tTEscanR Visualization Module
1. Overview
tTEscanR includes a dedicated visualization module that provides multiple functions for generating plots based on the output tables produced at each step of the analysis. The primary goal of this module is to facilitate a more intuitive and streamlined interpretation of results, allowing researchers to easily explore and understand their data. Additionally, it helps summarize complex findings in a visually accessible manner, enhancing the overall clarity and impact of the analysis.
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("tTEscanR")To illustrate the usage of each plotting function and demonstrate the flexibility provided by various parameters, we will first run tTEscanR. In this tutorial, we will analyze a single-cell fetal human atlas described in (Cao et al. 2020) and (Domcke et al. 2020), and previously examined by (Gao et al. 2022). A subset of this dataset is included as a default dataset in tTEscanR and can be directly loaded to perform the analysis. A step-by-step explanation of this pipeline is available in the tTEscanR User Guide vignette.
tTEobject <- runPipeline(
mRNA_data = default_tTEscanR_mRNA_data,
tRNA_data = default_tTEscanR_tRNA_data,
meta_data = default_tTEscanR_metadata,
species = "hg38", batch = "tissue", sample_id = "conditions",
runDESeq = FALSE
)
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The visualization functions in tTEscanR are highly customizable and can be applied to any properly formatted dataset. Depending on the plot type, specific data requirements must be met, which may involve prior data transformation or restructuring.
This guide provides an overview of the available visualization options in tTEscanR, and illustrates key parameter settings and their effects through practical examples.
The visualization functions in tTEscanR are not limited to outputs generated within the package’s pipeline. Users can apply them to external dataset, provided that the required data formatting and structure are met.
| Function | Purpose |
|---|---|
plotProportion() |
Shows features’ frequencies differences within and |
| between conditions | |
plotDistribution() |
Displays features’ distributions across |
| conditions | |
plotTargetComparison() |
Variation of plotDistribution() to |
| compare a target feature against the mean across conditions | |
plotCorrelation() |
Features correlation |
plotPermutation() |
Compares the baseline codon exonic background |
| against the current codon usage | |
plotTEscore() |
Represents the tTE scores obtained from |
computeTheoreticalTE() |
2.1. Data transformation
There are helper functions in
tTEscanR designed to properly transform data for
downstream analysis and visualization. the data. One such function is
transformFormat(), which converts a count
matrix, with features as rows and conditions as columns, into a
long-format table. In this format, each row-column
combination from the original matrix becomes an individual row in the
output.
The function also provides an option to normalize
the input data. Parameters such as rownames_to_column,
names_to and values_to allow users to
customize the names of the columns in the resulting long-format
table.
