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Molecular interactions inference from single-cell multi-omics data

Home Page: https://cantinilab.github.io/HuMMuS/

License: GNU Affero General Public License v3.0

R 45.07% Python 52.89% CSS 2.04%

biological-networks epigenomics multi-omics multilayer-networks single-cell transcriptomics

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hummus's Issues

Troubleshooting Converting Seurat Object to Hummus Object with 10X-Multiome Dataset

Hello,

Thank you for creating this wonderful package. I'm excited to give it a test run with my 10X-Multiome dataset. As I started to follow along with your vignette using my own dataset, I encountered an error message when transitioning into a hummus object: "hummus <- as(pbmc, 'hummus_object')". The error is as follows: Error in validObject(object = .Object) : invalid class "Seurat" object: 'assays' must be a named list.

Here's what my "pbmc" object looks like:
An object of class Seurat
324407 features across 23619 samples within 8 assays
Active assay: RNA (36601 features, 0 variable features)
6 layers present: counts.Gene Expression.RNA_280, counts.Gene Expression.RNA_302, counts.Gene Expression.RNA_72, counts.Gene Expression.RNA_203, counts.Gene Expression.RNA_271, counts.Gene Expression.RNA_294
7 other assays present: SCT, integrated, prediction.score.celltype.l1, prediction.score.celltype.l2, prediction.score.celltype.l3, ATAC, peaks
5 dimensional reductions calculated: pca, integrated_dr, ref.umap, lsi, umap

Could you give me some suggestions on what may be worth trying from here?

Thanks,
Daniel

Error in add_network() function within Running Cicero for Hummus Object

Hi Rémi,

I hope you're doing well!

I'm mostly through your documentation, but I encountered an error today while trying to run Cicero via this script:

# Compute ATAC peak networks
hummus_case <- compute_atac_peak_network(hummus_case,
                                         atac_assay = "peaks",
                                         verbose = 1,
                                         genome = BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38,
                                         store_network = FALSE)

hummus_control <- compute_atac_peak_network(hummus_control,
                                            atac_assay = "peaks",
                                            verbose = 1,
                                            genome = BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38,
                                            store_network = FALSE)

Here's the output and error message I received:
[1] "Starting Cicero"
[1] "Calculating distance_parameter value"
[1] "Running models"
[1] "Assembling connections"
[1] "Successful cicero models: 10131"
[1] "Other models: "

Zero or one element in range
3454
[1] "Models with errors: 0"
[1] "Done"

236663 peak edges with a coaccess score > 0 were found.
Peak network construction time: 5.47283
Error in add_network(object = hummus, network = atac_peak_network, network_name = network_name, :
Object is not a multiplex, a multilayer nor an hummus object.

And, here's what my Hummus objects look like:

> hummus_case
An object of class "Hummus_Object"
<S4 Type Object>
attr(,"assays")
attr(,"assays")$RNA
Assay (v5) data with 36601 features for 15621 cells
First 10 features:
 MIR1302-2HG, FAM138A, OR4F5, AL627309.1, AL627309.3, AL627309.2, AL627309.5, AL627309.4, AP006222.2, AL732372.1 
Layers:
 counts.Gene Expression.RNA_72, counts.Gene Expression.RNA_203, counts.Gene Expression.RNA_271, counts.Gene
Expression.RNA_294 

attr(,"assays")$SCT
SCTAssay data with 27672 features for 15621 cells, and 4 SCTModel(s) 
First 10 features:
 AL627309.1, AL627309.5, AL627309.4, LINC01409, FAM87B, LINC01128, LINC00115, FAM41C, SAMD11, NOC2L 

attr(,"assays")$integrated
SCTAssay data with 3000 features for 15621 cells, and 1 SCTModel(s) 
Top 10 variable features:
 IGKC, VCAN, IGHA1, IGLC2, AL136456.1, LINC02694, IGLC3, TCF7L2, BANK1, GNLY 

attr(,"assays")$prediction.score.celltype.l1
Assay data with 8 features for 15621 cells
First 8 features:
 other T, CD8 T, B, CD4 T, DC, NK, Mono, other 

attr(,"assays")$prediction.score.celltype.l2
Assay data with 30 features for 15621 cells
First 10 features:
 gdT, CD8 TEM, CD8 TCM, dnT, B intermediate, CD4 TCM, pDC, NK, B naive, CD14 Mono 

