Explore Mouse Gastrulation Data
Lindsay Hayes
14 September 2021
Summary
- Embryo Development – https://www.pnas.org/content/105/34/12331
- Publication – https://pubmed.gov/30787436
- Shiny – https://marionilab.cruk.cam.ac.uk/MouseGastrulation2018
- Dataset – https://bioconductor.org/packages/MouseGastrulationData
Load packages
#BiocManager::install( "MouseGastrulationData" )
#library( "MouseGastrulationData" )
library( "tidyverse" )System has not been booted with systemd as init system (PID 1). Can't operate.
Failed to create bus connection: Host is down
running command 'timedatectl' had status 1Check out Cell Metadata
df_meta <- read_tsv( "/home/idies/workspace/c_moor_data/gastrulation/gastrulation_meta.tsv" )
── Column specification ─────────────────────────────────────────────
cols(
cell = col_character(),
barcode = col_character(),
sample = col_double(),
pool = col_double(),
stage = col_character(),
sequencing.batch = col_double(),
theiler = col_character(),
doub.density = col_double(),
doublet = col_logical(),
cluster = col_double(),
cluster.sub = col_double(),
cluster.stage = col_double(),
cluster.theiler = col_double(),
stripped = col_logical(),
celltype = col_character(),
colour = col_character(),
sizeFactor = col_double()
)df_metavariable we are going to explore: sample, stage, celltype
Lets explore the metadata
Number of cells of each celltype
table( df_meta$celltype )
Allantois Anterior Primitive Streak
1820 1158
Blood progenitors 1 Blood progenitors 2
745 2587
Cardiomyocytes Caudal epiblast
1206 2445
Caudal Mesoderm Caudal neurectoderm
1075 948
Def. endoderm Endothelium
1108 1084
Epiblast Erythroid1
14619 2929
Erythroid2 Erythroid3
1106 2697
ExE ectoderm ExE endoderm
11758 9339
ExE mesoderm Forebrain/Midbrain/Hindbrain
2571 4854
Gut Haematoendothelial progenitors
1940 2733
Intermediate mesoderm Mesenchyme
3551 4979
Mixed mesoderm Nascent mesoderm
1994 5028
Neural crest NMP
622 2041
Notochord Paraxial mesoderm
464 3811
Parietal endoderm PGC
284 392
Pharyngeal mesoderm Primitive Streak
3086 7265
Rostral neurectoderm Somitic mesoderm
5392 2079
Spinal cord Surface ectoderm
1796 3523
Visceral endoderm
1283 par(mar=c(5,10,1,1))
barplot(table( df_meta$celltype ), las = 1, horiz=T, cex.names=0.5, main = "Cells per Celltype", xlab="number of cells")
Number of cells of each stage
table( df_meta$stage )
E6.5 E6.75 E7.0
3697 2169 16571
E7.25 E7.5 E7.75
15294 12876 17720
E8.0 E8.25 E8.5
22059 18642 20978
mixed_gastrulation
9325 #barplot(...) # plot the tableNumber of cells from each sample
```r
#table( ... ) # make a table
#barplot( ... ) # plot the table
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### How many cells or samples are in each stage?
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBIb3cgbWFueSBjZWxscyBjb21lIGZyb20gc2FtcGxlcyBhdCBlYWNoIHN0YWdlP1xudGFibGUoZGZfbWV0YSRzYW1wbGUsIGRmX21ldGEkc3RhZ2UpXG5gYGAifQ== -->