The normalization performed by transformFormat() is done by converting the raw counts into relative abundances (i.e. dividing each column by its column-wise sum).
head(SummarizedExperiment::assay(tTEobject, "CodonUsage"))
#> Adrenal-Adrenocortical cells Adrenal-Chromaffin cells
#> AAA 6166175 886228
#> CAA 3066594 448080
#> GAA 8300641 910614
#> TAA 112872 11098
#> ACA 3187448 466826
#> CCA 3059730 390216
#> Adrenal-CSH1_CSH2 positive cells Adrenal-Erythroblasts
#> AAA 3344 44304
#> CAA 1402 22537
#> GAA 3492 59136
#> TAA 25 841
#> ACA 1475 22276
#> CCA 1334 23884
#> Adrenal-Lymphoid cells Adrenal-Megakaryocytes Adrenal-Myeloid cells
#> AAA 11038 42300 36893
#> CAA 5101 19093 17955
#> GAA 12985 64845 47507
#> TAA 175 1052 626
#> ACA 6900 15392 17871
#> CCA 6195 16781 20070
#> Adrenal-Schwann cells Adrenal-SLC26A4_PAEP positive cells
#> AAA 109220 1430
#> CAA 52286 705
#> GAA 79659 2063
#> TAA 366 33
#> ACA 62369 721
#> CCA 36385 707
#> Adrenal-Stromal cells Adrenal-Sympathoblasts
#> AAA 225932 157616
#> CAA 115227 78964
#> GAA 299018 155340
#> TAA 3813 1499
#> ACA 105923 82729
#> CCA 107250 64360
#> Adrenal-Vascular endothelial cells Kidney-Erythroblasts
#> AAA 1275567 9538
#> CAA 501192 4876
#> GAA 1215295 13806
#> TAA 4343 160
#> ACA 527553 4295
#> CCA 454083 6158
#> Kidney-Lymphoid cells Kidney-Megakaryocytes Kidney-Mesangial cells
#> AAA 34199 7478 2504779
#> CAA 14132 3140 1261824
#> GAA 37766 9261 3370219
#> TAA 687 103 54056
#> ACA 19593 3120 1242898
#> CCA 19358 3014 1215544
#> Kidney-Metanephric cells Kidney-Myeloid cells Kidney-Stromal cells
#> AAA 7521573 139053 55697
#> CAA 3670040 61444 27835
#> GAA 8056900 164372 71086
#> TAA 105181 3090 1204
#> ACA 4058174 68573 28082
#> CCA 3476146 80878 28061
#> Kidney-Ureteric bud cells Kidney-Vascular endothelial cells
#> AAA 527428 664824
#> CAA 250736 275177
#> GAA 659729 671394
#> TAA 10527 4116
#> ACA 319696 290949
#> CCA 307435 258525
#> Liver-Erythroblasts Liver-Hematopoietic stem cells Liver-Hepatoblasts
#> AAA 1037985 29892 2126351
#> CAA 524436 15073 905114
#> GAA 1434153 38646 2343218
#> TAA 13997 430 29070
#> ACA 487704 14841 1102880
#> CCA 537230 15920 1076046
#> Liver-Lymphoid cells Liver-Megakaryocytes Liver-Mesothelial cells
#> AAA 28317 66910 5774
#> CAA 13309 31489 2913
#> GAA 34481 102769 7454
#> TAA 238 1122 117
#> ACA 14895 26452 3015
#> CCA 14527 29211 2901
#> Liver-Myeloid cells Liver-Stellate cells Liver-Vascular endothelial cells
#> AAA 59673 38651 256809
#> CAA 27005 20439 108669
#> GAA 77027 53415 269968
#> TAA 751 508 993
#> ACA 29575 18573 109748
#> CCA 33653 18590 101172
#> Cerebellum-Astrocytes Cerebellum-Granule neurons
#> AAA 63739371 97625429
#> CAA 31160666 47400759
#> GAA 47789505 69668438
#> TAA 173957 257708
#> ACA 35966713 56256886
#> CCA 21105159 31257078
#> Cerebellum-Inhibitory interneurons Cerebellum-Microglia
#> AAA 49465234 297019
#> CAA 23923518 140519
#> GAA 35011710 285373
#> TAA 117706 2084
#> ACA 28389418 152719
#> CCA 15911559 121642
#> Cerebellum-Oligodendrocytes Cerebellum-Purkinje neurons
#> AAA 3003825 67460691
#> CAA 1470164 32637390
#> GAA 2229290 55778231
#> TAA 6584 362118
#> ACA 1716456 38149185
#> CCA 976600 22440911
#> Cerebellum-SLC24A4_PEX5L positive cells Cerebellum-Unipolar brush cells
#> AAA 5633163 22696252
