--- a/README.md
+++ b/README.md
@@ -1,135 +1,134 @@
-<img src="docs/source/inmoose.png" width="600">
-
-[](https://pypi.org/project/inmoose)
-[](https://pepy.tech/project/inmoose)
-[](https://coveralls.io/github/epigenelabs/inmoose)
-[](https://inmoose.readthedocs.io/en/latest/?badge=latest)
-[](LICENSE)
-
-# InMoose
-
-InMoose is the **In**tegrated **M**ulti **O**mic **O**pen **S**ource **E**nvironment.
-It is a collection of tools for the analysis of omic data.
-
-InMoose is developed and maintained by <img src="docs/source/epigenelogo.png" width="20"> [Epigene Labs](https://www.epigenelabs.com/).
-
-# Installation
-
-You can install InMoose directly with:
-
-```
-pip install inmoose
-```
-
-# Documentation
-
-Documentation is hosted on [readthedocs.org](https://inmoose.readthedocs.io/en/latest/).
-
-# Citing
-
-Depending on the features you use, you may cite one of the following papers:
-- Behdenna A, Colange M, Haziza J, Gema A, Appé G, Azencot CA and Nordor A. (2023) pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. BMC Bioinformatics 7;24(1):459. https://doi.org/10.1186/s12859-023-05578-5.
-- Colange M, Appé G, Meunier L, Weill S, Nordor A, Behdenna A. (2024)
- Differential Expression Analysis with InMoose, the Integrated Multi-Omic Open-Source Environment in Python. BioRxiv. https://doi.org/XXX
-
-# Batch Effect Correction
-
-InMoose provides features to correct technical biases, also called batch
-effects, in transcriptomic data:
-- for microarray data, InMoose supersedes
- [pyCombat](https://github.com/epigenelabs/pycombat/), a Python3 implementation
- of [ComBat](https://doi.org/10.1093/biostatistics/kxj037), one of the most
- widely used tool for batch effect correction on microarray data.
-- for RNASeq data, InMoose features a port to Python3 of
- [ComBat-Seq](https://doi.org/10.1093/nargab/lqaa078), one of the most widely
- used tool for batch effect correction on RNASeq data.
-
-To use these functions, simply import them and call them with default
-parameters:
-```python
-from inmoose.pycombat import pycombat_norm, pycombat_seq
-
-microarray_corrected = pycombat_norm(microarray_data, microarray_batches)
-rnaseq_corrected = pycombat_seq(rnaseq_data, rnaseq_batches)
-```
-
-* `microarray_data`, `rnaseq_data`: the expression matrices, containing the
- information about the gene expression (rows) for each sample (columns).
-* `microarray_batches`, `rnaseq_batches`: list of batch indices, describing the
- batch for each sample. The list of batches should contain as many elements as
- the number of samples in the expression matrix.
-
-
-# Cohort QC
-InMoose provides classes `CohortMetric` and `QCReport` to help to perform quality control (QC) on cohort datasets after batch effect correction.
-
-`CohortMetric`: This class handles the analysis and provides methods for performing quality control on cohort datasets.
-
-**Description**
-The `CohortMetric` class performs a range of quality control analyses, including:
-- Principal Component Analysis (PCA) to assess data variation.
-- Comparison of sample distributions across different datasets or batches.
-- Quantification of the effect of batch correction.
-- Silhouette Score calculation to assess how well batches are separated.
-- Entropy calculation to evaluate the mixing of samples from different batches.
-
-**Usage Example**
-```python
-from inmoose.cohort_qc.cohort_metric import CohortMetric
-
-cohort_quality_control = CohortMetric(
- clinical_df=clinical_data,
- batch_column="batch",
- data_expression_df=gene_expression_after_correction,
- data_expression_df_before=gene_expression_before_correction,
- covariates=["biopsy_site", "sample_type"]
-)
-```
-
-`QCReport`: This class takes a CohortMetric argument, and generates an HTML report summarizing the QC results.
