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update Figure1 D and E #7

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196 changes: 196 additions & 0 deletions figure1-ABE/data/ari.tsv
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Original file line number Diff line number Diff line change
@@ -0,0 +1,196 @@
Method Data Technology ARI
0 BANKSY STARmap_plus STARmap+ 0.586546
1 BANKSY abc_atlas_wmb_thalamus MERFISH 0.42765
2 BANKSY cosmx_liver cosmx 0.19102915
3 BANKSY cosmx_lung cosmx 0.24296299999999998
4 BANKSY libd_dlpfc Visium 0.3923225
5 BANKSY merfish_devheart Merfish 0.5797235000000001
6 BANKSY osmfish_Ssp osmFISH 0.0328302
7 BANKSY slideseq2_olfactory_bulb Slideseq2 0.40108900000000003
8 BANKSY stereoseq_liver Stereo-seq 0.11386850000000001
9 BANKSY stereoseq_mouse_embryo Stereo-seq 0.351812
10 BANKSY visium_breast_cancer_SEDR Visium 0.446539
11 BANKSY visium_chicken_heart Visium 0.251147
12 BANKSY xenium-ffpe-bc-idc Xenium 0.0947465
13 BANKSY xenium-mouse-brain-SergioSalas Xenium 0.418823
14 BayesSpace libd_dlpfc Visium 0.47879499999999997
15 BayesSpace visium_breast_cancer_SEDR Visium 0.510135
16 BayesSpace visium_chicken_heart Visium 0.44309
17 CellCharter STARmap_plus STARmap+ 0.525049
18 CellCharter abc_atlas_wmb_thalamus MERFISH 0.41834
19 CellCharter cosmx_liver cosmx 0.22284700000000002
20 CellCharter cosmx_lung cosmx 0.21436899999999998
21 CellCharter libd_dlpfc Visium 0.43249
22 CellCharter merfish_devheart Merfish 0.5211060000000001
23 CellCharter osmfish_Ssp osmFISH 0.477721
24 CellCharter slideseq2_olfactory_bulb Slideseq2 0.322064
25 CellCharter stereoseq_liver Stereo-seq 0.1311165
26 CellCharter stereoseq_mouse_embryo Stereo-seq 0.343304
27 CellCharter visium_breast_cancer_SEDR Visium 0.507811
28 CellCharter visium_chicken_heart Visium 0.40035699999999996
29 CellCharter visium_hd_cancer_colon Visium HD 0.307882
30 CellCharter xenium-ffpe-bc-idc Xenium 0.17313650000000003
31 CellCharter xenium-mouse-brain-SergioSalas Xenium 0.437107
32 DR-SC STARmap_plus STARmap+ 0.298422
33 DR-SC abc_atlas_wmb_thalamus MERFISH 0.41131249999999997
34 DR-SC cosmx_lung cosmx 0.173799
35 DR-SC libd_dlpfc Visium 0.424577
36 DR-SC merfish_devheart Merfish 0.411312
37 DR-SC slideseq2_olfactory_bulb Slideseq2 0.294904
38 DR-SC stereoseq_liver Stereo-seq 0.2325575
39 DR-SC stereoseq_mouse_embryo Stereo-seq 0.375291
40 DR-SC visium_breast_cancer_SEDR Visium 0.509267
41 DR-SC visium_chicken_heart Visium 0.553949
42 DR-SC visium_hd_cancer_colon Visium HD 0.325487
43 DR-SC xenium-ffpe-bc-idc Xenium 0.1331735
44 DR-SC xenium-mouse-brain-SergioSalas Xenium 0.160782
45 Giotto STARmap_plus STARmap+ 0.0
46 Giotto abc_atlas_wmb_thalamus MERFISH 0.410396
47 Giotto libd_dlpfc Visium 0.0
48 Giotto merfish_devheart Merfish 0.0
49 Giotto stereoseq_liver Stereo-seq 0.15579700000000002
50 Giotto visium_breast_cancer_SEDR Visium 0.590932
51 Giotto visium_hd_cancer_colon Visium HD 0.0
52 GraphST libd_dlpfc Visium 0.514263
53 GraphST visium_breast_cancer_SEDR Visium 0.518954
54 GraphST visium_chicken_heart Visium 0.466684
55 SCAN-IT STARmap_plus STARmap+ 0.605667