#> CAA 2692071 11052623
#> GAA 4690387 15371447
#> TAA 42222 31911
#> ACA 3228251 13119797
#> CCA 2092925 7287252
#> Cerebellum-Vascular endothelial cells Muscle-Erythroblasts
#> AAA 1308766 625
#> CAA 540715 306
#> GAA 1227159 797
#> TAA 6185 15
#> ACA 590357 319
#> CCA 479972 337
#> Muscle-Lymphatic endothelial cells Muscle-Lymphoid cells
#> AAA 1882 2414
#> CAA 1032 1051
#> GAA 2467 2773
#> TAA 37 38
#> ACA 1015 1398
#> CCA 1053 1364
#> Muscle-Megakaryocytes Muscle-Myeloid cells Muscle-Satellite cells
#> AAA 771 5797 118294
#> CAA 364 2783 58697
#> GAA 1260 7176 157236
#> TAA 18 103 2907
#> ACA 232 2788 60254
#> CCA 227 2868 55010
#> Muscle-Schwann cells Muscle-Skeletal muscle cells
#> AAA 43338 509886
#> CAA 20801 242311
#> GAA 31235 790478
#> TAA 124 13174
#> ACA 24545 188972
#> CCA 13583 172747
#> Muscle-Smooth muscle cells Muscle-Stromal cells
#> AAA 13370 445242
#> CAA 6691 220292
#> GAA 17521 398327
#> TAA 214 3208
#> ACA 6467 243093
#> CCA 6596 166050
#> Muscle-Vascular endothelial cells Cerebrum-Astrocytes
#> AAA 83586 18749795
#> CAA 35162 9116905
#> GAA 86955 13416790
#> TAA 872 38173
#> ACA 38762 10651432
#> CCA 33625 6015299
#> Cerebrum-Excitatory neurons Cerebrum-Inhibitory neurons
#> AAA 349533872 94160656
#> CAA 169638650 46000061
#> GAA 257515243 70790567
#> TAA 1105201 296421
#> ACA 202245156 53689149
#> CCA 112603689 31279430
#> Cerebrum-Limbic system neurons Cerebrum-Megakaryocytes Cerebrum-Microglia
#> AAA 17994113 6887 263870
#> CAA 8763701 3250 130367
#> GAA 13368227 7326 272598
#> TAA 55092 62 1906
#> ACA 10285897 3380 135911
#> CCA 5995050 2370 112700
#> Cerebrum-Oligodendrocytes Cerebrum-SKOR2_NPSR1 positive cells
#> AAA 4162874 3599275
#> CAA 2034080 1743538
#> GAA 3044826 2968252
#> TAA 7783 18212
#> ACA 2380833 2027494
#> CCA 1325881 1187244
#> Cerebrum-Vascular endothelial cells Pancreas-Acinar cells
#> AAA 1498256 363958
#> CAA 620067 156909
#> GAA 1424395 438365
#> TAA 6959 8878
#> ACA 671093 185052
#> CCA 551141 187057
#> Pancreas-CCL19_CCL21 positive cells Pancreas-Ductal cells Pancreas-ENS glia
#> AAA 5968 295649 187157
#> CAA 2709 83691 89394
#> GAA 7590 334266 140486
#> TAA 245 4323 906
#> ACA 3121 110839 106996
#> CCA 3299 126963 65345
#> Pancreas-ENS neurons Pancreas-Erythroblasts Pancreas-Islet endocrine cells
#> AAA 27941 29052 461602
#> CAA 13931 14045 230025
#> GAA 29722 38413 423322
#> TAA 363 600 4367
#> ACA 14003 15026 257090
#> CCA 12603 23596 191086
#> Pancreas-Lymphatic endothelial cells Pancreas-Lymphoid cells
#> AAA 4635 267075
#> CAA 2527 100799
#> GAA 6346 277923
#> TAA 113 7618
#> ACA 2638 155246
#> CCA 2714 154827
#> Pancreas-Mesothelial cells Pancreas-Myeloid cells
#> AAA 5150 43166
#> CAA 2524 18032
#> GAA 6809 50133
#> TAA 155 1396
#> ACA 2481 23813
#> CCA 2722 26757
#> Pancreas-Smooth muscle cells Pancreas-Stromal cells
#> AAA 80597 271887
#> CAA 38350 130149
#> GAA 105058 328945
#> TAA 2446 6664
#> ACA 43975 146884
#> CCA 42656 132276
#> Pancreas-Vascular endothelial cells Placenta-AFP_ALB positive cells
#> AAA 556209 2137
#> CAA 221018 887
#> GAA 541793 2200
#> TAA 4042 38
#> ACA 244386 1146
#> CCA 216252 1026
#> Placenta-Extravillous trophoblasts Placenta-IGFBP1_DKK1 positive cells
#> AAA 418572 16917
#> CAA 158537 7735
#> GAA 376697 20008