```r
# How many cells come from samples at each stage?
table(df_meta$sample, df_meta$stage)
E6.5 E6.75 E7.0 E7.25 E7.5 E7.75 E8.0 E8.25 E8.5
1 360 0 0 0 0 0 0 0 0
2 0 0 0 0 356 0 0 0 0
3 0 0 0 0 458 0 0 0 0
4 0 0 0 0 276 0 0 0 0
5 1207 0 0 0 0 0 0 0 0
6 0 0 0 0 2798 0 0 0 0
7 0 2169 0 0 0 0 0 0 0
8 0 0 0 0 0 3254 0 0 0
9 0 0 0 0 0 3093 0 0 0
10 0 0 2359 0 0 0 0 0 0
12 0 0 0 0 0 5305 0 0 0
13 0 0 0 0 0 6068 0 0 0
14 0 0 1311 0 0 0 0 0 0
15 0 0 1620 0 0 0 0 0 0
16 0 0 0 0 0 0 6230 0 0
17 0 0 0 0 0 0 0 0 4483
18 2130 0 0 0 0 0 0 0 0
19 0 0 0 0 6996 0 0 0 0
20 0 0 0 0 1992 0 0 0 0
21 0 0 0 0 0 0 0 0 0
22 0 0 0 0 0 0 0 0 0
23 0 0 0 1429 0 0 0 0 0
24 0 0 0 0 0 0 0 6707 0
25 0 0 0 0 0 0 0 7289 0
26 0 0 0 6649 0 0 0 0 0
27 0 0 0 7216 0 0 0 0 0
28 0 0 0 0 0 0 0 4646 0
29 0 0 0 0 0 0 0 0 7569
30 0 0 3785 0 0 0 0 0 0
31 0 0 3778 0 0 0 0 0 0
32 0 0 3718 0 0 0 0 0 0
33 0 0 0 0 0 0 5443 0 0
34 0 0 0 0 0 0 5314 0 0
35 0 0 0 0 0 0 5072 0 0
36 0 0 0 0 0 0 0 0 4915
37 0 0 0 0 0 0 0 0 4011
mixed_gastrulation
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 4651
22 4674
23 0
24 0
25 0
26 0
27 0
28 0
29 0
30 0
31 0
32 0
33 0
34 0
35 0
36 0
37 0# How many samples and cells are there in each stage?
df_meta %>% group_by(stage) %>% summarise(samples = length(unique(sample)), cells = length(sample))
# How many cells from each samples are there?
par(mar=c(5,10,1,1))
barplot(table( df_meta$sample ), las = 1, horiz=T, cex.names=1, main = "Cells per sample", xlab="number of cells")
# Why might there be a difference in the number of cells from each sample?
# How many cells come from samples at each stage?
table(df_meta$sample, df_meta$stage)
E6.5 E6.75 E7.0 E7.25 E7.5 E7.75 E8.0 E8.25 E8.5
1 360 0 0 0 0 0 0 0 0
2 0 0 0 0 356 0 0 0 0
3 0 0 0 0 458 0 0 0 0
4 0 0 0 0 276 0 0 0 0
5 1207 0 0 0 0 0 0 0 0
6 0 0 0 0 2798 0 0 0 0
7 0 2169 0 0 0 0 0 0 0
8 0 0 0 0 0 3254 0 0 0
9 0 0 0 0 0 3093 0 0 0
10 0 0 2359 0 0 0 0 0 0
12 0 0 0 0 0 5305 0 0 0
13 0 0 0 0 0 6068 0 0 0
14 0 0 1311 0 0 0 0 0 0
15 0 0 1620 0 0 0 0 0 0
16 0 0 0 0 0 0 6230 0 0
17 0 0 0 0 0 0 0 0 4483
18 2130 0 0 0 0 0 0 0 0
19 0 0 0 0 6996 0 0 0 0
20 0 0 0 0 1992 0 0 0 0
21 0 0 0 0 0 0 0 0 0
22 0 0 0 0 0 0 0 0 0
23 0 0 0 1429 0 0 0 0 0
24 0 0 0 0 0 0 0 6707 0
25 0 0 0 0 0 0 0 7289 0
26 0 0 0 6649 0 0 0 0 0
27 0 0 0 7216 0 0 0 0 0
28 0 0 0 0 0 0 0 4646 0
29 0 0 0 0 0 0 0 0 7569
30 0 0 3785 0 0 0 0 0 0
31 0 0 3778 0 0 0 0 0 0
32 0 0 3718 0 0 0 0 0 0
33 0 0 0 0 0 0 5443 0 0
34 0 0 0 0 0 0 5314 0 0
35 0 0 0 0 0 0 5072 0 0
36 0 0 0 0 0 0 0 0 4915
37 0 0 0 0 0 0 0 0 4011
mixed_gastrulation
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 4651
22 4674
23 0
24 0
25 0
26 0
27 0
28 0
29 0
30 0
31 0
32 0
33 0
34 0
35 0
36 0
37 0barplot(table(df_meta$sample, df_meta$stage), main = "cells per sample per stage", ylab = "number of cells")
How many cells or samples are in each celltype?
