MMP1 single-cell analysis summary for GPT
=========================================

Generated time:
[1] "2026-07-06 11:53:49 CST"

R working directory:
[1] "H:/糖尿病足--疼痛--中药/6.单细胞"

Output directory:
[1] "MMP1_single_cell_analysis"


1. Object overview
------------------
Seurat object found: YES
Cells: 69944 
Genes: 22889 
Default assay: RNA 

Metadata columns:
 [1] "orig.ident"                         "nCount_RNA"                         "nFeature_RNA"                      
 [4] "Sample"                             "Group"                              "Type"                              
 [7] "percent.hb"                         "percent.mt"                         "percent.rb"                        
[10] "RNA_snn_res.0.8"                    "seurat_clusters"                    "SingleR_label"                     
[13] "SingleR_major"                      "CellType_for_MMP1"                  "Group_for_MMP1"                    
[16] "Sample_for_MMP1"                    "MMP1_expr"                          "MMP1_positive"                     
[19] "MMP1_status"                        "ECM_remodeling_Score1"              "Inflammation_Score1"               
[22] "Angiogenesis_Score1"                "Oxidative_stress_Score1"            "Senescence_SASP_Score1"            
[25] "Ferroptosis_Score1"                 "Fibroblast_activation_Score1"       "Keratinocyte_wound_response_Score1"
[28] "Macrophage_activation_Score1"       "Endothelial_dysfunction_Score1"    


2. Key columns used
-------------------
gene_of_interest: [1] "MMP1"
celltype_col: [1] "SingleR_label"
group_col: [1] "Group"
sample_col: [1] "orig.ident"
control_group: [1] "Control"
disease_group: [1] "DFU"


3. Group and cell type composition
----------------------------------

Cell number by group
--------------------
vec
Control     DFU 
  28559   41385 

Cell number by cell type
------------------------
vec
          B cells      CD8+ T cells Endothelial cells  Epithelial cells       Fibroblasts               HSC     Keratinocytes 
             1128              5377              7498              1759             39801               680              7431 
      Macrophages         Monocytes           Neurons 
             2206              3241               823 

Cell number by cell type and group:
                   
                    Control   DFU
  B cells               229   899
  CD8+ T cells         1387  3990
  Endothelial cells    3449  4049
  Epithelial cells     1072   687
  Fibroblasts         16381 23420
  HSC                   508   172
  Keratinocytes        3913  3518
  Macrophages           403  1803
  Monocytes             669  2572
  Neurons               548   275


4. MMP1 expression overview
---------------------------
MMP1 expression summary:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   0.000   0.000   0.112   0.000   6.237 

MMP1 positive cells:

FALSE  TRUE 
66145  3799 

MMP1 positive ratio:
[1] 0.05431488

MMP1_positive table:

MMP1- MMP1+ 
66145  3799 

MMP1_status table:

 MMP1_low MMP1_high 
    68045      1899 


5. MMP1 expression by cell type
-------------------------------
# A tibble: 10 × 4
   CellType_for_MMP1 mean_MMP1 positive_ratio n_cells
   <fct>                 <dbl>          <dbl>   <int>
 1 Fibroblasts         0.181          0.0788    39801
 2 B cells             0.0497         0.0381     1128
 3 Monocytes           0.0366         0.0324     3241
 4 CD8+ T cells        0.0286         0.0193     5377
 5 Keratinocytes       0.0209         0.0318     7431
 6 Macrophages         0.0159         0.0136     2206
 7 Endothelial cells   0.0141         0.0157     7498
 8 Epithelial cells    0.0116         0.0142     1759
 9 HSC                 0.00480        0.00294     680
10 Neurons             0              0           823


