This data is from cellxgene ,DNA Methylation Atlas of the Mouse Brain at Single-Cell Resolution
The steps what I have followed is this
h5ad_file <- "h5ad_file/5327c540-58b7-4dd1-8af1-d112ed939b4b.h5ad"
adata <- sc$read_h5ad(h5ad_file)
head(adata)
adata
AnnData object with n_obs × n_vars = 103982 × 39042
obs: 'AllcPath', 'CCC_Rate', 'CG_Rate', 'CG_RateAdj', 'CH_Rate', 'CH_RateAdj', 'FinalReads', 'InputReads', 'MappedReads', 'Region', 'index_name', 'uid', 'BamFilteringRate', 'MappingRate', 'Pos96', 'Plate', 'Col96', 'Row96', 'Col384', 'Row384', 'FACS_Date', 'Slice', 'BICCN_class_label', 'BICCN_subclass_label', 'BICCN_cluster_label', 'L1CellClass', 'class_umap_1', 'Order', 'RegionName', 'MajorRegion', 'SubRegion', 'DetailRegion', 'PotentialOverlap (MMB)', 'Anterior (CCF coords)', 'Posterior (CCF coords)', 'SubRegionColor', 'Replicate', 'BICCN_ontology_term_id', 'disease_ontology_term_id', 'assay_ontology_term_id', 'cell_type_ontology_term_id', 'tissue_ontology_term_id', 'development_stage_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'sex_ontology_term_id', 'is_primary_data', 'organism_ontology_term_id', 'donor_id', 'suspension_type', 'tissue_type', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid'
var: 'Unnamed: 0', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type'
uns: 'MajorRegion_colors', 'Region_colors', 'citation', 'schema_reference', 'schema_version', 'title'
obsm: 'X_tsne', 'X_umap'
Now when I try to see the data layers or structure in this
I get this
head(adata$obs)
AllcPath
10E_M_0 /gale/raidix/rdx-4/mapping/10E/CEMBA190625-10E-1-CEMBA190625-10E-2-A1/allc_CEMBA190625-10E-1-CEMBA190625-10E-2-A1_ad001.tsv.gz
10E_M_1 /gale/raidix/rdx-4/mapping/10E/CEMBA190625-10E-1-CEMBA190625-10E-2-A1/allc_CEMBA190625-10E-1-CEMBA190625-10E-2-A1_ad002.tsv.gz
10E_M_10 /gale/raidix/rdx-4/mapping/10E/CEMBA190625-10E-1-CEMBA190625-10E-2-A10/allc_CEMBA190625-10E-1-CEMBA190625-10E-2-A10_ad004.tsv.gz
10E_M_101 /gale/raidix/rdx-4/mapping/10E/CEMBA190625-10E-1-CEMBA190625-10E-2-B10/allc_CEMBA190625-10E-1-CEMBA190625-10E-2-B10_ad002.tsv.gz
10E_M_102 /gale/raidix/rdx-4/mapping/10E/CEMBA190625-10E-1-CEMBA190625-10E-2-B10/allc_CEMBA190625-10E-1-CEMBA190625-10E-2-B10_ad004.tsv.gz
10E_M_103 /gale/raidix/rdx-4/mapping/10E/CEMBA190625-10E-1-CEMBA190625-10E-2-B10/allc_CEMBA190625-10E-1-CEMBA190625-10E-2-B10_ad006.tsv.gz
CCC_Rate CG_Rate CG_RateAdj CH_Rate CH_RateAdj FinalReads
10E_M_0 0.008198210 0.8226325 0.8211664 0.04163979 0.03371801 1626504
10E_M_1 0.006018933 0.7430346 0.7414785 0.02412729 0.01821801 2009998
10E_M_10 0.006569452 0.7501719 0.7485198 0.02766457 0.02123461 1383636
10E_M_101 0.006352796 0.7608976 0.7593689 0.02654676 0.02032307 2474670
10E_M_102 0.005408991 0.7529803 0.7516369 0.01949651 0.01416413 2430290
10E_M_103 0.005817363 0.7346639 0.7331113 0.02153866 0.01581329 2949180
InputReads MappedReads Region index_name
10E_M_0 4407752 2892347 10E ad001
10E_M_1 5524084 3657352 10E ad002
10E_M_10 3455260 2172987 10E ad004
10E_M_101 7245482 4778768 10E ad002
10E_M_102 7004754 4609570 10E ad004
10E_M_103 8645474 5564327 10E ad006
uid BamFilteringRate MappingRate
10E_M_0 CEMBA190625-10E-1-CEMBA190625-10E-2-A1 0.5623475 0.6561955
10E_M_1 CEMBA190625-10E-1-CEMBA190625-10E-2-A1 0.5495774 0.6620739
10E_M_10 CEMBA190625-10E-1-CEMBA190625-10E-2-A10 0.6367438 0.6288925
