# deconvPDAC: a quantifier of PDAC tumor cellular heterogeneity

Yasmina Kermezli, Lucie Lamothe, Jerome Cros, Magali Richard, Yuna Blum

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Yasmina Kermezli, Lucie Lamothe, Jerome Cros, Magali Richard, Yuna Blum. deconvPDAC: a quantifier of PDAC tumor cellular heterogeneity. Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM 2024), Jun 2024, Toulouse, France. . [hal-04669313](https://hal.science/hal-04669313v1)

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https://hal.science/hal-04669313v1

Submitted on 8 Aug 2024

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Distributed under a Creative Commons Attribution 4.0 International License
# deconvPDAC : a quantifier of PDAC tumor cellular heterogeneity

Yasmina Kermezli¹, Lucie Lamothe¹,+ , Jerome Cros², Magali Richard¹,* and Yuna Blum³,*

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¹ Laboratory TIMC-IMAG, UMR5525, Univ.Grenoble-Alpes, CNRS,38000, Grenoble, France;
² Beaujon Hospital, Dpt of Pathology, Univ. Paris-INSERM U1149, Paris, France;
³ IGDR, UMR 6290, CNRS, Université de Rennes 1, Rennes, France
*corr: magali.richard@univ-grenoble-alpes.fr & yuna.blum@univ.rennes1.fr; + presenting author

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## deconvPDAC Pipeline

In this study, we characterized a pool of **796,000 single cell RNA-Seq**
from normal and tumoral pancreas from **12 different published studies**.

<figure><img src="image_6.png" /><figcaption>**Figure 1: Pipeline of the project.** Steps 1 to 3 lead to identification of robust integrative markers for PDAC cell types. Step 4 and 5 are potential applications of this new marker set.

**2024 update**: the project is currently being remade using extended datasets. Following figures were realized in 2021 using only 4 studies</figcaption></figure>

### Step 1 & 2: Cell identification & integration

Single cell type assignment based on a dedicated **literature gene markers curation** and a subsequent analysis of a recent PDAC scRNA-seq data-sets. Identification of **15 independent cell types**.

<figure><img src="image_7.png" /><figcaption>**Figure 2: Cell-types identification and integration.** Top panel: decision tree for cell type assignation. Bottom panel: Clustering of single cells data after integration of all studies together. Left panel: UMAP of all identified cell-types, Right panel: UMAP of cells colored by their study of origin.</figcaption></figure>

### Step 3: Integrative cell-type specific markers

Extraction of **robust integrative gene markers**.

<figure><img src="image_8.png" /><figcaption>**Figure 3: Robust integrative cell-type markers.** Heatmap of the newly generated cell-types markers across averaged genetic profiles of integrated cell types. Markers were identified by applying the SCRAN workflow (Lun et al, 2016) after bootstrap sampling.</figcaption></figure>

### Step 4: Single cell identity assignment

<figure><img src="image_9.png" /><figcaption>**Figure 4: Posterior cell assignment on Peng's dataset.** a) Misclassification rate using our robust list of markers or the markers from the literature and the tools scSorter (Guo et al, 2021) or cellID (Cortal et al 2021). b) Representation of the contingency table showing the proportion of true and false discoveries for each cell population using cellID and our robust markers.</figcaption></figure>

### Step 5: Benchmark of deconvolution using the new single-cell references

<figure><img src="image_10.png" /><figcaption>**Figure 5: Benchmark of tumor composition estimation.** Root mean square error and pearson correlation of deconvolution methods for 5 independent simulations on (A) pseudo-bulk healthy pancreas (baron dataset) and (B) on in silico simulation of cancer pancreas (deconbench dataset). Correlations between real cell-types proportion and BisqueRNA estimated cell-type proportion for (C) pseudo-bulk healthy pancreas and for (D) in silico simulation of cancer pancreas.</figcaption></figure>

## Conclusion

- An **integrative robust set of PDAC cell-type** specific gene markers.
- The use of these signatures can help us understanding and finely quantifying tumor composition and pave the way to more efficient precision medicine.