# How many cells come from samples from each celltype?
table(df_meta$sample, df_meta$celltype)
Allantois Anterior Primitive Streak Blood progenitors 1
1 0 0 0
2 0 0 4
3 1 0 5
4 0 0 4
5 0 0 0
6 0 19 39
7 0 13 0
8 10 1 74
9 20 0 28
10 0 41 0
12 108 0 29
13 100 0 31
14 0 37 9
15 0 49 9
16 161 0 34
17 101 0 14
18 0 0 0
19 9 47 118
20 2 8 31
21 4 45 61
22 4 51 61
23 0 37 0
24 185 1 13
25 216 0 10
26 0 158 32
27 0 201 29
28 125 0 11
Blood progenitors 2 Cardiomyocytes Caudal epiblast
1 0 0 0
2 19 0 19
3 11 0 11
4 2 0 2
5 0 0 0
6 24 0 48
7 0 0 0
8 196 0 103
9 305 1 97
10 0 0 0
12 224 51 213
13 215 50 209
14 7 0 1
15 17 0 0
16 219 76 159
17 30 109 0
18 0 0 0
19 171 0 263
20 41 0 75
21 64 0 103
22 63 0 90
23 0 0 0
24 40 183 40
25 56 182 34
26 0 0 3
27 2 0 5
28 60 99 109
Caudal Mesoderm Caudal neurectoderm Def. endoderm Endothelium
1 0 0 0 0
2 3 4 0 0
3 1 0 8 0
4 0 0 7 0
5 0 0 0 0
6 5 8 64 0
7 0 0 2 0
8 18 36 25 0
9 39 39 8 4
10 0 0 3 0
12 85 96 28 30
13 91 117 26 51
14 0 0 11 0
15 0 0 19 0
16 73 57 9 92
17 8 0 20 186
18 0 0 0 0
19 37 50 125 1
20 6 9 31 0
21 4 19 98 0
22 8 21 78 0
23 0 0 10 0
24 61 20 23 120
25 77 24 23 131
26 0 0 150 0
27 1 0 187 0
28 71 47 16 107
Epiblast Erythroid1 Erythroid2 Erythroid3 ExE ectoderm
1 186 0 0 0 58
2 3 1 0 0 52
3 26 0 0 0 50
4 26 0 0 0 55
5 624 0 0 0 253
6 228 0 0 0 758
7 856 0 0 0 403
8 34 4 0 0 843
9 0 62 0 0 650
10 752 0 0 0 417
12 27 250 11 0 427
13 37 266 14 0 403
14 398 0 0 0 246
15 522 0 0 0 268
16 0 784 167 6 188
17 0 8 14 491 0
18 1466 0 0 0 322
19 486 1 0 0 936
20 155 0 0 0 270
21 613 0 0 0 649
22 629 0 0 0 722
23 490 0 1 1 164
24 0 251 254 81 82
25 0 250 281 84 85
26 1890 0 0 0 687
27 1775 0 0 0 780
28 0 227 70 7 122
ExE endoderm ExE mesoderm Forebrain/Midbrain/Hindbrain Gut
1 35 0 0 0
2 43 11 0 7
3 85 5 0 6
4 59 0 0 2
5 65 0 0 0
6 328 23 0 41
7 197 0 0 0
8 537 56 0 44
9 536 49 2 56
10 254 0 0 0
12 360 108 56 123
13 571 103 108 137
14 103 1 0 1
15 131 1 0 1
16 578 89 116 108
17 39 133 585 169
18 26 0 0 0
19 919 77 0 79
20 206 25 0 29
21 219 40 0 36
22 195 37 0 45
23 101 2 0 0
24 80 254 830 131
25 129 246 882 140
26 572 32 0 1
27 639 40 0 4
28 390 151 287 113
Haematoendothelial progenitors Intermediate mesoderm Mesenchyme
1 0 0 0
2 7 17 23
3 5 7 36
4 8 0 19
5 0 0 0
6 49 17 140
7 3 0 0
8 74 85 179
9 63 111 166
10 6 0 1
12 127 202 280
13 122 229 297
14 18 0 8
15 29 0 12
16 156 217 240
17 80 48 316
18 0 0 0
19 143 146 297
20 42 33 82