6. DFU vs Control statistics by cell type
-----------------------------------------
# A tibble: 10 × 11
   CellType_for_MMP1 Control_n_cells Disease_n_cells Control_mean Disease_mean Control_median Disease_median logFC_Disease_vs_Control
   <fct>                       <int>           <int>        <dbl>        <dbl>          <dbl>          <dbl>                    <dbl>
 1 Keratinocytes                3913            3518     0.000160       0.0440              0              0                  0.0438 
 2 Fibroblasts                 16381           23420     0.154          0.199               0              0                  0.0446 
 3 Endothelial cells            3449            4049     0.00181        0.0246              0              0                  0.0228 
 4 B cells                       229             899     0.169          0.0192              0              0                 -0.150  
 5 Epithelial cells             1072             687     0              0.0297              0              0                  0.0297 
 6 CD8+ T cells                 1387            3990     0.0562         0.0190              0              0                 -0.0372 
 7 HSC                           508             172     0              0.0190              0              0                  0.0190 
 8 Macrophages                   403            1803     0.0171         0.0156              0              0                 -0.00151
 9 Monocytes                     669            2572     0.0334         0.0374              0              0                  0.00404
10 Neurons                       548             275     0              0                   0              0                  0      
# ℹ 3 more variables: p_value <dbl>, p_adj_BH <dbl>, significance <chr>


7. MMP1-positive cell ratio by cell type and group
--------------------------------------------------
# A tibble: 20 × 5
   CellType_for_MMP1 Group_for_MMP1 total_cells MMP1_positive_cells MMP1_positive_ratio
   <fct>             <fct>                <int>               <int>               <dbl>
 1 B cells           Control                229                  26            0.114   
 2 B cells           DFU                    899                  17            0.0189  
 3 CD8+ T cells      Control               1387                  50            0.0360  
 4 CD8+ T cells      DFU                   3990                  54            0.0135  
 5 Endothelial cells Control               3449                  10            0.00290 
 6 Endothelial cells DFU                   4049                 108            0.0267  
 7 Epithelial cells  Control               1072                   0            0       
 8 Epithelial cells  DFU                    687                  25            0.0364  
 9 Fibroblasts       Control              16381                 960            0.0586  
10 Fibroblasts       DFU                  23420                2176            0.0929  
11 HSC               Control                508                   0            0       
12 HSC               DFU                    172                   2            0.0116  
13 Keratinocytes     Control               3913                   1            0.000256
14 Keratinocytes     DFU                   3518                 235            0.0668  
15 Macrophages       Control                403                   9            0.0223  
16 Macrophages       DFU                   1803                  21            0.0116  
17 Monocytes         Control                669                  20            0.0299  
18 Monocytes         DFU                   2572                  85            0.0330  
19 Neurons           Control                548                   0            0       
20 Neurons           DFU                    275                   0            0       


8. Pseudobulk results
---------------------

Pseudobulk expression table, first 50 rows:
# A tibble: 50 × 7
   Sample_for_MMP1 Group_for_MMP1 CellType_for_MMP1 mean_MMP1 median_MMP1 positive_ratio n_cells
   <chr>           <fct>          <fct>                 <dbl>       <dbl>          <dbl>   <int>
 1 GSM5050523      DFU            B cells              0                0         0            5
 2 GSM5050523      DFU            CD8+ T cells         0                0         0          239
 3 GSM5050523      DFU            Endothelial cells    0.0158           0         0.0139     431
 4 GSM5050523      DFU            Epithelial cells     0                0         0           12
 5 GSM5050523      DFU            Fibroblasts          0.0224           0         0.0176    2898
 6 GSM5050523      DFU            HSC                  0                0         0           20
 7 GSM5050523      DFU            Keratinocytes        0.0159           0         0.0274     146
 8 GSM5050523      DFU            Macrophages          0                0         0           45
 9 GSM5050523      DFU            Monocytes            0                0         0           45
10 GSM5050523      DFU            Neurons              0                0         0            1
# ℹ 40 more rows
# ℹ Use `print(n = ...)` to see more rows