-
-**Description**
-The `QCReport` class extends `CohortMetric` and generates a comprehensive HTML report based on the quality control analysis. It includes visualizations and summaries of PCA, batch correction, Silhouette Scores, entropy, and more.
-
-**Usage Example**
-```python
-from inmoose.cohort_qc.qc_report import QCReport
-
-# Generate and save the QC report
-qc_report = QCReport(cohort_quality_control)
-qc_report.save_html_report_local(output_path='reports')
-```
-
-# Differential Expression Analysis
-
-InMoose provides features to analyse diffentially expressed genes in bulk
-transcriptomic data:
-- for microarray data, InMoose features a port of
- [limma](https://doi.org/10.1093/nar/gkv007), the *de facto* standard tool
- for differential expression analysis on microarray data.
-- for RNASeq data, InMoose features a ports to Python3 of
- [edgeR](https://doi.org/10.12688/f1000research.8987.2) and
- [DESeq2](https://doi.org/10.1186/s13059-014-0550-8), two of the most widely
- used tools for differential expression analysis on RNASeq data.
-
-See the dedicated sections of the
-[documentation](https://inmoose.readthedocs.io/en/latest/).
-
-# Consensus clustering
-InMoose provides features to compute consensus clustering, a resampling based algorithm compatible with any clustering algorithms which class implementation is instantiated with parameter `n_clusters`, and possess a `fit_predict` method, which is invoked on data.
-Consensus clustering helps determining the best number of clusters to use and output confidence metrics and plots.
-
-
-To use these functions, import the consensusClustering class and a clustering algorithm class:
-```python
-from inmoose.consensus_clustering.consensus_clustering import consensusClustering
-from sklearn.cluster import AgglomerativeClustering
-
-CC = consensusClustering(AgglomerativeClustering)
-CC.compute_consensus_clustering(numpy_ndarray)
-```
-
-# How to contribute
-
-Please refer to [CONTRIBUTING.md](https://github.com/epigenelabs/inmoose/blob/master/CONTRIBUTING.md) to learn more about the contribution guidelines.
-
+
+[](https://pypi.org/project/inmoose)
+[](https://pepy.tech/project/inmoose)
+[](https://coveralls.io/github/epigenelabs/inmoose)
+[](https://inmoose.readthedocs.io/en/latest/?badge=latest)
+[](LICENSE)
+
+# InMoose
+
+InMoose is the **In**tegrated **M**ulti **O**mic **O**pen **S**ource **E**nvironment.
+It is a collection of tools for the analysis of omic data.
+
+InMoose is developed and maintained by <img src="docs/source/epigenelogo.png" width="20"> [Epigene Labs](https://www.epigenelabs.com/).
+
+# Installation
+
+You can install InMoose directly with:
+
+```
+pip install inmoose
+```
+
+# Documentation
+
+Documentation is hosted on [readthedocs.org](https://inmoose.readthedocs.io/en/latest/).
+
+# Citing
+
+Depending on the features you use, you may cite one of the following papers:
+- Behdenna A, Colange M, Haziza J, Gema A, Appé G, Azencot CA and Nordor A. (2023) pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. BMC Bioinformatics 7;24(1):459. https://doi.org/10.1186/s12859-023-05578-5.
+- Colange M, Appé G, Meunier L, Weill S, Nordor A, Behdenna A. (2024)
+ Differential Expression Analysis with InMoose, the Integrated Multi-Omic Open-Source Environment in Python. BioRxiv. https://doi.org/XXX
+
+# Batch Effect Correction
+
+InMoose provides features to correct technical biases, also called batch
+effects, in transcriptomic data:
+- for microarray data, InMoose supersedes
+ [pyCombat](https://github.com/epigenelabs/pycombat/), a Python3 implementation
+ of [ComBat](https://doi.org/10.1093/biostatistics/kxj037), one of the most
+ widely used tool for batch effect correction on microarray data.
+- for RNASeq data, InMoose features a port to Python3 of
+ [ComBat-Seq](https://doi.org/10.1093/nargab/lqaa078), one of the most widely
+ used tool for batch effect correction on RNASeq data.