21 100 39 171
22 116 40 178
23 13 0 0
24 164 272 325
25 178 326 410
26 103 2 61
27 129 1 68
28 109 240 241
Mixed mesoderm Nascent mesoderm Neural crest NMP Notochord
1 0 1 0 0 0
2 11 7 0 0 2
3 37 21 0 0 2
4 15 12 0 0 2
5 0 0 0 0 0
6 127 120 0 0 17
7 20 138 0 0 0
8 60 32 0 0 38
9 5 1 0 0 52
10 9 161 0 0 0
12 49 24 0 2 27
13 75 44 0 10 23
14 20 107 0 0 0
15 36 129 0 0 0
16 1 0 0 58 18
17 0 0 87 192 0
18 0 3 0 0 0
19 305 401 0 0 39
20 85 84 0 0 7
21 212 296 0 0 27
22 176 312 0 0 26
23 29 180 0 0 0
24 0 0 34 417 4
25 0 0 43 455 11
26 212 869 0 0 5
27 264 895 0 0 5
28 0 0 1 173 18
Paraxial mesoderm Parietal endoderm PGC Pharyngeal mesoderm
1 0 0 0 0
2 8 0 2 10
3 1 2 3 3
4 0 2 2 1
5 0 9 0 0
6 6 24 19 16
7 0 0 3 0
8 25 8 22 30
9 57 0 26 61
10 0 11 1 0
12 153 3 15 146
13 174 2 24 182
14 0 0 1 0
15 0 2 4 0
16 176 0 13 190
17 252 2 4 151
18 0 1 0 0
19 61 3 36 66
20 14 0 6 15
21 32 2 16 40
22 22 4 20 38
23 0 1 3 0
24 464 2 13 312
25 480 3 10 336
26 0 33 24 0
27 0 52 27 0
28 280 0 11 196
Primitive Streak Rostral neurectoderm Somitic mesoderm
1 45 0 0
2 2 51 4
3 9 34 0
4 11 10 0
5 132 0 0
6 121 205 2
7 326 2 0
8 22 284 23
9 1 145 59
10 399 0 0
12 15 620 71
13 26 578 75
14 202 3 0
15 227 3 0
16 0 176 78
17 0 1 99
18 204 1 0
19 296 585 40
20 99 151 8
21 346 339 5
22 424 349 5
23 252 3 0
24 0 106 294
25 0 125 270
26 928 25 0
27 1049 35 0
28 0 153 154
Spinal cord Surface ectoderm Visceral endoderm
1 0 0 12
2 0 8 3
3 0 11 20
4 0 4 12
5 0 0 23
6 0 30 69
7 0 3 109
8 0 48 60
9 1 58 25
10 0 4 107
12 27 163 19
13 29 163 44
14 0 1 12
15 0 2 18
16 68 152 20
17 244 222 6
18 0 1 17
19 0 80 103
20 0 12 25
21 0 42 39
22 0 49 31
23 0 1 25
24 250 409 2
25 299 440 8
26 0 19 90
27 0 25 115
28 153 221 12
[ reached getOption("max.print") -- omitted 9 rows ]# How many samples andn cells are there in each celltype?
df_meta %>% group_by(celltype) %>% summarise(samples = length(unique(sample)), cells = length(sample))
t <- df_meta %>% group_by(celltype) %>% summarise(samples = length(unique(sample)), cells = length(sample))
par(mar=c(5,7,1,1))
barplot(t$samples, names=t$celltype, las = 1, horiz=T, cex.names=0.5, main = "Samples per cell type", xlab="number of samples")
# How many cells come from samples from each celltype?
barplot(table(df_meta$sample, df_meta$celltype), las = 1, horiz=T, cex.names=0.5, main = "Samples per cell type", xlab="number of cells")
What are the trends for cell types changing across stage?