Pseudobulk statistics:
# A tibble: 10 × 9
   CellType_for_MMP1 Control_n_samples Disease_n_samples Control_mean Disease_mean logFC_Disease_vs_Cont…¹   p_value p_adj_BH significance
   <fct>                         <int>             <int>        <dbl>        <dbl>                   <dbl>     <dbl>    <dbl> <chr>       
 1 Keratinocytes                     9                14    0.0000866       0.0448                 0.0447    0.00386   0.0214 **          
 2 Fibroblasts                      10                14    0.146           0.224                  0.0786    0.00569   0.0214 **          
 3 Endothelial cells                10                14    0.0118          0.0355                 0.0237    0.00713   0.0214 **          
 4 Epithelial cells                  9                14    0               0.0193                 0.0193    0.0953    0.198  ns          
 5 Macrophages                      10                13    0.0119          0.0266                 0.0147    0.110     0.198  ns          
 6 CD8+ T cells                     10                14    0.0141          0.0187                 0.00457   0.248     0.314  ns          
 7 HSC                               8                12    0               0.0408                 0.0408    0.267     0.314  ns          
 8 Monocytes                        10                14    0.0125          0.0246                 0.0121    0.280     0.314  ns          
 9 B cells                           2                13    0.0855          0.0170                -0.0684    0.502     0.502  ns          
10 Neurons                           9                11    0               0                      0       NaN       NaN      NA          
# ℹ abbreviated name: ¹​logFC_Disease_vs_Control


9. MMP1-related module scores
-----------------------------
Marker sets used:
$ECM_remodeling
[1] 19

$Inflammation
[1] 15

$Angiogenesis
[1] 11

$Oxidative_stress
[1] 11

$Senescence_SASP
[1] 12

$Ferroptosis
[1] 12

$Fibroblast_activation
[1] 12

$Keratinocyte_wound_response
[1] 11

$Macrophage_activation
[1] 13

$Endothelial_dysfunction
[1] 11


Score columns:
 [1] "ECM_remodeling_Score1"              "Inflammation_Score1"                "Angiogenesis_Score1"               
 [4] "Oxidative_stress_Score1"            "Senescence_SASP_Score1"             "Ferroptosis_Score1"                
 [7] "Fibroblast_activation_Score1"       "Keratinocyte_wound_response_Score1" "Macrophage_activation_Score1"      
[10] "Endothelial_dysfunction_Score1"    

Correlation between MMP1 and module scores:
                                  Score Spearman_rho       p_value      p_adj_BH
rho               ECM_remodeling_Score1   0.24022938  0.000000e+00  0.000000e+00
rho3            Oxidative_stress_Score1   0.16090863  0.000000e+00  0.000000e+00
rho4             Senescence_SASP_Score1   0.27413452  0.000000e+00  0.000000e+00
rho5                 Ferroptosis_Score1   0.19705894  0.000000e+00  0.000000e+00
rho6       Fibroblast_activation_Score1   0.16989125  0.000000e+00  0.000000e+00
rho1                Inflammation_Score1   0.13324292 1.970653e-274 3.284422e-274
rho8       Macrophage_activation_Score1   0.09540507 4.225120e-141 6.035885e-141
rho2                Angiogenesis_Score1   0.06865460  7.687388e-74  9.609235e-74
rho7 Keratinocyte_wound_response_Score1  -0.04175511  2.252464e-28  2.502738e-28
rho9     Endothelial_dysfunction_Score1   0.04010488  2.660723e-26  2.660723e-26


