# Analysis ## Clustering `SAW count`, `realign` and `reanalyze` outputs spatial cluster results in AnnData H5AD format. These files record preprocessing, dimensionality reduction, clustering and differential expression analysis. {% hint style="info" %} Spatial clustering and UMAP projection in the H5AD can be visualized in **StereoMap**. {% endhint %} Here is an example for displaying information recorded in an H5AD: ```sh $ h5dump -n ./C04144D5/outs/analysis/C04144D5.bin20_1.0.h5ad HDF5 "./C04144D5/outs/analysis/C04144D5.bin20_1.0.h5ad" { FILE_CONTENTS { group / group /X dataset /X/data dataset /X/indices dataset /X/indptr group /layers group /layers/log1p dataset /layers/log1p/data dataset /layers/log1p/indices dataset /layers/log1p/indptr group /obs dataset /obs/_index group /obs/leiden dataset /obs/leiden/categories dataset /obs/leiden/codes dataset /obs/n_genes_by_counts group /obs/orig.ident dataset /obs/orig.ident/categories dataset /obs/orig.ident/codes dataset /obs/pct_counts_mt dataset /obs/total_counts dataset /obs/x dataset /obs/y group /obsm dataset /obsm/X_pca dataset /obsm/X_umap dataset /obsm/spatial group /obsp group /obsp/connectivities dataset /obsp/connectivities/data dataset /obsp/connectivities/indices dataset /obsp/connectivities/indptr group /obsp/distances dataset /obsp/distances/data dataset /obsp/distances/indices dataset /obsp/distances/indptr group /raw group /raw/X dataset /raw/X/data dataset /raw/X/indices dataset /raw/X/indptr group /raw/var dataset /raw/var/_index dataset /raw/var/mean_umi dataset /raw/var/n_cells dataset /raw/var/n_counts group /raw/var/real_gene_name dataset /raw/var/real_gene_name/categories dataset /raw/var/real_gene_name/codes group /raw/varm group /uns dataset /uns/bin_size dataset /uns/bin_type group /uns/gene_exp_leiden dataset /uns/gene_exp_leiden/1 ... dataset /uns/gene_exp_leiden/_index group /uns/hvg dataset /uns/hvg/method group /uns/hvg/params dataset /uns/hvg/source group /uns/key_record dataset /uns/key_record/cluster dataset /uns/key_record/gene_exp_cluster dataset /uns/key_record/hvg dataset /uns/key_record/marker_genes dataset /uns/key_record/neighbors dataset /uns/key_record/pca dataset /uns/key_record/umap dataset /uns/leiden_resolution dataset /uns/merged group /uns/neighbors dataset /uns/neighbors/connectivities_key dataset /uns/neighbors/distances_key group /uns/neighbors/params dataset /uns/neighbors/params/method dataset /uns/neighbors/params/metric dataset /uns/neighbors/params/n_neighbors dataset /uns/omics dataset /uns/pca_variance_ratio group /uns/rank_genes_groups dataset /uns/rank_genes_groups/logfoldchanges group /uns/rank_genes_groups/mean_count dataset /uns/rank_genes_groups/mean_count/1 ... dataset /uns/rank_genes_groups/mean_count/_index dataset /uns/rank_genes_groups/names group /uns/rank_genes_groups/params dataset /uns/rank_genes_groups/params/corr_method dataset /uns/rank_genes_groups/params/groupby dataset /uns/rank_genes_groups/params/layer dataset /uns/rank_genes_groups/params/method dataset /uns/rank_genes_groups/params/reference dataset /uns/rank_genes_groups/params/use_raw group /uns/rank_genes_groups/pts dataset /uns/rank_genes_groups/pts/1 ... dataset /uns/rank_genes_groups/pts/_index group /uns/rank_genes_groups/pts_rest dataset /uns/rank_genes_groups/pts_rest/1 ... dataset /uns/rank_genes_groups/pts_rest/_index dataset /uns/rank_genes_groups/pvals dataset /uns/rank_genes_groups/pvals_adj dataset /uns/rank_genes_groups/scores dataset /uns/resolution dataset /uns/result_keys