# What cell types are in each stage?
table( df_meta$celltype, df_meta$stage )
E6.5 E6.75 E7.0 E7.25 E7.5 E7.75
Allantois 0 0 0 0 12 238
Anterior Primitive Streak 0 13 577 396 74 1
Blood progenitors 1 0 0 19 61 201 162
Blood progenitors 2 0 0 24 2 268 940
Cardiomyocytes 0 0 0 0 0 102
Caudal epiblast 0 0 1 8 418 622
Caudal Mesoderm 0 0 0 1 52 233
Caudal neurectoderm 0 0 0 0 71 288
Def. endoderm 0 2 61 347 235 87
Endothelium 0 0 0 0 1 85
Epiblast 2276 856 5068 4155 924 98
Erythroid1 0 0 0 0 2 582
Erythroid2 0 0 0 1 0 25
Erythroid3 0 0 0 1 0 0
ExE ectoderm 633 403 2027 1631 2121 2323
ExE endoderm 126 197 1336 1312 1640 2004
ExE mesoderm 0 0 10 74 141 316
Forebrain/Midbrain/Hindbrain 0 0 0 0 0 166
Gut 0 0 2 5 164 360
Haematoendothelial progenitors 0 3 206 245 254 386
Intermediate mesoderm 0 0 0 3 220 627
Mesenchyme 0 0 24 129 597 922
Mixed mesoderm 0 20 309 505 580 189
Nascent mesoderm 4 138 1588 1944 645 101
Neural crest 0 0 0 0 0 0
NMP 0 0 0 0 0 12
Notochord 0 0 0 10 69 140
Paraxial mesoderm 0 0 0 0 90 409
Parietal endoderm 10 0 109 86 31 13
PGC 0 3 35 54 68 87
Pharyngeal mesoderm 0 0 0 0 111 419
Primitive Streak 381 326 2957 2229 538 64
Rostral neurectoderm 1 2 13 63 1036 1627
Somitic mesoderm 0 0 0 0 54 228
Spinal cord 0 0 0 0 0 57
Surface ectoderm 1 3 36 45 145 432
Visceral endoderm 52 109 347 230 232 148
E8.0 E8.25 E8.5 mixed_gastrulation
Allantois 418 526 618 8
Anterior Primitive Streak 0 1 0 96
Blood progenitors 1 109 34 37 122
Blood progenitors 2 983 156 87 127
Cardiomyocytes 138 464 502 0
Caudal epiblast 1020 183 0 193
Caudal Mesoderm 504 209 64 12
Caudal neurectoderm 458 91 0 40
Def. endoderm 57 62 81 176
Endothelium 174 358 466 0
Epiblast 0 0 0 1242
Erythroid1 1544 728 73 0
Erythroid2 169 605 306 0
Erythroid3 6 172 2518 0
ExE ectoderm 960 289 0 1371
ExE endoderm 1165 599 546 414
ExE mesoderm 488 651 814 77
Forebrain/Midbrain/Hindbrain 153 1999 2536 0
Gut 381 384 563 81
Haematoendothelial progenitors 567 451 405 216
Intermediate mesoderm 1370 838 414 79
Mesenchyme 966 976 1016 349
Mixed mesoderm 3 0 0 388
Nascent mesoderm 0 0 0 608
Neural crest 0 78 544 0
NMP 66 1045 918 0
Notochord 157 33 2 53
Paraxial mesoderm 857 1224 1177 54
Parietal endoderm 22 5 2 6
PGC 54 34 21 36
Pharyngeal mesoderm 830 844 804 78
Primitive Streak 0 0 0 770
Rostral neurectoderm 1569 384 9 688
Somitic mesoderm 544 718 525 10
Spinal cord 208 702 829 0
Surface ectoderm 682 1070 1018 91
Visceral endoderm 59 22 14 70dim(table( df_meta$celltype, df_meta$stage ))[1] 37 10barplot(table( df_meta$celltype, df_meta$stage )[32,], main = "Primitive Streak", ylab = "number of cells")
barplot(table( df_meta$celltype, df_meta$stage )[11,], main = "Epiblast", ylab = "number of cells")
# What about the notochord?
#barplot(table( df_meta$celltype, df_meta$stage )[...,], main = "Notochord", ylab = "number of cells")
