RNASeq_similarity_matrix: visually identify sample mix-ups in RNASeq data using a ‘genomic’ sequence similarity matrix

Kist, Nicolaas Christiaan; Power, Robert A.; Skelton, Andrew; Seegobin, Seth; Verbelen, Moira; Bonde, Bushan; Malki, Karim

doi:10.1093/bioinformatics/btz821

Cited by 3 publications

(7 citation statements)

References 7 publications

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“…Our study expands significantly upon the previous findings derived from comparisons of 4 EpKS and contralateral uninvolved control skin samples solely from ART treated patients [29]. As reported by Kist et al [93], one control skin sample in that analysis was even sequenced twice. Here, we show a much more comprehensive analysis of a substantially more encompassing set of KS patients and controls that not only provides the new insights summarized above but has confirmed the overall conclusions of our previous study.…”

Section: Plos Pathogenssupporting

confidence: 80%

Comparative transcriptome analysis of endemic and epidemic Kaposi’s sarcoma (KS) lesions and the secondary role of HIV-1 in KS pathogenesis

et al. 2020

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In sub-Saharan Africa, endemic Kaposi's sarcoma (EnKS) is still prevalent despite high incidence of epidemic Kaposi's sarcoma (EpKS) resulting from the ongoing HIV-1 epidemic. While KSHV is clearly the etiologic agent of KS, the mechanisms underlying KS development are not fully understood. For example, HIV-1 co-infection and concomitant immune dysfunction have been associated with EpKS development. However, the direct or indirect role(s) of HIV-1, and therefore of immune suppression, in EpKS remains unclear. How, or whether, EpKS is mechanistically distinct from EnKS is unknown. Thus, the absence of HIV-1 co-infection in EnKS provides a unique control for investigating and deciphering whether HIV-1 plays a direct or indirect role in the EpKS tumor microenvironment. We hypothesized that HIV-1 co-infection would induce transcriptome changes that differentiate EpKS from EnKS, thereby defining the direct intra-tumor role of HIV-1 in KS. Comparison of ARTtreated and-naïve patients would further define the impact of ART on the KS transcriptome. We utilized RNA-seq followed by multiparameter bioinformatics analysis to compare transcriptomes from KS lesions to uninvolved control skin. We provide the first transcriptomic comparison of EpKS versus EnKS, ART-treated vs-naïve EpKS and male vs female EpKS to define the roles of HIV-1 co-infection, the impact of ART, and gender on KS gene expression profiles. Our findings suggest that ART-use and gender have minimal impact on transcriptome profiles of KS lesions. Gene expression profiles strongly correlated between EpKS and EnKS patients (Spearman r = 0.83, p<10 −10). A subset of genes involved in tumorigenesis and inflammation/immune responses showed higher magnitude, but not unique dysregulation in EnKS compared to EpKS. While gender and ART had no detectable contribution, the trend toward higher magnitude of gene dysregulation in EnKS coupled with

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Section: Plos Pathogenssupporting

confidence: 80%

Comparative transcriptome analysis of endemic and epidemic Kaposi’s sarcoma (KS) lesions and the secondary role of HIV-1 in KS pathogenesis

et al. 2020

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“…Others reported that roughly 2% 6 or 3% 1 of all samples analyzed, including samples from a global network of biobanks 6 or publicly available genomic data from multiple studies, 1 were paired with the wrong metadata. The high prevalence of these errors, and the recognition that even a small number of errors can negatively impact study integrity, has led to the adoption of quality control protocols designed to detect problematic samples in genomic and transcriptomic studies 1,6–9 . However, the causes of sample misannotation are not limited to genomic analysis.…”

Section: Introductionmentioning

confidence: 99%

“…The high prevalence of these errors, and the recognition that even a small number of errors can negatively impact study integrity, has led to the adoption of quality control protocols designed to detect problematic samples in genomic and transcriptomic studies. 1,[6][7][8][9] However, the causes of sample misannotation are not limited to genomic analysis. These types of issues can also occur in cytometry datasets, but there are no established methods for the molecular identification of a sample from information captured within cytometry data.…”

