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

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. Data and objective description
Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 2/17

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. 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. 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. 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. 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. Transforming count data
Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 6/17

9. Distribution for sample 1
Log-scale distribution
Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 7/17

10. Distribution for sample 1
TSS distribution: yij =
nij
p
k=1
nik
Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 7/17

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. 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. Distribution for sample 1
CSS distribution: extension of quantile normalization for metagenomic
data
Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 7/17

14. All sample distributions
Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 8/17

15. PCA
Log-scale
Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 9/17

16. PCA
CLR
Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 9/17

17. Differences between LBA/EN samples
Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 10/17

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. 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. Predicting the presence of viruses
Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 12/17

21. First global analysis in EN (sparse PLS, regression
mode)
Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 13/17

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. 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. 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. In LBA?
Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 15/17

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. Nathalie Villa-Vialaneix | Étude du pathobiome respiratoire 17/17