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Étude du pathobiome respiratoire chez les jeunes bovins atteints de bronchopneumonie infectieuse

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Groupe de travail Biopuces, INRA d’Auzeville
http://www.nathalievilla.org/biopuces
December 14th, 2017

Published in: Science
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Étude du pathobiome respiratoire chez les jeunes bovins atteints de bronchopneumonie infectieuse

  1. 1. Étude du pathobiome respiratoire chez les jeunes bovins atteints de bronchopneumonie infectieuse (analyses statistiques) nathalie.villa-vialaneix@inra.fr http://www.nathalievilla.org Groupe de travail Biopuces Jeudi 14 décembre 2017 Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 1/17
  2. 2. Data and objective description Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 2/17
  3. 3. Available datasets Microbiote 45 (paired) samples coming from two conditions (LBA/EN) 270 identified “species” (starting from 406 species and removing duplicates... possible problems due to unknown species) Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 3/17
  4. 4. Available datasets Microbiote 45 (paired) samples coming from two conditions (LBA/EN) 270 identified “species” (starting from 406 species and removing duplicates... possible problems due to unknown species) data already “normalized” by subsampling (possible biases due to this kind of techniques) Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 3/17
  5. 5. Available datasets Microbiote 45 (paired) samples coming from two conditions (LBA/EN) 270 identified “species” (starting from 406 species and removing duplicates... possible problems due to unknown species) data already “normalized” by subsampling (possible biases due to this kind of techniques) Virus presence 46 (paired) samples (the same than for the microbiote) coming from two conditions (LBA/EN) 9 targeted virus for which we have an information about presence/absence Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 3/17
  6. 6. Objectives 1 How are the differences between LBA and EN samples characterized from a microbiote point of view? 2 Are we able to predict the presence/absence of some viruses from their microbiotes? Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 4/17
  7. 7. Overview of the used methods 1 exploratory analyses and basic transformations for compositional data 2 mixOmics: sparse PLS-DA and sparse PLS between the two datasets on transformed data Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 5/17
  8. 8. Transforming count data Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 6/17
  9. 9. Distribution for sample 1 Log-scale distribution Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 7/17
  10. 10. Distribution for sample 1 TSS distribution: yij = nij p k=1 nik Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 7/17
  11. 11. Distribution for sample 1 CLR distribution: ˜yij = log yij n p k=1 yik Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 7/17
  12. 12. Distribution for sample 1 ILR distribution: Y = Y × V where Y is the matrix of CLR transformed data and V is such that VV = Ip1 and VV = Ip + a1, a > 0. Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 7/17
  13. 13. Distribution for sample 1 CSS distribution: extension of quantile normalization for metagenomic data Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 7/17
  14. 14. All sample distributions Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 8/17
  15. 15. PCA Log-scale Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 9/17
  16. 16. PCA CLR Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 9/17
  17. 17. Differences between LBA/EN samples Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 10/17
  18. 18. sPLS-DA Method: on log-transformed counts, CV (10-fold, 100 replicates) to choose the number of variables on each axis (resp. 6 and 3), paired analysis (multilevel) Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 11/17
  19. 19. sPLS-DA Method: on log-transformed counts, CV (10-fold, 100 replicates) to choose the number of variables on each axis (resp. 6 and 3), paired analysis (multilevel) Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 11/17
  20. 20. Predicting the presence of viruses Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 12/17
  21. 21. First global analysis in EN (sparse PLS, regression mode) Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 13/17
  22. 22. Predicting the presence of certain viruses in EN Problem: very unbalanced cases (viruses are observed only 1/3 times in the 23 samples) Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 14/17
  23. 23. Predicting the presence of certain viruses in EN Problem: very unbalanced cases (viruses are observed only 1/3 times in the 23 samples) ⇒ Definition of a group of three viruses (RSV, PI-3 and Coronavirus) to be simultaneously predicted Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 14/17
  24. 24. Predicting the presence of certain viruses in EN Problem: very unbalanced cases (viruses are observed only 1/3 times in the 23 samples) ⇒ Definition of a group of three viruses (RSV, PI-3 and Coronavirus) to be simultaneously predicted Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 14/17
  25. 25. In LBA? Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 15/17
  26. 26. Conclusions and perspectives in our case, standard transformations deteriorate discrimination between samples sparse PLS-DA seems relevant to discriminate between LBA/EN samples predicting the presence of viruses is a more complicated task kernel approaches to perform discrimination (currently under study; PCA is promising) Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 16/17
  27. 27. Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 17/17

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