attr(,"assays")$prediction.score.celltype.l3
Assay data with 57 features for 15621 cells
First 10 features:
 gdT-3, CD8 TEM-2, CD8 TCM-1, dnT-2, B intermediate lambda, CD4 TCM-3, B intermediate kappa, CD8 TEM-1, CD4 TCM-1, pDC 

attr(,"assays")$ATAC
ChromatinAssay data with 170277 features for 15621 cells
Variable features: 170277 
Genome: 
Annotation present: TRUE 
Motifs present: FALSE 
Fragment files: 6 

attr(,"assays")$peaks
ChromatinAssay data with 86762 features for 15621 cells
Variable features: 0 
Genome: 
Annotation present: TRUE 
Motifs present: FALSE 
Fragment files: 6 

attr(,"active.assay")
[1] "ATAC"
attr(,"multilayer")
Multilayer network containing  2  bipartite networks and  2  multiplex networks.
 
- Multiplex names:  TF, SCT 
- Bipartite names:  tf_peak, atac_rna 
attr(,"motifs_db")
Motifs database object with :
-  1503 motifs
-  914  TFs
-  1540 TF to motif names mapping
> hummus_control
An object of class "Hummus_Object"
<S4 Type Object>
attr(,"assays")
attr(,"assays")$RNA
Assay (v5) data with 36601 features for 8363 cells
First 10 features:
 MIR1302-2HG, FAM138A, OR4F5, AL627309.1, AL627309.3, AL627309.2, AL627309.5, AL627309.4, AP006222.2, AL732372.1 
Layers:
 counts.Gene Expression.RNA_280, counts.Gene Expression.RNA_302 

attr(,"assays")$SCT
SCTAssay data with 27672 features for 8363 cells, and 2 SCTModel(s) 
First 10 features:
 AL627309.1, AL627309.5, AL627309.4, LINC01409, FAM87B, LINC01128, LINC00115, FAM41C, SAMD11, NOC2L 

attr(,"assays")$integrated
SCTAssay data with 3000 features for 8363 cells, and 0 SCTModel(s) 
Top 10 variable features:
 IGKC, VCAN, IGHA1, IGLC2, AL136456.1, LINC02694, IGLC3, TCF7L2, BANK1, GNLY 

attr(,"assays")$prediction.score.celltype.l1
Assay data with 8 features for 8363 cells
First 8 features:
 other T, CD8 T, B, CD4 T, DC, NK, Mono, other 

attr(,"assays")$prediction.score.celltype.l2
Assay data with 30 features for 8363 cells
First 10 features:
 gdT, CD8 TEM, CD8 TCM, dnT, B intermediate, CD4 TCM, pDC, NK, B naive, CD14 Mono 

attr(,"assays")$prediction.score.celltype.l3
Assay data with 57 features for 8363 cells
First 10 features:
 gdT-3, CD8 TEM-2, CD8 TCM-1, dnT-2, B intermediate lambda, CD4 TCM-3, B intermediate kappa, CD8 TEM-1, CD4 TCM-1, pDC 

attr(,"assays")$ATAC
ChromatinAssay data with 170277 features for 8363 cells
Variable features: 170277 
Genome: 
Annotation present: TRUE 
Motifs present: FALSE 
Fragment files: 6 

attr(,"assays")$peaks
ChromatinAssay data with 86762 features for 8363 cells
Variable features: 0 
Genome: 
Annotation present: TRUE 
Motifs present: FALSE 
Fragment files: 6 

attr(,"active.assay")
[1] "ATAC"
attr(,"multilayer")
Multilayer network containing  2  bipartite networks and  2  multiplex networks.
 
- Multiplex names:  TF, SCT 
- Bipartite names:  tf_peak, atac_rna 
attr(,"motifs_db")
Motifs database object with :
-  1503 motifs
-  914  TFs
-  1540 TF to motif names mapping

I would really appreciate any suggestions on what may be worth trying from here! And, thanks for your help!

Best,
Daniel

no 'Initiate_Hummus_Object' function

Hi!
Don't forget to add this part haha :)
hummus_object function results in ' no method for coercing this S4 class to a vector'.

Adding PPI information

Hello, thank you for this tool! It would be very useful for me as I have scRNA-seq, scATAC-seq and proteomic data.

I would like to incorporate the PPI data into the GRN.
I saw this information in the readme "For now, such personalisation requires to use directly some hummuspy (python package) functions at the end of the pipeline and write some configuration files manually. It will be simplified soon !", however if you could please give me some more information about how to write the config files in that case it would be great!

Thank you so much
Best regards,
Maria

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