+
+To use these functions, simply import them and call them with default
+parameters:
+```python
+from inmoose.pycombat import pycombat_norm, pycombat_seq
+
+microarray_corrected = pycombat_norm(microarray_data, microarray_batches)
+rnaseq_corrected = pycombat_seq(rnaseq_data, rnaseq_batches)
+```
+
+* `microarray_data`, `rnaseq_data`: the expression matrices, containing the
+ information about the gene expression (rows) for each sample (columns).
+* `microarray_batches`, `rnaseq_batches`: list of batch indices, describing the
+ batch for each sample. The list of batches should contain as many elements as
+ the number of samples in the expression matrix.
+
+
+# Cohort QC
+InMoose provides classes `CohortMetric` and `QCReport` to help to perform quality control (QC) on cohort datasets after batch effect correction.
+
+`CohortMetric`: This class handles the analysis and provides methods for performing quality control on cohort datasets.
+
+**Description**
+The `CohortMetric` class performs a range of quality control analyses, including:
+- Principal Component Analysis (PCA) to assess data variation.
+- Comparison of sample distributions across different datasets or batches.
+- Quantification of the effect of batch correction.
+- Silhouette Score calculation to assess how well batches are separated.
+- Entropy calculation to evaluate the mixing of samples from different batches.
+
+**Usage Example**
+```python
+from inmoose.cohort_qc.cohort_metric import CohortMetric
+
+cohort_quality_control = CohortMetric(
+ clinical_df=clinical_data,
+ batch_column="batch",
+ data_expression_df=gene_expression_after_correction,
+ data_expression_df_before=gene_expression_before_correction,
+ covariates=["biopsy_site", "sample_type"]
+)
+```
+
+`QCReport`: This class takes a CohortMetric argument, and generates an HTML report summarizing the QC results.
+
+**Description**
+The `QCReport` class extends `CohortMetric` and generates a comprehensive HTML report based on the quality control analysis. It includes visualizations and summaries of PCA, batch correction, Silhouette Scores, entropy, and more.
+
+**Usage Example**
+```python
+from inmoose.cohort_qc.qc_report import QCReport
+
+# Generate and save the QC report
+qc_report = QCReport(cohort_quality_control)
+qc_report.save_html_report_local(output_path='reports')
+```
+
+# Differential Expression Analysis
+
+InMoose provides features to analyse diffentially expressed genes in bulk
+transcriptomic data:
+- for microarray data, InMoose features a port of
+ [limma](https://doi.org/10.1093/nar/gkv007), the *de facto* standard tool
+ for differential expression analysis on microarray data.
+- for RNASeq data, InMoose features a ports to Python3 of
+ [edgeR](https://doi.org/10.12688/f1000research.8987.2) and
+ [DESeq2](https://doi.org/10.1186/s13059-014-0550-8), two of the most widely
+ used tools for differential expression analysis on RNASeq data.
+
+See the dedicated sections of the
+[documentation](https://inmoose.readthedocs.io/en/latest/).
+
+# Consensus clustering
+InMoose provides features to compute consensus clustering, a resampling based algorithm compatible with any clustering algorithms which class implementation is instantiated with parameter `n_clusters`, and possess a `fit_predict` method, which is invoked on data.
+Consensus clustering helps determining the best number of clusters to use and output confidence metrics and plots.
+
+
+To use these functions, import the consensusClustering class and a clustering algorithm class:
+```python
+from inmoose.consensus_clustering.consensus_clustering import consensusClustering
+from sklearn.cluster import AgglomerativeClustering
+
+CC = consensusClustering(AgglomerativeClustering)
+CC.compute_consensus_clustering(numpy_ndarray)
+```
+
+# How to contribute
+
+Please refer to [CONTRIBUTING.md](https://github.com/epigenelabs/inmoose/blob/master/CONTRIBUTING.md) to learn more about the contribution guidelines.
+
Datasets
Models