group /uns/sn dataset /uns/sn/_index dataset /uns/sn/batch dataset /uns/sn/sn group /var dataset /var/_index dataset /var/dispersions dataset /var/dispersions_norm dataset /var/highly_variable dataset /var/mean_umi dataset /var/means dataset /var/n_cells dataset /var/n_counts group /var/real_gene_name dataset /var/real_gene_name/categories dataset /var/real_gene_name/codes group /varm group /varp } } ``` ### Check datasets Observe the information via Python Jupyter Notebook. Usually, AnnData package is used to check the metadata stored in an H5AD file. ```python import anndata as ad adata = ad.read_h5ad('./C4144D5.h5ad') ``` When you input the variable `adata`, it will return a description of the data content contained within the `adata` object. ```python # adata AnnData object with n_obs × n_vars = 439383 × 29759 obs: 'total_counts', 'n_genes_by_counts', 'pct_counts_mt', 'leiden', 'orig.ident', 'x', 'y' var: 'real_gene_name', 'n_cells', 'n_counts', 'mean_umi', 'means', 'dispersions', 'dispersions_norm', 'highly_variable' uns: 'bin_size', 'bin_type', 'gene_exp_leiden', 'hvg', 'leiden_resolution', 'neighbors', 'omics', 'pca_variance_ratio', 'rank_genes_groups', 'resolution', 'sn' obsm: 'X_pca', 'X_umap', 'spatial' obsp: 'connectivities', 'distances' ``` * **`obs`**: Observations metadata (cell-level annotations). A table (DataFrame) storing information about cells, such as `total_counts` namely MID, `n_genes_by_counts` namely gene type, `leiden` cluster labels and spatial coordinates. * **`var`**: Variables metadata (gene-level annotations). A table (DataFrame) storing information about genes, such as gene names, whether they are highly variable, or their biological functions. `adata.X` is a preprocessed and normalized gene expression matrix, typically represented as a sparse matrix. Type `adata.obs` to observe the Observations metadata: ```python # adata.obs total_counts n_genes_by_counts pct_counts_mt leiden orig.ident x y 5583457496640 5 5 0.0 16 sample 1300 11840 5583457496660 24 19 0.0 16 sample 1300 11860 5583457496680 35 24 0.0 16 sample 1300 11880 5583457496700 26 20 0.0 16 sample 1300 11900 5583457496720 20 13 0.0 16 sample 1300 11920 ... ... ... ... ... ... ... ... 84782654432300 5 4 0.0 16 sample 19740 9260 84782654432320 73 36 0.0 2 sample 19740 9280 84782654432340 66 33 0.0 2 sample 19740 9300 84782654432360 73 39 0.0 2 sample 19740 9320 84782654432380 18 9 0.0 16 sample 19740 9340 ``` Clusters out from Leiden algorithm are `adata.obs['leiden']`. Type `adata.var` to observe the Variables metadata: ```python # adata.var real_gene_name n_cells n_counts mean_umi means dispersions dispersions_norm highly_variable ENSMUSG00000000001 Gnai3 3641 4927 1.353200 0.218442 3.864523 0.787665 True ENSMUSG00000000003 Pbsn 84 99 1.178571 0.004794 3.560551 -0.295258 False ENSMUSG00000000028 Cdc45 279 335 1.200717 0.016009 3.475172 -0.599423 False ENSMUSG00000000031 H19 11 13 1.181818 0.000698 3.654413 0.039135 False ENSMUSG00000000037 Scml2 890 1142 1.283146 0.053320 3.649287 0.020871 False ... ... ... ... ... ... ... ... ... ENSMUSG00000116984 CT030713.2 339 418 1.233038 0.020487 3.625916 -0.062388 False ENSMUSG00000116987 AC150035.3 212 268 1.264151 0.013059 3.599458 -0.156647 False ENSMUSG00000116988 AC164314.2 479 630 1.315240 0.029183 3.738448 0.338513 False ENSMUSG00000116989 AC131339.4 67 77 1.149254 0.003788 3.486996 -0.557302 False ENSMUSG00000116993 AC135964.2 210 276 1.314286 0.012649 3.561962 -0.290230 False ``` Also, you can obtain the results of dimensionality