# What about the Mesenchyme?
#barplot(table( df_meta$celltype, df_meta$stage )[...,], main = "Mesenchyme", ylab = "number of cells")Put it all together and look at abundance of each cell type in each sample across stages
table( df_meta$sample ) # number of cells per sample
1 2 3 4 5 6 7 8 9 10 12 13 14
360 356 458 276 1207 2798 2169 3254 3093 2359 5305 6068 1311
15 16 17 18 19 20 21 22 23 24 25 26 27
1620 6230 4483 2130 6996 1992 4651 4674 1429 6707 7289 6649 7216
28 29 30 31 32 33 34 35 36 37
4646 7569 3785 3778 3718 5443 5314 5072 4915 4011 table( df_meta$celltype, df_meta$sample ) # number of cells per sample per celltype
1 2 3 4 5 6 7
Allantois 0 0 1 0 0 0 0
Anterior Primitive Streak 0 0 0 0 0 19 13
Blood progenitors 1 0 4 5 4 0 39 0
Blood progenitors 2 0 19 11 2 0 24 0
Cardiomyocytes 0 0 0 0 0 0 0
Caudal epiblast 0 19 11 2 0 48 0
Caudal Mesoderm 0 3 1 0 0 5 0
Caudal neurectoderm 0 4 0 0 0 8 0
Def. endoderm 0 0 8 7 0 64 2
Endothelium 0 0 0 0 0 0 0
Epiblast 186 3 26 26 624 228 856
Erythroid1 0 1 0 0 0 0 0
Erythroid2 0 0 0 0 0 0 0
Erythroid3 0 0 0 0 0 0 0
ExE ectoderm 58 52 50 55 253 758 403
ExE endoderm 35 43 85 59 65 328 197
ExE mesoderm 0 11 5 0 0 23 0
Forebrain/Midbrain/Hindbrain 0 0 0 0 0 0 0
Gut 0 7 6 2 0 41 0
Haematoendothelial progenitors 0 7 5 8 0 49 3
Intermediate mesoderm 0 17 7 0 0 17 0
Mesenchyme 0 23 36 19 0 140 0
Mixed mesoderm 0 11 37 15 0 127 20
Nascent mesoderm 1 7 21 12 0 120 138
Neural crest 0 0 0 0 0 0 0
NMP 0 0 0 0 0 0 0
Notochord 0 2 2 2 0 17 0
8 9 10 12 13 14 15
Allantois 10 20 0 108 100 0 0
Anterior Primitive Streak 1 0 41 0 0 37 49
Blood progenitors 1 74 28 0 29 31 9 9
Blood progenitors 2 196 305 0 224 215 7 17
Cardiomyocytes 0 1 0 51 50 0 0
Caudal epiblast 103 97 0 213 209 1 0
Caudal Mesoderm 18 39 0 85 91 0 0
Caudal neurectoderm 36 39 0 96 117 0 0
Def. endoderm 25 8 3 28 26 11 19
Endothelium 0 4 0 30 51 0 0
Epiblast 34 0 752 27 37 398 522
Erythroid1 4 62 0 250 266 0 0
Erythroid2 0 0 0 11 14 0 0
Erythroid3 0 0 0 0 0 0 0
ExE ectoderm 843 650 417 427 403 246 268
ExE endoderm 537 536 254 360 571 103 131
ExE mesoderm 56 49 0 108 103 1 1
Forebrain/Midbrain/Hindbrain 0 2 0 56 108 0 0
Gut 44 56 0 123 137 1 1
Haematoendothelial progenitors 74 63 6 127 122 18 29
Intermediate mesoderm 85 111 0 202 229 0 0
Mesenchyme 179 166 1 280 297 8 12
Mixed mesoderm 60 5 9 49 75 20 36
Nascent mesoderm 32 1 161 24 44 107 129
Neural crest 0 0 0 0 0 0 0
NMP 0 0 0 2 10 0 0
Notochord 38 52 0 27 23 0 0
16 17 18 19 20 21 22
Allantois 161 101 0 9 2 4 4
Anterior Primitive Streak 0 0 0 47 8 45 51
Blood progenitors 1 34 14 0 118 31 61 61
Blood progenitors 2 219 30 0 171 41 64 63
Cardiomyocytes 76 109 0 0 0 0 0
Caudal epiblast 159 0 0 263 75 103 90
Caudal Mesoderm 73 8 0 37 6 4 8
Caudal neurectoderm 57 0 0 50 9 19 21
Def. endoderm 9 20 0 125 31 98 78
Endothelium 92 186 0 1 0 0 0
Epiblast 0 0 1466 486 155 613 629
Erythroid1 784 8 0 1 0 0 0
Erythroid2 167 14 0 0 0 0 0
Erythroid3 6 491 0 0 0 0 0
ExE ectoderm 188 0 322 936 270 649 722
ExE endoderm 578 39 26 919 206 219 195
ExE mesoderm 89 133 0 77 25 40 37