56 SCAN-IT abc_atlas_wmb_thalamus MERFISH 0.40502950000000004
57 SCAN-IT cosmx_liver cosmx 0.291428
58 SCAN-IT cosmx_lung cosmx 0.22969499999999998
59 SCAN-IT libd_dlpfc Visium 0.42672350000000003
60 SCAN-IT osmfish_Ssp osmFISH 0.438015
61 SCAN-IT slideseq2_olfactory_bulb Slideseq2 0.354806
62 SCAN-IT stereoseq_liver Stereo-seq 0.0808062
63 SCAN-IT visium_breast_cancer_SEDR Visium 0.49805
64 SCAN-IT visium_chicken_heart Visium 0.27552350000000003
65 SCAN-IT visium_hd_cancer_colon Visium HD 0.449759
66 SCAN-IT xenium-mouse-brain-SergioSalas Xenium 0.405461
67 scMEB libd_dlpfc Visium 0.382042
68 scMEB visium_breast_cancer_SEDR Visium 0.468681
69 scMEB visium_chicken_heart Visium 0.516841
70 scMEB visium_hd_cancer_colon Visium HD 0.27199
71 SEDR STARmap_plus STARmap+ 0.4440275
72 SEDR abc_atlas_wmb_thalamus MERFISH 0.39850450000000004
73 SEDR cosmx_lung cosmx 0.193699
74 SEDR libd_dlpfc Visium 0.512438
75 SEDR merfish_devheart Merfish 0.3588485
76 SEDR slideseq2_olfactory_bulb Slideseq2 0.38655
77 SEDR stereoseq_liver Stereo-seq 0.254971
78 SEDR stereoseq_mouse_embryo Stereo-seq 0.25578049999999997
79 SEDR visium_breast_cancer_SEDR Visium 0.454791
80 SEDR visium_chicken_heart Visium 0.3947265
81 SEDR visium_hd_cancer_colon Visium HD 0.226319
82 SEDR xenium-mouse-brain-SergioSalas Xenium 0.324439
83 SOTIP libd_dlpfc Visium 0.402126
84 STAGATE STARmap_plus STARmap+ 0.411152
85 STAGATE abc_atlas_wmb_thalamus MERFISH 0.3947405
86 STAGATE cosmx_lung cosmx 0.2001695
87 STAGATE libd_dlpfc Visium 0.4241495
88 STAGATE merfish_devheart Merfish 0.464059
89 STAGATE osmfish_Ssp osmFISH 0.123865
90 STAGATE slideseq2_olfactory_bulb Slideseq2 0.2770725
91 STAGATE stereoseq_mouse_embryo Stereo-seq 0.0302528
92 STAGATE visium_breast_cancer_SEDR Visium 0.423711
93 STAGATE visium_chicken_heart Visium 0.5278995
94 STAGATE visium_hd_cancer_colon Visium HD 0.392316
95 STAGATE xenium-mouse-brain-SergioSalas Xenium 0.233947
96 SpaceFlow STARmap_plus STARmap+ 0.633237
97 SpaceFlow abc_atlas_wmb_thalamus MERFISH 0.44786400000000004
98 SpaceFlow cosmx_liver cosmx 0.150306
99 SpaceFlow cosmx_lung cosmx 0.23240149999999998
100 SpaceFlow libd_dlpfc Visium 0.3248765
101 SpaceFlow merfish_devheart Merfish 0.5135645
102 SpaceFlow osmfish_Ssp osmFISH 0.254667
103 SpaceFlow slideseq2_olfactory_bulb Slideseq2 0.40415100000000004
104 SpaceFlow stereoseq_liver Stereo-seq 0.19564900000000002
105 SpaceFlow stereoseq_mouse_embryo Stereo-seq 0.385838
106 SpaceFlow visium_breast_cancer_SEDR Visium 0.417785
107 SpaceFlow visium_chicken_heart Visium 0.3205285
108 SpaceFlow visium_hd_cancer_colon Visium HD 0.40079200000000004
109 SpaceFlow xenium-ffpe-bc-idc Xenium 0.12696455
110 SpaceFlow xenium-mouse-brain-SergioSalas Xenium 0.449782
111 SPICEMIX abc_atlas_wmb_thalamus MERFISH 0.260981
112 SPICEMIX cosmx_lung cosmx 0.0907142
113 SPICEMIX libd_dlpfc Visium 0.203352
114 SPICEMIX stereoseq_liver Stereo-seq 0.12959
115 SPICEMIX visium_breast_cancer_SEDR Visium 0.347565
116 SPICEMIX visium_chicken_heart Visium 0.371323
117 SPICEMIX visium_hd_cancer_colon Visium HD 0.103312
118 SPICEMIX xenium-mouse-brain-SergioSalas Xenium 0.01649585