10E_M_101 CEMBA190625-10E-1-CEMBA190625-10E-2-B10 0.5178469 0.6595514
10E_M_102 CEMBA190625-10E-1-CEMBA190625-10E-2-B10 0.5272271 0.6580631
10E_M_103 CEMBA190625-10E-1-CEMBA190625-10E-2-B10 0.5300156 0.6436116
Pos96 Plate Col96 Row96 Col384 Row384 FACS_Date Slice
10E_M_0 A1 CEMBA190625-10E-1 0 0 0 0 190625 10
10E_M_1 A1 CEMBA190625-10E-1 0 0 0 1 190625 10
10E_M_10 A10 CEMBA190625-10E-1 9 0 19 0 190625 10
10E_M_101 B10 CEMBA190625-10E-1 9 1 18 3 190625 10
10E_M_102 B10 CEMBA190625-10E-1 9 1 19 2 190625 10
10E_M_103 B10 CEMBA190625-10E-1 9 1 19 3 190625 10
BICCN_class_label BICCN_subclass_label BICCN_cluster_label
10E_M_0 Inh MGE-Sst MGE-Sst Rxra
10E_M_1 Exc CA3 CA3 Cadm2
10E_M_10 Exc CA3 CA3 Cadm2
10E_M_101 Exc CA3 CA3 Cadm2
10E_M_102 Exc CA1 CA1 Chrm3
10E_M_103 Exc CA1 CA1 Chrm3
L1CellClass class_umap_1 Order RegionName MajorRegion SubRegion
10E_M_0 Inh 8.687794 41 CA-3 HPF CA1-3
10E_M_1 Exc-HPF 14.093295 41 CA-3 HPF CA1-3
10E_M_10 Exc-HPF 13.630747 41 CA-3 HPF CA1-3
10E_M_101 Exc-HPF 12.042387 41 CA-3 HPF CA1-3
10E_M_102 Exc-HPF 6.567603 41 CA-3 HPF CA1-3
10E_M_103 Exc-HPF 5.560691 41 CA-3 HPF CA1-3
DetailRegion PotentialOverlap (MMB) Anterior (CCF coords)
10E_M_0 CA1, CA2, CA3, SUB, ProS PA, HATA 7500
10E_M_1 CA1, CA2, CA3, SUB, ProS PA, HATA 7500
10E_M_10 CA1, CA2, CA3, SUB, ProS PA, HATA 7500
10E_M_101 CA1, CA2, CA3, SUB, ProS PA, HATA 7500
10E_M_102 CA1, CA2, CA3, SUB, ProS PA, HATA 7500
10E_M_103 CA1, CA2, CA3, SUB, ProS PA, HATA 7500
Posterior (CCF coords) SubRegionColor Replicate
10E_M_0 8100 #d62728 10E-190625
10E_M_1 8100 #d62728 10E-190625
10E_M_10 8100 #d62728 10E-190625
10E_M_101 8100 #d62728 10E-190625
10E_M_102 8100 #d62728 10E-190625
10E_M_103 8100 #d62728 10E-190625
BICCN_ontology_term_id disease_ontology_term_id
10E_M_0 ILX:0770152 PATO:0000461
10E_M_1 ILX:0770097 PATO:0000461
10E_M_10 ILX:0770097 PATO:0000461
10E_M_101 ILX:0770097 PATO:0000461
10E_M_102 ILX:0770097 PATO:0000461
10E_M_103 ILX:0770097 PATO:0000461
assay_ontology_term_id cell_type_ontology_term_id
10E_M_0 EFO:0030027 CL:0000617
10E_M_1 EFO:0030027 CL:0000679
10E_M_10 EFO:0030027 CL:0000679
10E_M_101 EFO:0030027 CL:0000679
10E_M_102 EFO:0030027 CL:0000679
10E_M_103 EFO:0030027 CL:0000679
tissue_ontology_term_id development_stage_ontology_term_id
10E_M_0 UBERON:0003876 MmusDv:0000154
10E_M_1 UBERON:0003876 MmusDv:0000154
10E_M_10 UBERON:0003876 MmusDv:0000154
10E_M_101 UBERON:0003876 MmusDv:0000154
10E_M_102 UBERON:0003876 MmusDv:0000154
10E_M_103 UBERON:0003876 MmusDv:0000154
self_reported_ethnicity_ontology_term_id sex_ontology_term_id
10E_M_0 na PATO:0000384
10E_M_1 na PATO:0000384
10E_M_10 na PATO:0000384
10E_M_101 na PATO:0000384
10E_M_102 na PATO:0000384
10E_M_103 na PATO:0000384
is_primary_data organism_ontology_term_id donor_id suspension_type
10E_M_0 TRUE NCBITaxon:10090 pooled nucleus
10E_M_1 TRUE NCBITaxon:10090 pooled nucleus
10E_M_10 TRUE NCBITaxon:10090 pooled nucleus
10E_M_101 TRUE NCBITaxon:10090 pooled nucleus
10E_M_102 TRUE NCBITaxon:10090 pooled nucleus
10E_M_103 TRUE NCBITaxon:10090 pooled nucleus
tissue_type cell_type assay disease organism sex
10E_M_0 tissue GABAergic neuron snmC-Seq2 normal Mus musculus male
10E_M_1 tissue glutamatergic neuron snmC-Seq2 normal Mus musculus male
10E_M_10 tissue glutamatergic neuron snmC-Seq2 normal Mus musculus male
10E_M_101 tissue glutamatergic neuron snmC-Seq2 normal Mus musculus male
