Da abundancia á evidencia: fluxo de traballo para a análise diferencial de datos ómicos en R

.title[
# Da abundancia á evidencia: fluxo de traballo para a análise diferencial de datos ómicos en R
]
.subtitle[
## Xornadas de R
]
.author[
### Julia García Currás
]
.institute[
### Biostatech | CITIC-UDC
]
.date[
### 16/10/2025
]

---

</style>

# Introdución

---

# As ómicas

Nova disciplina en bioloxía que estuda, de maneira global e masiva, diferentes moléculas dun organismo.

- Xéranse datos biolóxicos de grande dimensionalidade que requiren dunha **análise estatística**: **R software**.

- Tipos de moléculas e as súas correspondentes ómicas:

---

# R e as ómicas

Vantaxes do uso de R na análise de datos ómicos:

* Software libre e gratuíto

* Ampla e activa comunidade de usuarios:

* Desenvolvemento, mantemento e mellora constante dos diferentes paquetes de análise

* Estandarización e reproducibilidade de análises

* Código aberto e compartido:

* Transparencia
    
    * Flexibilidade

---

# Análise de abundancia diferencial

---

# Deseño experimental común en análise de abundancia diferencial

![](data:image/png;base64,#Figures/DesenhoExperimental.svg)

---

# Fluxo xeral na análise de abundancia diferencial

![](data:image/png;base64,#Figures/Pipeline.svg)

---

# _Exemplo_

.footnote[
Amorim, M.; Martins, B.; Caramelo, F.; Gonçalves, C.; Trindade, G.; Simão, J.; Barreto, P.; Marques, I.; Leal, E. C.; Carvalho, E.; Reis, F.; Ribeiro-Rodrigues, T.; Girão, H.; Rodrigues-Santos, P.; Farinha, C.; Ambrósio, A. F.; Silva, R.; Fernandes, R. Putative Biomarkers in Tears for Diabetic Retinopathy Diagnosis. Front Med (Lausanne) 2022, 9, 873483. https://doi.org/10.3389/fmed.2022.873483.
]

---

# Preprocesado

2. **Análise de datos faltantes**:

* **Filtrado**: eliminación de moléculas con moitos valores faltantes. Recomendación: **filtrar por grupos**. 
  * **Imputación**: substitución de valores faltantes por valores estimados.

3. **Normalización** para reducir o erro sistemático presente nos datos.

]

]

---

# _Preprocesado (exemplo)_

.pull-left[
**Datos faltantes**: 
* Total: 67.9 %
* Grupo NC: 27.35% | Grupo DM: 23.28%
* **Filtro**: máximo 50% datos faltantes por grupo.

> 291 proteínas de 1407 proteínas totais

]

**Normalizacións:**

]

---

# Comparacións entre grupos

1. **Comparación** da cantidade de cada molécula entre grupos.

2. **Corrección do p-valor** por comparacións múltiples:
    
3. Cálculo do `$log_2$` _fold change_ ou `$logFC$`:

`$$log_2FC = log_2{(\frac{mean_{Case}(molecule_1)}{mean_{Control}(molecule_1)})}$$`

4. Representación: gráfico de tipo volcán ou **volcano plot**.

]

]

---

# _Comparacións entre grupos (exemplo)_

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---

# Análise de agrupación

As moléculas con abundancia diferencial permiten discriminar entre os grupos de estudo no seu conxunto?

* **Análise de compoñentes principais (PCA)**

]

* **Clustering xerárquico**

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]

---

# Análise funcional

Permite coñecer aquelas funcións asociadas a moléculas con abundancia diferencial. Dise que estás funcións están sobrerrepresentadas nos resultados.

* Base de datos de termos funcionais: relación molécula - función.

* Lista de moléculas: significativas e non significativas.

* Constraste: test `$\chi^2$` de Pearson ou test hiperxeométrico.

* Paquetes: `clusterProfiler::enrichGO`, `AnnotationDbi`, `enrichplot`.

---

# _Análise funcional (exemplo)_

Táboa de resultados:

<div style="height:70%; margin:auto;">
<div class="datatables html-widget html-fill-item" id="htmlwidget-ca6cdefb1e8c3796f7ae" style="width:100%;height:auto;"></div>
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</div>

---

# _Análise funcional (exemplo)_

.pull-left[
Resultados enriquecimiento (`enrichplot::dotplot`):
<img src="data:image/png;base64,#Presentacion_files/figure-html/unnamed-chunk-25-1.svg" style="display: block; margin: auto;" />
]

--
.pull-right[
Rede proteínas-funcións (`enrichplot::cnetplot`):

<img src="data:image/png;base64,#Presentacion_files/figure-html/unnamed-chunk-26-1.svg" style="display: block; margin: auto;" />
]

---

# Conclusións

---

# Conclusións

* Con este fluxo de traballo pódese obter a partir de datos ómicos de abundancia:

* **Biomarcadores** candidatos
    
    * **Funcións biolóxicas** chave

* Fluxo de traballo **estensible e personalizable**

* O uso de R neste tipo de análises é moi importante para asegurar a **reproducibilidade, transparencia e transferencia** dos coñecementos.

---
background-image: url('Figures/Fondo.svg')
background-size: 95%
background-position: center
background-repeat: no-repeat
class: center, middle, inverse

# Moitas grazas!

Julia García Curras