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confidence: 99%

A simple strategy for sample annotation error detection in cytometry datasets

Smithmyer¹,

Wiedeman²,

Skibinski

et al. 2021

Cytometry Pt A

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Mislabeling samples or data with the wrong participant information can affect study integrity and lead investigators to draw inaccurate conclusions. Quality control to prevent these types of errors is commonly embedded into the analysis of genomic datasets, but a similar identification strategy is not standard for cytometric data.Here, we present a method for detecting sample identification errors in cytometric data using expression of human leukocyte antigen (HLA) class I alleles. We measured HLA-A*02 and HLA-B*07 expression in three longitudinal samples from 41 participants using a 33-marker CyTOF panel designed to identify major immune cell types.3/123 samples (2.4%) showed HLA allele expression that did not match their longitudinal pairs. Furthermore, these same three samples' cytometric signature did not match qPCR HLA class I allele data, suggesting that they were accurately identified as mismatches. We conclude that this technique is useful for detecting samplelabeling errors in cytometric analyses of longitudinal data. This technique could also be used in conjunction with another method, like GWAS or PCR, to detect errors in cross-sectional data. We suggest widespread adoption of this or similar techniques will improve the quality of clinical studies that utilize cytometry.

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“…Others reported that roughly 2% (6) or 3% (1) of all samples analyzed, including samples from a global network of biobanks (6) or publicly available genomic data from multiple studies (1), were paired with the wrong metadata. The high prevalence of these errors, and the recognition that even a small number of errors can negatively impact study integrity, has led to the adoption of quality control protocols designed to detect problematic samples in genomic and transcriptomic studies (1,(6)(7)(8)(9). However, the causes of sample misannotation are not limited to genomic analysis.…”

Section: Introductionmentioning

confidence: 99%

“…While this approach has several limitations including the inability to identify sample mix-ups that occur between participants of the same sex and potential false positives in participants with sex chromosome anomalies, it has been used to successfully identify annotation errors in publicly available datasets (2). Another widely implemented method utilizes single nucleotide polymorphisms (SNPs) to identify samples collected from the same individual, allowing investigators to detect a larger proportion of errors in longitudinal datasets (1,8,9).…”

Section: Introductionmentioning

confidence: 99%

A simple strategy for sample annotation error detection in cytometry datasets

Smithmyer¹,

Wiedeman²,

Skibinski³

et al. 2021

Preprint

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Mislabeling samples or data with the wrong participant information can impact study integrity and lead investigators to draw inaccurate conclusions. Quality control to prevent these types of errors is commonly embedded into the analysis of genomic datasets, but a similar identification strategy is not standard for cytometric data. Here, we present a method for detecting sample identification errors in cytometric data using expression of HLA class I alleles. We measured HLA-A*02 and HLA-B*07 expression in 3 longitudinal samples from 41 participants using a 33-marker CyTOF panel designed to identify major immune cell types. 3/123 samples (2.4%) showed HLA allele expression that did not match their longitudinal pairs. Furthermore, these same three samples’ cytometric signature did not match qPCR HLA class I allele data, suggesting that they were accurately identified as mismatches. We conclude that this technique is useful for detecting sample labeling errors in cytometric analyses of longitudinal data. This technique could also be used in conjunction with another method, like GWAS or PCR, to detect errors in cross-sectional data. We suggest widespread adoption of this or similar techniques will improve the quality of clinical studies that utilize cytometry.

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RNASeq_similarity_matrix: visually identify sample mix-ups in RNASeq data using a ‘genomic’ sequence similarity matrix

Cited by 3 publications

References 7 publications

Comparative transcriptome analysis of endemic and epidemic Kaposi’s sarcoma (KS) lesions and the secondary role of HIV-1 in KS pathogenesis

Comparative transcriptome analysis of endemic and epidemic Kaposi’s sarcoma (KS) lesions and the secondary role of HIV-1 in KS pathogenesis

A simple strategy for sample annotation error detection in cytometry datasets

A simple strategy for sample annotation error detection in cytometry datasets

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