 39% improvement in imputation accuracy and generated effective imputation m

35 lly, we determine the factors that influence imputation accuracy and provide guidelines for implement

36 I performed worst with the exception of good imputation accuracy for common variants when a closely a

37 Neolithic Hungarian genome, we obtain ~ 90% imputation accuracy for heterozygous common variants at

38 ization of a low-density panel increases the imputation accuracy for IllumHD, AffyHD and the combined

39 ty scores (IQS) especially for LFV, although imputation accuracy from MERLIN depends on pedigree spli

40 and GIGI outperformed other approaches with imputation accuracy greater than 0.99 for the squared co

41 Measures of imputation accuracy improved significantly, particularly

42 Moreover, we compare two imputation accuracy metrics: the Imputation Quality Scor

43 Our method achieved a better imputation accuracy over a set of competing methods on b

44 CUE outperforms existing methods in terms of imputation accuracy which leads to more precise cell-typ

45 the enrichment test, properly adjusting for imputation accuracy, model incompleteness and redundancy

46 cifically, population differences may affect imputation accuracy, risk magnitude and direction.

47 ation reference panel had a strong effect on imputation accuracy, with the 1000 Genomes Project Phase

48 terion; and (5) R (2) is the best measure of imputation accuracy.

49 Genome imputation, admixture resolution and genome-wide associa

50 sent DeepImpute, a deep neural network-based imputation algorithm that uses dropout layers and loss f

51 e various machine learning and missing value imputation algorithms to implement LEXI and demonstrate

52 indicate that BayesMetab outperformed other imputation algorithms when there is a mixture of missing

53 hods offered by 23 widely used missing-value imputation algorithms.

54 sed AUC in all year 3 models with or without imputation (all P < 0.05).

55 MICE imputation allowed us to impute missing values for clini

56 Similar results were found when using imputation analyses that included those lost to follow-u

57 to estimate the CFR without imputation, with imputation and adjusted with imputation.

58 ployed when there is incompatibility between imputation and analysis models.

59 R)) is biased due to incompatibility between imputation and analysis models.

60 ified, efficient, semi-automated genome-wide imputation and analysis pipeline, which prepares raw gen

61 ient and unified approach for normalization, imputation and batch effect correction.

62 rate than conventional methods like multiple imputation and comparable to missForest while achieving

63 equire new methods for handling missing data imputation and differential proteome analysis.

64 GWAS studies including quality control (QC), imputation and downstream analyses.

65 s show that scIGANs is effective for dropout imputation and enhances various downstream analysis.

66 dicate GMSimpute provides higher accuracy in imputation and exhibits stable performance across differ

67 2 appropriate missing data methods: multiple imputation and full information maximum likelihood.

68 We carried out multiple imputation and generated multivariable regression models

69 sub-distribution hazard ratio after multiple imputation and internal validation by bootstrapping.

70 masked, we used Impute2 to perform LD-based imputation and Kinpute was used to obtain higher accurac

71 ficient open source implementation of an HLA imputation and match algorithm using a graph database pl

72 Referencing to multi-hot deck, mean imputation and missing cases deletion, we assess the rel

73 ikelihood calling methods, post-calling, pre-imputation and post-imputation filters, different refere

74 By building LIMBR for imputation and time series tailored bias modeling into o

75 d solution for global scaling normalization, imputation and true count recovery of gene expression me

76 ved the naive CFR estimates obtained without imputation and were more representative.

77 mpare our methods to mean imputation, single imputation, and a penalized EM algorithm incorporating n

78 to estimate the CFR without imputation, with imputation, and adjusted with imputation.

79 We apply preprocessing, MICE imputation, and sparse principal component analysis (SPC

80 Statistical imputation applied to genome-wide array data is the most

81 Here, we present a reparameterized imputation approach for measurement error (RIME) that ca

82 Accordingly, we applied an imputation approach, Avocado, to a dataset of 3814 track

83 Transcriptomic imputation approaches combine eQTL reference panels with

84 population-based imputation outperforms all imputation approaches for all minor allele frequencies;

85 Data imputation approaches that aim to estimate missing regul

86 d and left-truncated observations, but these imputations are hidden and therefore sometimes unrecogni

87 redistribution algorithm, we illustrate how imputations are made by the KM estimator.

