TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository
Abstract Background In order to correctly decode phenotypic information from RNA-sequencing (RNA-seq) data, careful selection of the RNA-seq quantification measure is critical for inter-sample comparisons and for downstream analyses, such as differential gene expression between two or more condition...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-06-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-021-02936-w |
| _version_ | 1819085842413518848 |
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| author | Yingdong Zhao Ming-Chung Li Mariam M. Konaté Li Chen Biswajit Das Chris Karlovich P. Mickey Williams Yvonne A. Evrard James H. Doroshow Lisa M. McShane |
| author_facet | Yingdong Zhao Ming-Chung Li Mariam M. Konaté Li Chen Biswajit Das Chris Karlovich P. Mickey Williams Yvonne A. Evrard James H. Doroshow Lisa M. McShane |
| author_sort | Yingdong Zhao |
| collection | DOAJ |
| description | Abstract Background In order to correctly decode phenotypic information from RNA-sequencing (RNA-seq) data, careful selection of the RNA-seq quantification measure is critical for inter-sample comparisons and for downstream analyses, such as differential gene expression between two or more conditions. Several methods have been proposed and continue to be used. However, a consensus has not been reached regarding the best gene expression quantification method for RNA-seq data analysis. Methods In the present study, we used replicate samples from each of 20 patient-derived xenograft (PDX) models spanning 15 tumor types, for a total of 61 human tumor xenograft samples available through the NCI patient-derived model repository (PDMR). We compared the reproducibility across replicate samples based on TPM (transcripts per million), FPKM (fragments per kilobase of transcript per million fragments mapped), and normalized counts using coefficient of variation, intraclass correlation coefficient, and cluster analysis. Results Our results revealed that hierarchical clustering on normalized count data tended to group replicate samples from the same PDX model together more accurately than TPM and FPKM data. Furthermore, normalized count data were observed to have the lowest median coefficient of variation (CV), and highest intraclass correlation (ICC) values across all replicate samples from the same model and for the same gene across all PDX models compared to TPM and FPKM data. Conclusion We provided compelling evidence for a preferred quantification measure to conduct downstream analyses of PDX RNA-seq data. To our knowledge, this is the first comparative study of RNA-seq data quantification measures conducted on PDX models, which are known to be inherently more variable than cell line models. Our findings are consistent with what others have shown for human tumors and cell lines and add further support to the thesis that normalized counts are the best choice for the analysis of RNA-seq data across samples. |
| first_indexed | 2024-12-21T21:10:47Z |
| format | Article |
| id | doaj.art-c10f445a336e4544bc1017e110c062d5 |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-12-21T21:10:47Z |
| publishDate | 2021-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-c10f445a336e4544bc1017e110c062d52022-12-21T18:50:08ZengBMCJournal of Translational Medicine1479-58762021-06-0119111510.1186/s12967-021-02936-wTPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models RepositoryYingdong Zhao0Ming-Chung Li1Mariam M. Konaté2Li Chen3Biswajit Das4Chris Karlovich5P. Mickey Williams6Yvonne A. Evrard7James H. Doroshow8Lisa M. McShane9Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer InstituteBiometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer InstituteBiometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer InstituteLeidos Biomedical Research, Inc., Frederick National Laboratory for Cancer ResearchLeidos Biomedical Research, Inc., Frederick National Laboratory for Cancer ResearchLeidos Biomedical Research, Inc., Frederick National Laboratory for Cancer ResearchLeidos Biomedical Research, Inc., Frederick National Laboratory for Cancer ResearchLeidos Biomedical Research, Inc., Frederick National Laboratory for Cancer ResearchDivision of Cancer Treatment and Diagnosis, National Cancer InstituteBiometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer InstituteAbstract Background In order to correctly decode phenotypic information from RNA-sequencing (RNA-seq) data, careful selection of the RNA-seq quantification measure is critical for inter-sample comparisons and for downstream analyses, such as differential gene expression between two or more conditions. Several methods have been proposed and continue to be used. However, a consensus has not been reached regarding the best gene expression quantification method for RNA-seq data analysis. Methods In the present study, we used replicate samples from each of 20 patient-derived xenograft (PDX) models spanning 15 tumor types, for a total of 61 human tumor xenograft samples available through the NCI patient-derived model repository (PDMR). We compared the reproducibility across replicate samples based on TPM (transcripts per million), FPKM (fragments per kilobase of transcript per million fragments mapped), and normalized counts using coefficient of variation, intraclass correlation coefficient, and cluster analysis. Results Our results revealed that hierarchical clustering on normalized count data tended to group replicate samples from the same PDX model together more accurately than TPM and FPKM data. Furthermore, normalized count data were observed to have the lowest median coefficient of variation (CV), and highest intraclass correlation (ICC) values across all replicate samples from the same model and for the same gene across all PDX models compared to TPM and FPKM data. Conclusion We provided compelling evidence for a preferred quantification measure to conduct downstream analyses of PDX RNA-seq data. To our knowledge, this is the first comparative study of RNA-seq data quantification measures conducted on PDX models, which are known to be inherently more variable than cell line models. Our findings are consistent with what others have shown for human tumors and cell lines and add further support to the thesis that normalized counts are the best choice for the analysis of RNA-seq data across samples.https://doi.org/10.1186/s12967-021-02936-wRNA sequencingQuantification measuresNormalizationTPMFPKMCount |
| spellingShingle | Yingdong Zhao Ming-Chung Li Mariam M. Konaté Li Chen Biswajit Das Chris Karlovich P. Mickey Williams Yvonne A. Evrard James H. Doroshow Lisa M. McShane TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository Journal of Translational Medicine RNA sequencing Quantification measures Normalization TPM FPKM Count |
| title | TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository |
| title_full | TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository |
| title_fullStr | TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository |
| title_full_unstemmed | TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository |
| title_short | TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository |
| title_sort | tpm fpkm or normalized counts a comparative study of quantification measures for the analysis of rna seq data from the nci patient derived models repository |
| topic | RNA sequencing Quantification measures Normalization TPM FPKM Count |
| url | https://doi.org/10.1186/s12967-021-02936-w |
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