Forebrain/Midbrain/Hindbrain 116 585 0 0 0 0 0
Gut 108 169 0 79 29 36 45
Haematoendothelial progenitors 156 80 0 143 42 100 116
Intermediate mesoderm 217 48 0 146 33 39 40
Mesenchyme 240 316 0 297 82 171 178
Mixed mesoderm 1 0 0 305 85 212 176
Nascent mesoderm 0 0 3 401 84 296 312
Neural crest 0 87 0 0 0 0 0
NMP 58 192 0 0 0 0 0
Notochord 18 0 0 39 7 27 26
23 24 25 26 27 28 29
Allantois 0 185 216 0 0 125 322
Anterior Primitive Streak 37 1 0 158 201 0 0
Blood progenitors 1 0 13 10 32 29 11 12
Blood progenitors 2 0 40 56 0 2 60 33
Cardiomyocytes 0 183 182 0 0 99 241
Caudal epiblast 0 40 34 3 5 109 0
Caudal Mesoderm 0 61 77 0 1 71 21
Caudal neurectoderm 0 20 24 0 0 47 0
Def. endoderm 10 23 23 150 187 16 30
Endothelium 0 120 131 0 0 107 169
Epiblast 490 0 0 1890 1775 0 0
Erythroid1 0 251 250 0 0 227 23
Erythroid2 1 254 281 0 0 70 71
Erythroid3 1 81 84 0 0 7 550
ExE ectoderm 164 82 85 687 780 122 0
ExE endoderm 101 80 129 572 639 390 294
ExE mesoderm 2 254 246 32 40 151 332
Forebrain/Midbrain/Hindbrain 0 830 882 0 0 287 991
Gut 0 131 140 1 4 113 214
Haematoendothelial progenitors 13 164 178 103 129 109 197
Intermediate mesoderm 0 272 326 2 1 240 198
Mesenchyme 0 325 410 61 68 241 356
Mixed mesoderm 29 0 0 212 264 0 0
Nascent mesoderm 180 0 0 869 895 0 0
Neural crest 0 34 43 0 0 1 241
NMP 0 417 455 0 0 173 342
Notochord 0 4 11 5 5 18 0
30 31 32 33 34 35 36
Allantois 0 0 0 86 95 76 122
Anterior Primitive Streak 161 140 149 0 0 0 0
Blood progenitors 1 0 1 0 30 23 22 6
Blood progenitors 2 0 0 0 277 236 251 16
Cardiomyocytes 0 0 0 30 13 19 71
Caudal epiblast 0 0 0 259 308 294 0
Caudal Mesoderm 0 0 0 146 138 147 25
Caudal neurectoderm 0 0 0 138 134 129 0
Def. endoderm 10 12 6 17 15 16 17
Endothelium 0 0 0 34 23 25 77
Epiblast 1133 1143 1120 0 0 0 0
Erythroid1 0 0 0 282 245 233 18
Erythroid2 0 0 0 0 1 1 130
Erythroid3 0 0 0 0 0 0 805
ExE ectoderm 361 372 363 264 260 248 0
ExE endoderm 299 259 290 235 211 141 67
ExE mesoderm 2 1 5 135 138 126 174
Forebrain/Midbrain/Hindbrain 0 0 0 21 13 3 497
Gut 0 0 0 91 94 88 79
Haematoendothelial progenitors 56 53 44 135 139 137 71
Intermediate mesoderm 0 0 0 403 384 366 80
Mesenchyme 0 2 1 265 234 227 200
Mixed mesoderm 91 90 63 0 2 0 0
Nascent mesoderm 398 400 393 0 0 0 0
Neural crest 0 0 0 0 0 0 126
NMP 0 0 0 4 4 0 224
Notochord 0 0 0 35 60 44 0
37
Allantois 73
Anterior Primitive Streak 0
Blood progenitors 1 5
Blood progenitors 2 8
Cardiomyocytes 81
Caudal epiblast 0
Caudal Mesoderm 10
Caudal neurectoderm 0
Def. endoderm 14
Endothelium 34
Epiblast 0
Erythroid1 24
Erythroid2 91
Erythroid3 672
ExE ectoderm 0
ExE endoderm 146
ExE mesoderm 175
Forebrain/Midbrain/Hindbrain 463
Gut 101
Haematoendothelial progenitors 57
Intermediate mesoderm 88
Mesenchyme 144
Mixed mesoderm 0
Nascent mesoderm 0
Neural crest 90
NMP 160
Notochord 2
[ reached getOption("max.print") -- omitted 10 rows ]table( df_meta$celltype, df_meta$sample )[,5] # number of cells per celltype for sample #5 Allantois Anterior Primitive Streak
0 0
Blood progenitors 1 Blood progenitors 2
0 0
Cardiomyocytes Caudal epiblast
0 0