88 P]) of 3.2 or lower, both with non-responder imputation at week 14.

89 ontains more than 1.5 million markers and an imputation backbone that successfully tags over 94% of c

90 parable or even available, we implemented an imputation-based approach that only requires mass-to-cha

91 test three fine-mapping approaches (Bayesian imputation-based association mapping, Bayesian sparse li

92 ng with missing values obviates the need for imputation-based pre-processing of the data, while at th

93 tion, which is crucial in scRNA-seq drop-out imputation but has not been appropriately examined.

94 ue that Segal's approach is akin to multiple imputation but with the outcome variable omitted from th

95 ue that Segal's approach is akin to multiple imputation, but with the outcome variable omitted from t

96 Imputation was performed using multiple imputation by chained equations to assess effect of miss

97 Multiple imputation by chained equations was used to account for

98 Missing data were handled with multiple imputation by chained equations.

99 t a reference panel (N = 3,541) for genotype imputation by integrating the whole-genome sequence data

100 After imputation by scHiCluster, topologically associating dom

101 e, and non-relapse mortality, using multiple imputations by chained equations to deal with missing da

102 In contrast, highly accurate imputation can be achieved even if a small number (<200)

103 Our results demonstrate that BLR and data imputation can be used to obtain improved risk stratific

104 We show that imputation can mitigate, though not eliminate reference

105 Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymo

106 We investigated allele-specific imputation challenges in regard to geographic origin of

107 on accuracy of diagnostic codes and lab test imputations compared to the state-of-art methods.

108 Linear models in combination with multiple-imputation could significantly outperform a t-test-based

109 ion, haplotype pre-phasing, imputation, post imputation, data management and the extension to other e

110 P can also handle with missing genotype data imputations efficiently.

111 superiority over the other three methods in imputation error and stability of correlation structure.

112 el, and the J-test), assessing the impact of imputation errors and the choice of reference panel by u

113 These imputation errors had an impact on whether the CpG-SNP w

114 t side and cross-species information improve imputation, especially in data-scarce settings.

115 nt-corrected maximum likelihood and multiple-imputation estimation procedures that permit valid and e

116 Family-based imputation (FBI) provides an opportunity to evaluate LFV

117 thods, post-calling, pre-imputation and post-imputation filters, different reference panels, as well

118 e available genotype resources provides good imputation for common variants with well-selected refere

119 We implement Learning and Imputation for Mass-spec Bias Reduction (LIMBR) software

120 unt for other sources of bias, like multiple imputation for measurement error (MIME), rely on interna

121 rimary analyses were conducted with multiple imputation for missing BMI.

122 We used multiple imputation for missing covariates, logistic regression t

123 Sensitivity analyses based on multiple imputation for missing data yielded P values for the pri

124 ith significance testing when using multiple-imputation for missing data.

125 sychiatric recording accuracy, with multiple imputation for missing data.

126 ce by week 24 (also calculated with multiple imputation for missing responses) was 0.73 (95% CI, 0.54

127 the placebo group (risk ratio after multiple imputation for missing responses, 0.66; 95% confidence i

128 d in sensitivity analyses that used multiple imputation for missing values in the overall cohort of 1

129 ased imputation outperforms population-based imputation for rare variants but not for common ones; (3

130 ddress these limitations by presenting a new imputation framework, called Extensible Matrix Factoriza

131 While methods of accurate genotype imputation from > 1 x coverage data have recently become

132 Imputation from a national survey estimated that 4,200 R

133 n ancient genomes, outperforms a single-step imputation from genotype likelihoods, suggesting that cu

134 This study compared imputation from lower-density commercial and customized

135 enotyped genome wide, achieving high-quality imputation from markers with minor allele frequency (MAF

136 ows gains in gene discovery when using dense imputation from multi-ethnic whole-genome sequencing dat

137 ed parameters to obtain a very high accuracy imputation from summary statistics.

138 segmentation, reversing the typical order of imputation/genome segmentation pipelines.