10. MMP1-high vs MMP1-low DEG summary
-------------------------------------
                                  File Rows Significant_genes Up_genes Down_genes
1     DEG_MMP1_high_vs_low_B_cells.csv 4753                78       78          0
2 DEG_MMP1_high_vs_low_Fibroblasts.csv 5193              3143     2226        917
3   DEG_MMP1_high_vs_low_Monocytes.csv 4907                16       16          0
                                                                                                                                                               Top_20_genes
1 MMP1, ABLIM3, POT1-AS1, AC092111.1, ACAN, WFDC1, LGALS7, ZNF471, AC239799.2, ZNF80, NPAS4, AC079341.2, AL356276.1, ZNF707, PALMD, ANKRD23, DUSP19, IL1RAP, AMACR, COL10A1
2                                 MMP1, CA12, CHI3L1, CD82, SLC16A3, TNFAIP6, PDPN, MGP, G0S2, TMEM158, WISP1, WNT5A, STEAP1, PTGES, MME, CHI3L2, RGS3, SPHK1, COL7A1, NRP2
3        MMP1, GLIS1, ABHD14A-ACY1, AP001434.1, MTRNR2L8, NPAS2, FAT4, AL078639.1, MPDZ, DYNC2H1, CA12, RIPOR3, APLP1, PTGFR, PRKG1, SAA1, PLEKHA4, LOX, DNM3OS, AC025580.2


11. Enrichment summary
----------------------
                                File Rows Significant_terms
1     GO_BP_MMP1_high_up_B_cells.csv    1                 1
2 GO_BP_MMP1_high_up_Fibroblasts.csv  658               652
3   GO_BP_MMP1_high_up_Monocytes.csv    2                 2
                                                                                                                                                                                                                                                                                                          Top_10_terms
1                                                                                                                                                                                                                                                                                        paraxial mesoderm development
2 extracellular matrix organization | extracellular structure organization | external encapsulating structure organization | collagen fibril organization | glycoprotein biosynthetic process | glycoprotein metabolic process | protein glycosylation | macromolecule glycosylation | protein folding | glycosylation
3                                                                                                                                                                                                                                   forebrain development | positive regulation of cytosolic calcium ion concentration


12. Important output files
--------------------------

Tables directory:
[1] "MMP1_single_cell_analysis/Tables"

Table files:
 [1] "Cell_number_by_CellType_Group.csv"                              "DEG_files_summary.csv"                                         
 [3] "Enrichment_files_summary.csv"                                   "MMP1_basic_summary.csv"                                        
 [5] "MMP1_by_CellType_Group_summary.csv"                             "MMP1_correlation_with_module_scores.csv"                       
 [7] "MMP1_expression_DFU_vs_Control_by_CellType_Wilcoxon.csv"        "MMP1_high_vs_low_DEG/DEG_MMP1_high_vs_low_B_cells.csv"         
 [9] "MMP1_high_vs_low_DEG/DEG_MMP1_high_vs_low_Fibroblasts.csv"      "MMP1_high_vs_low_DEG/DEG_MMP1_high_vs_low_Monocytes.csv"       
[11] "MMP1_high_vs_low_enrichment/GO_BP_MMP1_high_up_B_cells.csv"     "MMP1_high_vs_low_enrichment/GO_BP_MMP1_high_up_Fibroblasts.csv"
[13] "MMP1_high_vs_low_enrichment/GO_BP_MMP1_high_up_Monocytes.csv"   "MMP1_mean_expression_by_CellType.csv"                          
[15] "MMP1_positive_cell_ratio_by_CellType_Group.csv"                 "MMP1_pseudobulk_by_Sample_CellType.csv"                        
[17] "MMP1_pseudobulk_statistics_by_CellType.csv"                     "MMP1_related_marker_sets_used.rds"                             
[19] "MMP1_related_module_scores.csv"                                 "MMP1_single_cell_expression_long_table.csv"                    

Figures directory:
[1] "MMP1_single_cell_analysis/Figures"