119 BASS STARmap_plus STARmap+ 0.578344
120 BASS abc_atlas_wmb_thalamus MERFISH 0.41315
121 BASS cosmx_lung cosmx 0.175627
122 BASS libd_dlpfc Visium 0.5021355
123 BASS merfish_devheart Merfish 0.619177
124 BASS slideseq2_olfactory_bulb Slideseq2 0.398977
125 BASS visium_breast_cancer_SEDR Visium 0.613401
126 BASS visium_chicken_heart Visium 0.4872265
127 BASS visium_hd_cancer_colon Visium HD 0.37428649999999997
128 MAPLE abc_atlas_wmb_thalamus MERFISH 0.311754
129 MAPLE libd_dlpfc Visium 0.294613
130 MAPLE slideseq2_olfactory_bulb Slideseq2 0.609203
131 MAPLE visium_chicken_heart Visium 0.272994
132 MERINGUE libd_dlpfc Visium 0.212302
133 PRECAST STARmap_plus STARmap+ 0.273902
134 PRECAST abc_atlas_wmb_thalamus MERFISH 0.371727
135 PRECAST cosmx_lung cosmx 0.166193
136 PRECAST libd_dlpfc Visium 0.451211
137 PRECAST merfish_devheart Merfish 0.416367
138 PRECAST slideseq2_olfactory_bulb Slideseq2 0.400574
139 PRECAST stereoseq_liver Stereo-seq 0.223543
140 PRECAST stereoseq_mouse_embryo Stereo-seq 0.370056
141 PRECAST visium_breast_cancer_SEDR Visium 0.551167
142 PRECAST visium_chicken_heart Visium 0.5196745
143 PRECAST visium_hd_cancer_colon Visium HD 0.441785
144 PRECAST xenium-ffpe-bc-idc Xenium 0.1795685
145 PRECAST xenium-mouse-brain-SergioSalas Xenium 0.128127
146 scanpy STARmap_plus STARmap+ 0.31263
147 scanpy abc_atlas_wmb_thalamus MERFISH 0.4141655
148 scanpy cosmx_liver cosmx 0.14987309999999998
149 scanpy cosmx_lung cosmx 0.1879835
150 scanpy libd_dlpfc Visium 0.1985045
151 scanpy merfish_devheart Merfish 0.3160575
152 scanpy osmfish_Ssp osmFISH 0.334391
153 scanpy slideseq2_olfactory_bulb Slideseq2 0.4190805
154 scanpy stereoseq_liver Stereo-seq 0.1918725
155 scanpy stereoseq_mouse_embryo Stereo-seq 0.408946
156 scanpy visium_breast_cancer_SEDR Visium 0.436272
157 scanpy visium_chicken_heart Visium 0.4960635
158 scanpy visium_hd_cancer_colon Visium HD 0.4087815
159 scanpy xenium-ffpe-bc-idc Xenium 0.2200805
160 scanpy xenium-mouse-brain-SergioSalas Xenium 0.226889
161 Seurat STARmap_plus STARmap+ 0.272646
162 Seurat abc_atlas_wmb_thalamus MERFISH 0.362862
163 Seurat cosmx_liver cosmx 0.1091928
164 Seurat cosmx_lung cosmx 0.16661
165 Seurat libd_dlpfc Visium 0.34361149999999996
166 Seurat merfish_devheart Merfish 0.25801799999999997
167 Seurat osmfish_Ssp osmFISH 0.0756463
168 Seurat slideseq2_olfactory_bulb Slideseq2 0.4620475
169 Seurat stereoseq_liver Stereo-seq 0.217646
170 Seurat stereoseq_mouse_embryo Stereo-seq 0.417248
171 Seurat visium_breast_cancer_SEDR Visium 0.443662
172 Seurat visium_chicken_heart Visium 0.42068099999999997
173 Seurat visium_hd_cancer_colon Visium HD 0.3896465
174 Seurat xenium-ffpe-bc-idc Xenium 0.1811205
175 Seurat xenium-mouse-brain-SergioSalas Xenium 0.1310385
176 spaGCN STARmap_plus STARmap+ 0.296375
177 spaGCN abc_atlas_wmb_thalamus MERFISH 0.4227365
178 spaGCN cosmx_lung cosmx 0.144267
179 spaGCN libd_dlpfc Visium 0.4648555
180 spaGCN merfish_devheart Merfish 0.389649
181 spaGCN slideseq2_olfactory_bulb Slideseq2 0.40776900000000005
182 spaGCN stereoseq_liver Stereo-seq 0.226278
183 spaGCN stereoseq_mouse_embryo Stereo-seq 0.406477