139 d patients using both family-based and 'deep imputation' genotype-based approaches.

140 quencing to serve as the reference panel for imputation: GIGI-Pick, ExomePicks, PRIMUS, and random se

141 However, existing computational methods for imputation have largely been developed for and applied i

142 In the research setting, imputation helps avoid costs for wet lab-based HLA typin

143 Illustrating imputations hidden by the KM estimator helps to clarify

144 our data can substantially improve genotype imputation in diverse Asian and Oceanian populations.

145 scRNA-seq imputation methods outperformed no imputation in recovering gene expression observed in bul

146 the Nord-Trondelag Health Study, followed by imputation in the remaining sample (N = 19,705), and ide

147 rmance in downstream analyses compared to no imputation, in particular for clustering and trajectory

148 Genotype imputation is essential for genome-wide association stud

149 Multiple imputation is increasingly used to address the presence

150 Our proposed missing value imputation is more accurate than conventional methods li

151 ed rank tuning method based on missing value imputation is theoretically superior to existing methods

152 Genotype imputation is widely used in genome-wide association stu

153 d trio cohorts, (ii) genome-wide phasing and imputation, (iv) association analysis, (v) meta-analysis

154 Here, we present a joint-tissue imputation (JTI) approach and a Mendelian randomization

155 The lncRNA expression imputation (LEXI) framework enables characterization of

156 Here, we present an imputation method based on inferring the least epistatic

157 We developed 2DImpute, an imputation method for correcting false zeros (known as d

158 n omics data but also represents a competing imputation method for the scRNA-seq data.

159 proposed a k-nearest neighbor (kNN) weighted imputation method for trans-omics block missing data (TO

160 Notably, JTI includes the single-tissue imputation method PrediXcan as a special case and outper

161 Here, we describe a novel imputation method that reduces dropout effects in single

162 We objectively compare two imputation methods along with significance testing when

163 ent a new approach that improve the existing imputation methods and reach a precision suitable for mu

164 Current state-of-the-art imputation methods are tailored to samples from a specif

165 ed that 2DImpute outperforms several leading imputation methods by applying it on datasets from vario

166 communication-efficient distributed multiple imputation methods for incomplete data that are horizont

167 All imputation methods had increased power over complete-cas

168 Although many imputation methods have been developed, there is no cons

169 A wide variety of imputation methods have been established to deal with th

170 lts show that scDoc outperforms the existing imputation methods in reference to data visualization, c

171 of population-based (subjects are unrelated) imputation methods is well established.

172 performance of family- and population-based imputation methods on family data has been subject to mu

173 compare several family- and population-based imputation methods on family data of large pedigrees wit

174 ted the impact of missing values and feature imputation methods on two previously published autism de

175 We found that the majority of scRNA-seq imputation methods outperformed no imputation in recover

176 NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experime

177 form a systematic evaluation of 18 scRNA-seq imputation methods to assess their accuracy and usabilit

178 utput from linkage disequilibrium (LD) based imputation methods to compute more accurate genotype pro

179 combines the results obtained from multiple imputation methods to generate a more accurate result.

180 oving systematic technical noises, including imputation methods, which aim to address the increased s

181 Multiple imputation (MI) is a well-established method for dealing

182 oint (p = 0.014); using ANCOVA with Multiple Imputation (MI) method, the between-group difference was

183 ce, and thus, it is important to consider an imputation model that accounts for a mixture of missing

184 ery outcome variable must be included in the imputation model to avoid biasing associations towards t

185 t with the outcome variable omitted from the imputation model, while Kim's is akin to regression cali

186 t with the outcome variable omitted from the imputation model, while Kim's is akin to regression cali

187 By using the gene expression imputation models fitted from reference datasets that ha

188 metric Bayesian method fitted transcriptomic imputation models for 57.8% more genes over PrediXcan, t

189 imputation accuracy and generated effective imputation models for an average of 120% more genes.