10E_M_102 tissue glutamatergic neuron snmC-Seq2 normal Mus musculus male
10E_M_103 tissue glutamatergic neuron snmC-Seq2 normal Mus musculus male
tissue self_reported_ethnicity development_stage
10E_M_0 hippocampal field na 8-week-old stage
10E_M_1 hippocampal field na 8-week-old stage
10E_M_10 hippocampal field na 8-week-old stage
10E_M_101 hippocampal field na 8-week-old stage
10E_M_102 hippocampal field na 8-week-old stage
10E_M_103 hippocampal field na 8-week-old stage
observation_joinid
10E_M_0 501c0ti%K@
10E_M_1 zuj|4iS7FH
10E_M_10 @v_7`Vi);H
10E_M_101 OH(jj&{LD0
10E_M_102 t5S{HgAGlE
10E_M_103 k^C~Cc+*Hg
So based on the above data frame, i can see its computed as well as annotated. Now since this is a
DNA Methylation data, I would like to know how can i use this object visualize or compare cluster in Seurat?
Any resources in this context would be really helpful.
There are two issues here: (1) understanding the data and (2) understanding the data format.
As regards (1): this is heavily processed DNA methylation data. If you look at the .var you will almost certainly see that the features are genes and reflect a summary of the methylation state of each gene (typically an average over the promoter and gene body), possibly split by mC and hmC if the technology makes a difference. You can tell just by the shape of the data you do not have granular CpG information (n_obs × n_vars = 103982 × 39042).
As regards (2): To visualize this specifically in Seurat, you have to port the data from an AnnData python object to an R Seurat object. There are tools for doing this kind of thing, but I have had mixed success. I tend to do something like the following:
obs_df = adata.obs.copy()
obs_df['UMAP1'] = adata.obsm['X_umap'][:,0]
obs_df['UMAP2'] = adata.obsm['X_umap'][:,1]
var_df = adata.var.copy()
obs_df.to_csv('barcodes.csv')
var_df.to_csv('features.csv')
sp.io.mmwrite('values.mtx', adata.X.T)
obj <- CreateSeuratObject(
Matrix::readMM('values.mtx'),
meta.data=read.csv('barcodes.csv', row.names=1)
obj@meta.features <- read.csv('features.csv', row.names=1)
and do NOT follow the standard Seurat vignettes. You are starting with non-count values (gene averages), so things like cell normalization should not be performed.
Thank you for the insight, now coming to the seurat steps which you have given the code, so I have doubt regarding these steps to begin with
obs_df.to_csv('barcodes.csv')
var_df.to_csv('features.csv')
sp.io.mmwrite('values.mtx', adata.X.T)
Now as of now what I have to start is .h5ad file so where do I get these input since my only input is .h5ad in this case.
It should work fine. The default is a wilcox test, so you'll be testing whether the ranks of average methylation tend to be higher in one population than another. Just make sure in the documentation that the features are average per-cell methylation values along the feature/gene.
I did try one of the single cell methylation data where I do find the size of the .mtx file is in this range
79G values.mtx
So looking at the size i didn't go into the next step which you mentioned about creating seurat object.
As the h5ad was around 14GB, my doubt is why is the .mtx file is so large, and if i create seurat object do we end up similar size or it will be close to 70GB. Do let know your insight.
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