184 spaGCN visium_breast_cancer_SEDR Visium 0.529002
185 spaGCN visium_chicken_heart Visium 0.4172525
186 spaGCN visium_hd_cancer_colon Visium HD 0.3822085
187 spaGCN xenium-mouse-brain-SergioSalas Xenium 0.163136
188 spatialGE libd_dlpfc Visium 0.21794449999999999
189 Stardust abc_atlas_wmb_thalamus MERFISH 0.315232
190 Stardust libd_dlpfc Visium 0.26698
191 Stardust osmfish_Ssp osmFISH 0.072588
192 Stardust stereoseq_liver Stereo-seq 0.1446755
193 Stardust visium_breast_cancer_SEDR Visium 0.442789
194 Stardust visium_chicken_heart Visium 0.25796549999999996
157 changes: 0 additions & 157 deletions figure1-ABE/figure1-ABE.ipynb → figure1-ABE/figure1-AB.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -1037,163 +1037,6 @@
"\n",
"ggsave(\"Figure1B.pdf\", figB, height = 8, width = 13, units = \"in\", dpi=600, device = \"pdf\") "
]
},
{
"cell_type": "markdown",
"id": "5adbc80c-f831-4053-b4ac-f5449f2dd962",
"metadata": {},
"source": [
"## Figure E"
]
},
{
"cell_type": "markdown",
"id": "b3631142-1d8c-4dca-a242-e30c08fdfe97",
"metadata": {},
"source": [
"### preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 133,
"id": "4a5dd9ff-a1b8-4d1e-8bdd-c06293de6eea",
"metadata": {},
"outputs": [],
"source": [
"## Load SpaceHack results\n",
"resDT <- read.table(\"./data/SpaceHack_ARI_libd.tsv\", sep = \"\\t\", header = TRUE, stringsAsFactors = FALSE)\n",
"resDT <- resDT %>%\n",
" group_by(Method) %>%\n",
" mutate(ConfigColor = case_when(\n",
" Config == \"config_default\" ~ \"default\", # If Config is 'config_default', assign 'default'\n",
" TRUE ~ paste0(\"config\", dense_rank(factor(Config))) # For other configs, assign 'config1', 'config2', etc.\n",
" ))\n",
"## Only keep methods that have more than 1 configuration\n",
"data <- resDT %>%\n",
" group_by(Method) %>%\n",
" filter(n_distinct(Config) > 1) %>%\n",
" ungroup()\n",
"data$metric_value <- data$ARI\n",
"data$dataset <- data$ConfigColor\n",
"data$method <- data$Method\n",
"data <- data %>% \n",
" as.data.table() %>%\n",
" filter(!is.na(metric_value) & !(metric_value == 'NA'))\n",
"data$metric_value <- as.numeric(data$metric_value)\n",
"# Calculate the mean of 'metric_value' by 'Sample' and order the result by mean in descending order\n",
"dataset_mean <- data[,.(mean = mean(metric_value)), by = dataset][order(-mean),]\n",
"data_mean <- data[, .(mean = mean(metric_value)), by = method][order(-mean), ]\n",
"method_order <- c(1:nrow(data_mean)) %>% set_names(data_mean$method)\n",
"data_region <- data[, .(max = max(metric_value), min = min(metric_value)), by = .(method, dataset)\n",
" ][, x_pos := method_order[method]][]"
]
},
{
"cell_type": "code",
"execution_count": 134,
"id": "c6b4e8f6-94c1-4f7d-9607-60b6e268649f",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[1m\u001b[22m`summarise()` has grouped output by 'method'. You can override using the\n",
"`.groups` argument.\n",
"Warning message:\n",
"“\u001b[1m\u001b[22m`unnest()` has a new interface. See `?unnest` for details.\n",
"\u001b[36mℹ\u001b[39m Try `df %>% unnest(c(x, y))`, with `mutate()` if needed.”\n"
]
}
],
"source": [