190 llustrate the utility of transcriptome-based imputation models for discovery of cardiometabolic effec

191 We provide a resource of imputation models generated from GTEx and PsychENCODE pa

192 ols like PrediXcan and FUSION use parametric imputation models that have limitations for modeling the

193 l because it includes both of the parametric imputation models used by PrediXcan and FUSION as specia

194 s challenging to develop robust and accurate imputation models with a limited sample size for any sin

195 Missing data were handled by multiple imputation of 50 datasets.

196 alth Evaluation IV centers alone or multiple imputation of Acute Physiology and Chronic Health Evalua

197 ere, we evaluate a two-step pipeline for the imputation of common variants in ancient genomes at 0.05

198 Imputation of dropout events that may mislead downstream

199 able interface for performing visualization, imputation of gene dropouts, detection of rare transcrip

200 at geneEXPLORE provides a means for accurate imputation of gene expression, which can be further used

201 ence data set allows for high resolution HLA imputation of genotypes at all classical HLA class I and

202 Imputation of HLA types mapped this signal to a haplotyp

203 Imputation of HLA-DPB1 alleles revealed increased risk o

204 sed HLA, single-nucleotide-variant and indel imputation of large-scale genome-wide-association-study

205 sely sequenced data in family members, while imputation of LFV with FBI benefits more from informatio

206 chine learning algorithm, Precise Read-Level Imputation of Methylation (PReLIM), to increase coverage

207 labeling data from overlapping peptides, the imputation of missing data, and a normalization routine

208 d expression data that bayNorm allows robust imputation of missing values generating realistic transc

209 Validation and imputation of mtDNA variants was followed by single-vari

210 or the population founders, have allowed the imputation of rich sequence information into the descend

211 NA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns.

212 Although methods for the imputation of summary statistics exist, they lack precis

213 Genotype imputation of the human leukocyte antigen (HLA) region i

214 ScisTree enables new applications including imputation of the so-called doublets.

215 te enhancers, functional in zebrafish, allow imputation of tissue-specific and shared patterns of tra

216 Allele-frequency-based imputation of tumor (All-FIT) is an iterative weighted l

217 -locked entries, and estimated the effect of imputation on observed migraine frequencies.

218 acts as an effective regularization for data imputation on unassayed CpG sites, enabling transfer of

219 s compare favorably with approaches based on imputation or other strategies for handling missing data

220 ; (3) combining family- and population-based imputation outperforms all imputation approaches for all

221 1) GIGI outperforms Merlin; (2) family-based imputation outperforms population-based imputation for r

222 AlphaFamImpute is an imputation package for calling, phasing and imputing gen

223 mputed to the Haplotype Reference Consortium imputation panel.

224 Wide Association (GWA) with population-based imputation (PBI) has been successful in identifying comm

225 udy is the first to extensively evaluate the imputation performance of many available family- and pop

226 KoreanChip achieved higher imputation performance owing to the excellent genomic co

227 meaningful clusterings and state-of-the-art imputation performance.

228 veloped Gimpute, an automated processing and imputation pipeline for genome-wide association data.

229 on outlier detection, haplotype pre-phasing, imputation, post imputation, data management and the ext

230 rms pre-imputation quality control, phasing, imputation, post-imputation quality control, population

231 fter last observation carried forward (LOCF) imputation (primary analysis), the PANSS total score red

232 ent in the imputation, we propose a multiple imputation procedure, which provides a set of plausible

233 Single imputation procedures, while an improvement on previousl

234 ent laboratories with diverse genotyping and imputation protocols.

235 hich prepares raw genetic data, performs pre-imputation quality control, phasing, imputation, post-im

236 n quality control, phasing, imputation, post-imputation quality control, population stratification an

237 ty-one thousand nine hundred twenty SNPs met imputation quality of r(2) > 0.7 and minor allele freque

238 with minor allele frequency (MAF) > 0.1% and imputation quality score > 0.3.

239 compare two imputation accuracy metrics: the Imputation Quality Score and Pearson's correlation R (2)

240 er than 0.99 for the squared correlation and imputation quality scores (IQS) especially for LFV, alth

241 igree, and results in substantially improved imputation quality.