Figure files:
 [1] "01_UMAP_CellType.pdf"                           "01_UMAP_CellType.png"                          
 [3] "02_UMAP_Group.pdf"                              "02_UMAP_Group.png"                             
 [5] "03_MMP1_FeaturePlot.pdf"                        "03_MMP1_FeaturePlot.png"                       
 [7] "04_MMP1_FeaturePlot_split_by_group.pdf"         "04_MMP1_FeaturePlot_split_by_group.png"        
 [9] "05_MMP1_VlnPlot_CellType.pdf"                   "05_MMP1_VlnPlot_CellType.png"                  
[11] "06_MMP1_DotPlot_CellType.pdf"                   "06_MMP1_DotPlot_CellType.png"                  
[13] "07_MMP1_Boxplot_DFU_vs_Control_by_CellType.pdf" "07_MMP1_Boxplot_DFU_vs_Control_by_CellType.png"
[15] "08_MMP1_Global_Boxplot_DFU_vs_Control.pdf"      "08_MMP1_Global_Boxplot_DFU_vs_Control.png"     
[17] "09_MMP1_positive_ratio_by_CellType_Group.pdf"   "09_MMP1_positive_ratio_by_CellType_Group.png"  
[19] "10_MMP1_Pseudobulk_Boxplot_by_CellType.pdf"     "10_MMP1_Pseudobulk_Boxplot_by_CellType.png"    
[21] "11_MMP1_ModuleScore_Correlation.pdf"            "11_MMP1_ModuleScore_Correlation.png"           
[23] "12_GO_BP_MMP1_high_up_B_cells.pdf"              "12_GO_BP_MMP1_high_up_B_cells.png"             
[25] "12_GO_BP_MMP1_high_up_Fibroblasts.pdf"          "12_GO_BP_MMP1_high_up_Fibroblasts.png"         
[27] "12_GO_BP_MMP1_high_up_Monocytes.pdf"            "12_GO_BP_MMP1_high_up_Monocytes.png"           
[29] "13_MMP1_related_gene_heatmap_by_CellType.pdf"  


13. Session information
-----------------------
R version 4.5.2 (2025-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26200)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=Chinese (Simplified)_China.utf8  LC_CTYPE=Chinese (Simplified)_China.utf8    LC_MONETARY=Chinese (Simplified)_China.utf8
[4] LC_NUMERIC=C                                LC_TIME=Chinese (Simplified)_China.utf8    

time zone: Asia/Shanghai
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] UCell_2.12.0           DOSE_4.2.0             enrichplot_1.28.4      org.Hs.eg.db_3.21.0    AnnotationDbi_1.70.0  
 [6] IRanges_2.44.0         S4Vectors_0.48.1       Biobase_2.70.0         BiocGenerics_0.56.0    generics_0.1.4        
[11] clusterProfiler_4.16.0 Seurat_5.5.0           SeuratObject_5.4.0     sp_2.2-1               Matrix_1.7-5          
[16] scales_1.4.0           RColorBrewer_1.1-3     pheatmap_1.0.13        patchwork_1.3.2        readr_2.2.0           
[21] tidyr_1.3.2            dplyr_1.2.1            ggpubr_0.6.3           ggplot2_4.0.3         