#> TAA 331 273
#> ACA 165712 9144
#> CCA 140175 8029
#> Placenta-Lymphoid cells Placenta-Megakaryocytes Placenta-Myeloid cells
#> AAA 11178 1271 6544
#> CAA 5290 613 2863
#> GAA 13276 2134 7337
#> TAA 141 23 97
#> ACA 6905 402 3185
#> CCA 6400 424 3432
#> Placenta-PAEP_MECOM positive cells Placenta-Smooth muscle cells
#> AAA 12906 76837
#> CAA 5594 31321
#> GAA 14070 76282
#> TAA 96 218
#> ACA 5934 31184
#> CCA 5488 26946
#> Placenta-Stromal cells
#> AAA 343642
#> CAA 160005
#> GAA 406782
#> TAA 2612
#> ACA 152375
#> CCA 145711
#> Placenta-Syncytiotrophoblasts and villous cytotrophoblasts
#> AAA 613968
#> CAA 274864
#> GAA 676102
#> TAA 3983
#> ACA 279587
#> CCA 255352
#> Placenta-Trophoblast giant cells Placenta-Vascular endothelial cells
#> AAA 34321 24423
#> CAA 14531 12141
#> GAA 33752 29453
#> TAA 187 262
#> ACA 15861 12661
#> CCA 13457 11590
#> Lung-Bronchiolar and alveolar epithelial cells
#> AAA 3222845
#> CAA 1462215
#> GAA 3865594
#> TAA 61533
#> ACA 1603224
#> CCA 1573921
#> Lung-Ciliated epithelial cells Lung-CSH1_CSH2 positive cells
#> AAA 72744 495
#> CAA 34409 212
#> GAA 100343 562
#> TAA 1431 6
#> ACA 33289 221
#> CCA 33931 219
#> Lung-Lymphatic endothelial cells Lung-Lymphoid cells Lung-Megakaryocytes
#> AAA 102923 67449 13995
#> CAA 54729 31391 6369
#> GAA 138140 78435 22082
#> TAA 2459 902 306
#> ACA 54711 40496 4850
#> CCA 51471 36966 5408
#> Lung-Mesothelial cells Lung-Myeloid cells Lung-Neuroendocrine cells
#> AAA 23260 53276 119725
#> CAA 11985 25753 54782
#> GAA 30139 65154 103953
#> TAA 486 972 1146
#> ACA 11866 25626 70777
#> CCA 11664 27738 42161
#> Lung-Squamous epithelial cells Lung-Stromal cells
#> AAA 5486 2771166
#> CAA 2435 1450024
#> GAA 7713 3943915
#> TAA 152 43319
#> ACA 3533 1165550
#> CCA 3666 1244298
#> Lung-Vascular endothelial cells Lung-Visceral neurons
#> AAA 550874 26229
#> CAA 237725 12837
#> GAA 593870 22759
#> TAA 5079 188
#> ACA 246349 14404
#> CCA 225850 9641
#> Spleen-AFP_ALB positive cells Spleen-Erythroblasts Spleen-Lymphoid cells
#> AAA 3217 9406 35713
#> CAA 1407 4761 16695
#> GAA 3400 12379 44104
#> TAA 60 169 518
#> ACA 1682 4695 19686
#> CCA 1487 4893 19567
#> Spleen-Megakaryocytes Spleen-Mesothelial cells Spleen-Myeloid cells
#> AAA 3736 5855 24754
#> CAA 1666 3086 11124
#> GAA 5986 8088 31114
#> TAA 63 149 340
#> ACA 1252 3326 11971
#> CCA 1525 3266 13592
#> Spleen-STC2_TLX1 positive cells Spleen-Stromal cells
#> AAA 124738 45875
#> CAA 55682 24177
#> GAA 138082 62590
#> TAA 1733 699
#> ACA 61544 21531
#> CCA 58700 22483
#> Spleen-Vascular endothelial cells Eye-Amacrine cells Eye-Astrocytes
#> AAA 183729 642837 4663
#> CAA 76415 320262 2332
#> GAA 186958 502347 5748
#> TAA 876 2209 52
#> ACA 78269 359421 2268
#> CCA 69262 227118 2316
#> Eye-Bipolar cells Eye-Corneal and conjunctival epithelial cells
#> AAA 37504 2593
#> CAA 17406 1239
#> GAA 38751 3439
#> TAA 302 88
#> ACA 18405 1295
#> CCA 15490 1439
#> Eye-Ganglion cells Eye-Horizontal cells Eye-Lens fibre cells Eye-Microglia
#> AAA 1090893 75604 11333 2204
#> CAA 528290 39817 8562 1077
#> GAA 899746 80200 14304 2763
#> TAA 5877 869 656 29
#> ACA 611991 40194 6790 995
#> CCA 372863 33719 4540 1055
#> Eye-PDE11A_FAM19A2 positive cells Eye-Photoreceptor cells
#> AAA 52261 482211