242 e nonparametric Bayesian model improved both imputation R(2) for transcriptomic data and the TWAS pow

243 The choice of imputation reference panel had a strong effect on imputa

244 Yet, most HLA imputation reference panels target Caucasian ethnicities

245 s using custom genotyping microarrays, large imputation reference panels, and functional annotation a

246 est sequence panel from Africa to date as an imputation resource.

247 NAguideR further evaluates data imputation results through classic computational criteri

248 ecific reference panel (SSRP) to improve the imputation results.

249 which combines family- and population-based imputation results.

250 edicting power of association analysis using imputation results.

251 ve adversarial networks (GANs) for scRNA-seq imputation (scIGANs), which uses generated cells rather

252 using VCF (outputted from Michigan or Sanger imputation servers), IMPUTE2 or PLINK files.

253 Multiple imputation should be used to account for missing data wh

254 We compare our methods to mean imputation, single imputation, and a penalized EM algori

255 ype probabilities output from other LD-based imputation software, and uses a new method to combine th

256 association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detec

257 matched controls for a large transcriptomic imputation study of schizophrenia.

258 th missing data points, we also integrate an imputation system.

259 subsets (GPL96-570 and LINCS), and multiple imputation tasks (within and across microarray/RNA-seq d

260 Multiple imputation techniques are also used to handle missing va

261 where inclusion criteria are assessed after imputation, the popular multiple-imputation variance est

262 Genotype imputation, though generally accurate, often results in

263 re-processing steps such as normalization or imputation to account for excessive zeros or "drop-outs.

264 pants was performed with the use of multiple imputation to account for missing data.

265 abilization, normalization, and missing data imputation to account for the large dynamic range of PTM

266 opted gene selection, dimension reduction or imputation to address the dropouts.

267 ng the synthetic control method and multiple imputation to adjust for changes in hospital usage and m

268 egression with Variable Inputs (RLRVI), uses imputation to estimate values for missing features.

269 genotype-based heritability (h2SNP) by deep imputation to Haplotype Reference Consortium and the 100

270 tionally costly and propagates any errors in imputation to produce incorrect genome segmentation resu

271 ted this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profi

272 we benchmarked the imputation using the HLA imputation tool HIBAG, our multi-ethnic reference and an

273 r rate, caution should be taken before using imputation tools for clinical or research purposes, espe

274 erent reference panels, as well as different imputation tools.

275 onal age at birth and country, with multiple imputation used to account for missing data.

276 It performs imputation using a two-step approach.

277 tensive cross-validation, we benchmarked the imputation using the HLA imputation tool HIBAG, our mult

278 8 or its proxies could not be tested through imputation using these existing resources.

279 m for Hi-C contact matrices that is based on imputations using linear convolution and random walk.

280 n this motivating example, the corresponding imputation variance estimate for the log odds was 29% sm

281 The RW imputation variance estimator performed much better and

282 cohort, we illustrate the calculation of an imputation variance estimator proposed by Robins and Wan

283 essed after imputation, the popular multiple-imputation variance estimator proposed by Rubin ("Rubin'

284 Imputation was performed using multiple imputation by ch

285 re conducted on each individual dataset, and imputation was performed using the 1000 Genomes Project

286 Imputation was shown to improve prognostic ability of sp

287 For all outcomes, multiple imputation was used to account for missing data.

288 , weighted logistic regression with multiple imputation was used to assess the associations between p

289 proportional hazard regression with multiple imputations was used to evaluate the association of circ

290 Using imputation, we added 43,013 and 86,042 markers to the Sy

291 After genotyping and imputation, we performed association mapping using 6.3 m

292 account for the uncertainty inherent in the imputation, we propose a multiple imputation procedure,

293 Through direct genotyping and imputation, we tested almost 7 million genetic variants

294 Bootstrap and multiple imputation were used to handle missing data.

295 Dense genotyping and genotype imputation were used to refine mapping around GWAS hits.

296 To account for missing values, multiple imputations were performed.

297 Kinpute uses a novel method for IBD imputation, which works even in the absence of a pedigre

298 We used multiple imputation with chained equations to impute values for m

299 del imperfection to estimate the CFR without imputation, with imputation and adjusted with imputation

300 del imperfection to estimate the CFR without imputation, with imputation, and adjusted with imputatio