reduction analysis. ```python # adata.obsm['X_pca'] array([[ -6.2587485 , 4.520802 , -9.568217 , ..., -1.9764128 , 1.8916789 , -1.5253358 ], [ -8.745589 , -0.8465148 , -5.714299 , ..., -0.06065813, 0.4646256 , -1.4891121 ], [ -9.6469 , -0.99549234, -3.3434439 , ..., -1.5173951 , 0.70049083, -0.56164753], ..., [-10.14395 , -1.4017884 , -2.0154784 , ..., -1.759757 , 0.12367768, -0.3570788 ], [ -9.854649 , -0.06418456, -1.2702237 , ..., 0.20150849, -0.12149827, -0.20047918], [-11.477859 , -0.91366583, -3.719183 , ..., -1.4211301 , 0.35585332, -0.44572574]], dtype=float32) # adata.obsm['X_umap'] array([[-1.4992443, 1.803457 ], [ 1.5087016, -0.8629851], [ 2.234619 , -1.2892196], ..., [ 3.3134413, -1.8212844], [ 3.317021 , -1.615297 ], [ 1.4269315, -1.217469 ]], dtype=float32) ``` ## Differential expression analysis `SAW count`, `realign` and `reanalyze` outputs differential expression analysis results in CSV format. {% hint style="info" %} There are two kinds of results related to differential expression analysis, namely `find_marker_genes.csv` and `_marker_features.csv`. * `find_marker_genes.csv` is the original output file. * `_marker_features.csv` is [a formatted CSV](#differential-expression) that records mean MID counts, L2FC, adjusted p-value, and expression ratio of marker features for each cluster. {% endhint %} For each feature per cluster, the following terms were computed: * Mean MID Count * Log2 fold change of expression * Adjusted p-value (confidence level of feature expression in current cluster relative to others) * Expression proprotion. `Cluster 1 % of expressed = 1` means this feature is expressed in all cells/bins in the current cluster To obtain differential expression analysis from an H5AD file, read it with `anndata` package as well. Information of rank genes is stored in `adata.uns['rank_genes_groups']`, which is a dictionary object. Retrieve the required information based on the specified key. ```python # adata.uns['rank_genes_groups'].keys() dict_keys(['logfoldchanges', 'mean_count', 'names', 'params', 'pts', 'pts_rest', 'pvals', 'pvals_adj', 'scores']) ``` The output CSV under `/outs/analysis` records the format-integrated differential expression analysis results. {% code title="\\_marker\_features.csv" %} ```csv Feature ID,Feature Name,Cluster 1 Mean MID Count,Cluster 1 Log2 fold change,Cluster 1 Adjusted p-value,Cluster 1 % of expressed, ... ,Cluster 20 Mean MID Count,Cluster 20 Log2 fold change,Cluster 20 Adjusted p-value,Cluster 20 % of expressed ENSMUSG00000016559,H3f3b,67.1754386,42.00155933,1.76E-41,1, ... ,0.076923077,-63.19518177,0,0.076923077 ``` {% endcode %} {% hint style="info" %} Differential expression analysis result `_marker_features.csv` can be loaded in `StereoMap`and `Excel`. {% endhint %} ## Multiomics clustering If you perform `SAW reanalyze` on the Stereo-CITE T FF sample, its jointly multi-omics clustering result will be saved in H5MU. Here is an example for displaying information recorded in an H5MU: ```bash $ h5dump -n /outs/analysis/.bin20.h5mu HDF5 "/outs/analysis/.bin20.h5mu" { FILE_CONTENTS { group / group /mod group /mod/multiomics dataset /mod/multiomics/X group /mod/multiomics/layers dataset /mod/multiomics/layers/denoised_rna group /mod/multiomics/obs dataset /mod/multiomics/obs/_index group /mod/multiomics/obs/leiden dataset /mod/multiomics/obs/leiden/categories dataset /mod/multiomics/obs/leiden/codes dataset /mod/multiomics/obs/n_genes_by_counts group /mod/multiomics/obs/orig.ident dataset /mod/multiomics/obs/orig.ident/categories dataset /mod/multiomics/obs/orig.ident/codes dataset /mod/multiomics/obs/pct_counts_mt dataset /mod/multiomics/obs/total_counts dataset /mod/multiomics/obs/x dataset /mod/multiomics/obs/y group /mod/multiomics/obsm dataset /mod/multiomics/obsm/X_totalVI dataset /mod/multiomics/obsm/X_umap dataset /mod/multiomics/obsm/spatial group /mod/multiomics/obsp group /mod/multiomics/obsp/connectivities dataset /mod/multiomics/obsp/connectivities/data dataset /mod/multiomics/obsp/connectivities/indices dataset /mod/multiomics/obsp/connectivities/indptr group /mod/multiomics/obsp/distances dataset /mod/multiomics/obsp/distances/data dataset /mod/multiomics/obsp/distances/indices dataset /mod/multiomics/obsp/distances/indptr group /mod/multiomics/raw group /mod/multiomics/raw/X dataset /mod/multiomics/raw/X/data dataset /mod/multiomics/raw/X/indices dataset /mod/multiomics/raw/X/indptr group /mod/multiomics/raw/var dataset /mod/multiomics/raw/var/_index dataset /mod/multiomics/raw/var/mean_umi dataset /mod/multiomics/raw/var/n_cells dataset /mod/multiomics/raw/var/n_counts group /mod/multiomics/raw/var/real_gene_name dataset /mod/multiomics/raw/var/real_gene_name/categories dataset /mod/multiomics/raw/var/real_gene_name/codes group /mod/multiomics/raw/varm group /mod/multiomics/uns dataset /mod/multiomics/uns/bin_size dataset /mod/multiomics/uns/bin_type group /mod/multiomics/uns/gene_exp_leiden dataset /mod/multiomics/uns/gene_exp_leiden/1 ... dataset /mod/multiomics/uns/gene_exp_leiden/9 dataset /mod/multiomics/uns/gene_exp_leiden/_index group /mod/multiomics/uns/hvg dataset /mod/multiomics/uns/hvg/method group /mod/multiomics/uns/hvg/params dataset /mod/multiomics/uns/hvg/source dataset /mod/multiomics/uns/leiden_resolution group /mod/multiomics/uns/neighbors dataset /mod/multiomics/uns/neighbors/connectivities_key dataset /mod/multiomics/uns/neighbors/distance_key dataset /mod/multiomics/uns/omics dataset /mod/multiomics/uns/resolution group /mod/multiomics/uns/sn dataset /mod/multiomics/uns/sn/_index dataset /mod/multiomics/uns/sn/batch dataset /mod/multiomics/uns/sn/sn group /mod/multiomics/var dataset /mod/multiomics/var/_index dataset /mod/multiomics/var/highly_variable dataset /mod/multiomics/var/highly_variable_nbatches dataset /mod/multiomics/var/highly_variable_rank dataset /mod/multiomics/var/mean_umi dataset /mod/multiomics/var/means dataset /mod/multiomics/var/n_cells dataset /mod/multiomics/var/n_counts group /mod/multiomics/var/real_gene_name dataset /mod/multiomics/var/real_gene_name/categories dataset /mod/multiomics/var/real_gene_name/codes dataset /mod/multiomics/var/variances dataset /mod/multiomics/var/variances_norm group /mod/multiomics/varm group /mod/multiomics/varp group /mod/protein ... group /mod/rna ... group /obs dataset /obs/_index dataset /obs/_scvi_batch dataset /obs/_scvi_labels dataset /obs/_scvi_raw_norm_scaling group /obsm dataset /obsm/multiomics dataset /obsm/protein dataset /obsm/rna group /obsmap dataset /obsmap/multiomics dataset /obsmap/protein dataset /obsmap/rna group /obsp group /uns dataset /uns/_scvi_manager_uuid dataset /uns/_scvi_uuid group /var dataset /var/_index dataset /var/mean_umi dataset /var/n_cells dataset /var/n_counts group /var/real_gene_name dataset /var/real_gene_name/categories dataset /var/real_gene_name/codes group /varm dataset /varm/multiomics dataset /varm/protein dataset /varm/rna group /varmap dataset /varmap/multiomics dataset /varmap/protein dataset /varmap/rna group /varp } } ```