loaded via a namespace (and not attached):
  [1] fs_2.1.0                    matrixStats_1.5.0           spatstat.sparse_3.2-0       httr_1.4.8                 
  [5] tools_4.5.2                 sctransform_0.4.3           backports_1.5.1             utf8_1.2.6                 
  [9] R6_2.6.1                    lazyeval_0.2.3              uwot_0.2.4                  withr_3.0.2                
 [13] gridExtra_2.3               progressr_0.19.0            cli_3.6.6                   textshaping_1.0.5          
 [17] spatstat.explore_3.8-1      fastDummies_1.7.6           labeling_0.4.3              S7_0.2.2                   
 [21] spatstat.data_3.1-9         ggridges_0.5.7              pbapply_1.7-4               systemfonts_1.3.2          
 [25] yulab.utils_0.2.4           gson_0.1.0                  R.utils_2.13.0              dichromat_2.0-0.1          
 [29] parallelly_1.47.0           limma_3.64.3                rstudioapi_0.18.0           RSQLite_3.53.1             
 [33] gridGraphics_0.5-1          shape_1.4.6.1               ica_1.0-3                   spatstat.random_3.5-0      
 [37] car_3.1-5                   GO.db_3.21.0                ggbeeswarm_0.7.3            abind_1.4-8                
 [41] R.methodsS3_1.8.2           lifecycle_1.0.5             carData_3.0-6               SummarizedExperiment_1.40.0
 [45] qvalue_2.40.0               SparseArray_1.8.1           Rtsne_0.17                  grid_4.5.2                 
 [49] blob_1.3.0                  promises_1.5.0              crayon_1.5.3                miniUI_0.1.2               
 [53] ggtangle_0.1.2              lattice_0.22-9              cowplot_1.2.0               KEGGREST_1.48.1            
 [57] pillar_1.11.1               fgsea_1.34.2                GenomicRanges_1.62.0        future.apply_1.20.2        
 [61] codetools_0.2-20            fastmatch_1.1-8             glue_1.8.1                  ggfun_0.2.0                
 [65] spatstat.univar_3.2-0       data.table_1.18.4           vctrs_0.7.3                 png_0.1-9                  
 [69] treeio_1.32.0               spam_2.11-4                 gtable_0.3.6                cachem_1.1.0               
 [73] S4Arrays_1.8.1              mime_0.13                   Seqinfo_1.0.0               survival_3.8-6             
 [77] SingleCellExperiment_1.32.0 statmod_1.5.2               fitdistrplus_1.2-6          ROCR_1.0-12                
 [81] nlme_3.1-169                ggtree_3.16.3               bit64_4.8.2                 RcppAnnoy_0.0.23           
 [85] GenomeInfoDb_1.44.3         data.tree_1.2.0             irlba_2.3.7                 vipor_0.4.7                
 [89] KernSmooth_2.23-26          otel_0.2.0                  colorspace_2.1-2            DBI_1.3.0                  
 [93] ggrastr_1.0.2               tidyselect_1.2.1            bit_4.6.0                   compiler_4.5.2             
 [97] BiocNeighbors_2.2.0         DelayedArray_0.34.1         plotly_4.12.0               lmtest_0.9-40              
[101] rappdirs_0.3.4              stringr_1.6.0               digest_0.6.39               goftest_1.2-3              
[105] presto_1.0.0                spatstat.utils_3.2-3        XVector_0.50.0              htmltools_0.5.9            
[109] pkgconfig_2.0.3             MatrixGenerics_1.22.0       fastmap_1.2.0               rlang_1.2.0                
[113] GlobalOptions_0.1.4         htmlwidgets_1.6.4           UCSC.utils_1.4.0            shiny_1.13.0               
[117] farver_2.1.2                zoo_1.8-15                  jsonlite_2.0.0              BiocParallel_1.42.2        
[121] GOSemSim_2.34.0             R.oo_1.27.1                 magrittr_2.0.5              Formula_1.2-5              
[125] GenomeInfoDbData_1.2.14     ggplotify_0.1.3             dotCall64_1.2               Rcpp_1.1.1-1.1             
[129] ape_5.8-1                   reticulate_1.46.0           stringi_1.8.7               MASS_7.3-65                
[133] plyr_1.8.9                  parallel_4.5.2              listenv_0.10.1              ggrepel_0.9.8              
[137] deldir_2.0-4                Biostrings_2.76.0           splines_4.5.2               tensor_1.5.1               
[141] hms_1.1.4                   circlize_0.4.18             igraph_2.3.2                spatstat.geom_3.8-1        
[145] ggsignif_0.6.4              RcppHNSW_0.7.0              reshape2_1.4.5              BiocManager_1.30.27        
[149] tzdb_0.5.0                  httpuv_1.6.17               networkD3_0.4.1             RANN_2.6.2                 
[153] purrr_1.2.2                 polyclip_1.10-7             future_1.70.0               scattermore_1.2            
[157] broom_1.0.13                xtable_1.8-8                RSpectra_0.16-2             tidytree_0.4.7             
[161] rstatix_0.7.3               later_1.4.8                 viridisLite_0.4.3           ragg_1.5.2                 
[165] tibble_3.3.1                aplot_0.2.9                 memoise_2.0.1               beeswarm_0.4.0             
[169] cluster_2.1.8.2             globals_0.19.1             