Caudal Mesoderm Caudal neurectoderm
0 0
Def. endoderm Endothelium
0 0
Epiblast Erythroid1
624 0
Erythroid2 Erythroid3
0 0
ExE ectoderm ExE endoderm
253 65
ExE mesoderm Forebrain/Midbrain/Hindbrain
0 0
Gut Haematoendothelial progenitors
0 0
Intermediate mesoderm Mesenchyme
0 0
Mixed mesoderm Nascent mesoderm
0 0
Neural crest NMP
0 0
Notochord Paraxial mesoderm
0 0
Parietal endoderm PGC
9 0
Pharyngeal mesoderm Primitive Streak
0 132
Rostral neurectoderm Somitic mesoderm
0 0
Spinal cord Surface ectoderm
0 0
Visceral endoderm
23 table( df_meta$celltype, df_meta$sample )[,5]/1207*100 Allantois Anterior Primitive Streak
0.0000000 0.0000000
Blood progenitors 1 Blood progenitors 2
0.0000000 0.0000000
Cardiomyocytes Caudal epiblast
0.0000000 0.0000000
Caudal Mesoderm Caudal neurectoderm
0.0000000 0.0000000
Def. endoderm Endothelium
0.0000000 0.0000000
Epiblast Erythroid1
51.6984258 0.0000000
Erythroid2 Erythroid3
0.0000000 0.0000000
ExE ectoderm ExE endoderm
20.9610605 5.3852527
ExE mesoderm Forebrain/Midbrain/Hindbrain
0.0000000 0.0000000
Gut Haematoendothelial progenitors
0.0000000 0.0000000
Intermediate mesoderm Mesenchyme
0.0000000 0.0000000
Mixed mesoderm Nascent mesoderm
0.0000000 0.0000000
Neural crest NMP
0.0000000 0.0000000
Notochord Paraxial mesoderm
0.0000000 0.0000000
Parietal endoderm PGC
0.7456504 0.0000000
Pharyngeal mesoderm Primitive Streak
0.0000000 10.9362055
Rostral neurectoderm Somitic mesoderm
0.0000000 0.0000000
Spinal cord Surface ectoderm
0.0000000 0.0000000
Visceral endoderm
1.9055510 par(mar=c(5,10,1,1))
barplot((table( df_meta$celltype, df_meta$sample )[,5]/1207*100), las = 1, horiz=T, cex.names=0.5, main= "sample #5 E6.5", xlab = "percent of cells")
# what about sample 17
#barplot((table( df_meta$celltype, df_meta$sample )[, ... ]/ ... *100), las = 1, horiz=T, cex.names=0.5, main= "sample #17 E8.5")Document software
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 8
Matrix products: default
BLAS: /home/idies/R/lib64/R/lib/libRblas.so
LAPACK: /home/idies/R/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods
[7] base
other attached packages:
[1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.5 purrr_0.3.4
[5] readr_1.4.0 tidyr_1.1.3 tibble_3.1.1 ggplot2_3.3.3
[9] tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] tidyselect_1.1.1 xfun_0.22 haven_2.4.1
[4] colorspace_2.0-0 vctrs_0.3.8 generics_0.1.0
[7] htmltools_0.5.1.1 yaml_2.2.1 utf8_1.2.1
[10] rlang_0.4.10 pillar_1.6.0 glue_1.4.2
[13] withr_2.4.2 DBI_1.1.1 dbplyr_2.1.1
[16] modelr_0.1.8 readxl_1.3.1 lifecycle_1.0.0
[19] munsell_0.5.0 gtable_0.3.0 cellranger_1.1.0
[22] rvest_1.0.0 evaluate_0.14 knitr_1.33
[25] fansi_0.4.2 broom_0.7.6 Rcpp_1.0.6
[28] scales_1.1.1 backports_1.2.1 jsonlite_1.7.2
[31] fs_1.5.0 hms_1.0.0 digest_0.6.27
[34] stringi_1.5.3 grid_4.0.3 cli_2.5.0
[37] tools_4.0.3 magrittr_2.0.1 crayon_1.4.1
[40] pkgconfig_2.0.3 ellipsis_0.3.2 xml2_1.3.2
[43] reprex_2.0.0 lubridate_1.7.10 assertthat_0.2.1
[46] rmarkdown_2.7 httr_1.4.2 rstudioapi_0.13
[49] R6_2.5.0 compiler_4.0.3 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