Integrating several samples, vanishing genes :)

[Dalhte]

Hi there. First, thanks for this very nice tool :) I have a small probleme (it's probably trivial but I'm very new to bioinformatic analysis): I have 4 differents 10X multiom ATAC+RNA samples : PG2, PG6 PG24 and PG13. I integrated those using seurat/signac first then I tried to run Multivelo I treated the different samples separately for the preprocessing steps : For example for PG2 :

adata_atacPG2 = sc.read_10x_mtx('/media/david/F/yard/apps/cellranger-arc-2.0.2/PG2/outs/filtered_feature_bc_matrix/', var_names='gene_symbols', cache=True, gex_only=False) adata_atacPG2 = adata_atacPG2[:,adata_atacPG2.var['feature_types'] == "Peaks"] adata_atacPG2 = mv.aggregate_peaks_10x(adata_atacPG2,'/media/david/F/yard/apps/cellranger-arc-2.0.2/PG2/outs/atac_peak_annotation.tsv', '/media/david/F/yard/apps/cellranger-arc-2.0.2/PG2/outs/analysis/feature_linkage/feature_linkage.bedpe',verbose=True) mv.tfidf_norm(adata_atacPG2)

I renamed the cells with unique barcodes:

barcodes = adata_atacPG2.obs.index barcodesnew = ['PG2_' + bc[0:len(bc)-2] for bc in barcodes] adata_atacPG2.obs.index = barcodesnew

Having done that on the four samples, I generated a single object by concatenation :

adata_atacPG = adata_atacPG2.concatenate([adata_atacPG6, adata_atacPG24, adata_atacPG13])

Then I processed the RNA and so one. Everything seems to work very nicely BUT I lose some of the genes (and some important ones that is). After investigation, I realized that these genes were lost during the concatenation step, probably because these are specifically present in some of the adata_atacPGXX objects but not in every ones.

How may I tackle this problem ?

Best David

[Dalhte]

Hello again I solved part of my problem modifying the concat parameters to :

adata_atacPG = adata_atacPG2.concatenate([adata_atacPG6, adata_atacPG24, adata_atacPG13], join='outer') And retrieved 3000 more genes. But, I'm quite greedy and I would love to have some more (There are some interesting ones still missing). I realized those last few genes are absent from the atac_peak_annotation.tsv files. It's quite surprising as signac LinkPeaks function manage to find linkeage between these gene and ATAC open regions. So my question: Do you know if it might be possible to extract the -atac_peak_annotation.tsv and prehaps also the feature_linkage.bedpe files from Seurat/signac analyis ? Best David

[Dalhte]

me again, realizing my last question was discussed in previous #29 and #1 post !

[Dalhte]

Hi there I still have some problems I cannot solve this time : In my RNA anndata object (adatam1), I have 13873 genes In my ATAC anndata object ( adata_atacPGc), I also have 13873 genes, I checked that they are exactly the same I just runned the mv.recover_dynamics_chrom on these two objects :

adata_result1 = mv.recover_dynamics_chrom(adatam1, adata_atacPGc,max_iter=5, init_mode="invert",verbose=False,parallel=True,save_plot=False,rna_only=False,fit=True,n_anchors=500,extra_color_key='celltype') I got : 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 13873/13873 [19:44:34<00:00, 5.12s/it] With the 13873 genes But : adata_result1 AnnData object with n_obs × n_vars = 28298 × 8630 I'm loosing more than 5000 genes, some of them with RNA velocity in the RNA objects, and some of them really relevant for what I'm analyzing. How can I recover these genes ? Thanks for your help Best David

[danielee0707]

Hi David, We recommend first using scVelo's preprocessing steps to prepare the input data. The "filter_and_normalize" function is able to select genes with minimum counts in the unspliced and spliced matrices. Multivelo also does some filtering steps internally to discard low-quality genes and make sure the velocity inference algorithm will run and converge successfully. These steps are crucial to ensure that low-quality genes won't obstruct downstream analyses such as lineage prediction. Unfortunately, there's no way to turn these steps off currently.