"### settings for violin plots\n",
"bw <- 0.05\n",
"\n",
"wes_dic <- wes_palette[1:length(unique(data$ConfigColor))] %>% set_names(\n",
" c(\"default\", \"config1\", \"config2\", \"config3\", \"config4\", \"config5\")\n",
")\n",
"\n",
"densities <-\n",
" data %>%\n",
" group_by(method, dataset) %>%\n",
" summarise(density = list(density(metric_value, bw = bw, from = 0, to = 1, n = 100))) %>%\n",
" mutate(x = map(density, \"x\"), y = map(density, \"y\")) %>%\n",
" unnest(x, y) %>%\n",
" ungroup()\n",
"\n",
"densities_stacked <-\n",
" densities %>%\n",
" group_by(method, x) %>%\n",
" mutate(dataset = factor(dataset, dataset_mean$dataset)) %>% # set order of method performance\n",
" arrange(dataset) %>%\n",
" mutate(norm = sum(y), y = y * y, y = y / sum(y) * norm, y = ifelse(is.na(y), 0, y)) %>% # normalise between 0 and 1\n",
" mutate(ymax = cumsum(y), ymin = lag(ymax, default = 0)) %>%\n",
" ungroup() %>%\n",
" group_by(method) %>%\n",
" mutate(ymin = ymin / max(ymax), ymax = ymax / max(ymax)) %>% # normalise so that the maximal density is 1\n",
" ungroup()\n",
"\n",
"densities_violin <-\n",
" densities_stacked %>%\n",
" group_by(method, x) %>%\n",
" mutate(ymax_violin = ymax - max(ymax)/2, ymin_violin = ymin - max(ymax)/2) %>%\n",
" ungroup()\n",
"\n",
"densities_violin$method <- factor(densities_violin$method, levels = data_mean$method)\n",
"densities_violin$dataset <- factor(densities_violin$dataset, \n",
" levels = names(wes_dic))\n",
"data_mean$method <- factor(data_mean$method, levels = data_mean$method)"
]
},
{
"cell_type": "markdown",
"id": "741d4e7a-c091-4615-9805-385f94c80053",
"metadata": {},
"source": [
"### plot"
]
},
{
"cell_type": "code",
"execution_count": 135,
"id": "713f4a82-7b00-4d9f-8b9d-61db02080aef",
"metadata": {},
"outputs": [],
"source": [
"figE <-\n",
" ggplot(densities_violin) +\n",
" geom_ribbon(\n",
" aes(\n",
" x,\n",
" ymin = ymin_violin + as.numeric(method),\n",
" ymax = ymax_violin + as.numeric(method),\n",
" fill = dataset,\n",
" group = paste0(method, dataset),\n",
" ), position = \"identity\"\n",
" ) +\n",
" scale_fill_manual(values=wes_dic) +\n",
" geom_point(aes(y = method, x = mean), data = data_mean, size = 13, shape = 45, color = \"black\") +\n",
" scale_x_continuous(\"Adjusted Rand Index (ARI)\", limits = c(0, 1), expand = c(0, 0)) +\n",
" scale_y_discrete(\"\", breaks = factor(data_mean$method), labels = factor(data_mean$method)) + # Use scale_y_discrete() instead of scale_y_continuous()\n",
" coord_flip() +\n",
" labs(x = \"ARI\", y = \"\", title = \"ARI for methods with different configurations\", fill = \"Configuration\") +\n",
" theme_pub() +\n",
" theme(axis.text.x = element_text(angle = 45, hjust = 1), \n",
" plot.title = element_text(hjust = 0.5, size = 14, face = \"plain\"), # Centere\n",
" legend.position = \"right\", legend.justification = \"left\")\n",
"ggsave(\"./Figure1E.pdf\", width = 8, height = 5,\n",
" units = \"in\", dpi = 600, figE)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f319d967-df91-4ec5-a68a-ce2bb0cb1f04",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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