#> CAA 26428 237095
#> GAA 43819 455306
#> TAA 265 4317
#> ACA 28925 275268
#> CCA 19305 184170
#> Eye-Retinal pigment cells Eye-Retinal progenitors and Muller glia
#> AAA 134046 406188
#> CAA 41481 192007
#> GAA 138783 472685
#> TAA 323 4289
#> ACA 61895 191632
#> CCA 52159 178683
#> Eye-Skeletal muscle cells Eye-Smooth muscle cells Eye-Stromal cells
#> AAA 7716 5462 23240
#> CAA 3844 1964 12228
#> GAA 9522 5882 28577
#> TAA 123 22 433
#> ACA 3526 2481 11422
#> CCA 2712 2103 10866
#> Eye-Vascular endothelial cells Stomach-Ciliated epithelial cells
#> AAA 12054 6845
#> CAA 5038 3252
#> GAA 12099 9265
#> TAA 81 145
#> ACA 5379 3297
#> CCA 4546 3415
#> Stomach-ENS glia Stomach-ENS neurons Stomach-Erythroblasts
#> AAA 11798 3561 1710
#> CAA 5694 1866 885
#> GAA 9049 3489 2172
#> TAA 37 48 21
#> ACA 6679 1859 934
#> CCA 3997 1680 1096
#> Stomach-Goblet cells Stomach-Lymphatic endothelial cells
#> AAA 83460 1383
#> CAA 37402 688
#> GAA 105566 1860
#> TAA 1846 22
#> ACA 45214 664
#> CCA 48877 712
#> Stomach-Lymphoid cells Stomach-Mesothelial cells
#> AAA 10400 3391
#> CAA 4188 1587
#> GAA 11137 4374
#> TAA 208 84
#> ACA 6128 1709
#> CCA 5979 1706
#> Stomach-MUC13_DMBT1 positive cells Stomach-Myeloid cells
#> AAA 18058 2125
#> CAA 9193 975
#> GAA 22771 2539
#> TAA 402 51
#> ACA 10487 1095
#> CCA 10354 1160
#> Stomach-Neuroendocrine cells Stomach-Parietal and chief cells
#> AAA 9372 86730
#> CAA 4657 28953
#> GAA 9332 95954
#> TAA 87 1618
#> ACA 5221 37644
#> CCA 4088 39536
#> Stomach-PDE1C_ACSM3 positive cells Stomach-Squamous epithelial cells
#> AAA 2011 32042
#> CAA 783 15298
#> GAA 2413 41261
#> TAA 26 945
#> ACA 1084 17413
#> CCA 1186 17673
#> Stomach-Stromal cells Stomach-Vascular endothelial cells
#> AAA 26946 15719
#> CAA 13372 6417
#> GAA 36561 15638
#> TAA 589 108
#> ACA 13378 6943
#> CCA 13862 6044
#> Heart-Cardiomyocytes Heart-CLC_IL5RA positive cells
#> AAA 2581453 3255
#> CAA 1227461 1463
#> GAA 3850426 4354
#> TAA 52899 84
#> ACA 931815 1415
#> CCA 910526 1417
#> Heart-ELF3_AGBL2 positive cells Heart-Endocardial cells
#> AAA 5975 292037
#> CAA 2650 122232
#> GAA 8351 328205
#> TAA 125 4909
#> ACA 2316 132140
#> CCA 2331 118043
#> Heart-Epicardial fat cells Heart-Erythroblasts
#> AAA 54397 144
#> CAA 26782 44
#> GAA 69648 137
#> TAA 1129 4
#> ACA 27155 76
#> CCA 26569 88
#> Heart-Lymphatic endothelial cells Heart-Lymphoid cells Heart-Megakaryocytes
#> AAA 26998 39860 12002
#> CAA 13938 17846 5417
#> GAA 36490 45353 17179
#> TAA 598 674 300
#> ACA 13639 23783 5153
#> CCA 13206 21929 5472
#> Heart-Myeloid cells Heart-SATB2_LRRC7 positive cells Heart-Schwann cells
#> AAA 22190 32589 100318
#> CAA 10318 15521 48234
#> GAA 26344 27265 70868
#> TAA 515 170 203
#> ACA 10959 19105 57456
#> CCA 11476 11490 31765
#> Heart-Smooth muscle cells Heart-Stromal cells
#> AAA 47608 258513
#> CAA 22503 128591
#> GAA 61426 336125
#> TAA 986 5491
#> ACA 22233 127869
#> CCA 22242 127515
#> Heart-Vascular endothelial cells Heart-Visceral neurons
#> AAA 187186 28268
#> CAA 79279 13530
#> GAA 199368 30030
#> TAA 2016 331
#> ACA 85184 14410
#> CCA 76083 12359
#> Thymus-Antigen presenting cells Thymus-Stromal cells
#> AAA 9115 1263
#> CAA 4422 604
#> GAA 11108 1752
#> TAA 178 29
#> ACA 5034 703
#> CCA 4986 799
#> Thymus-Thymic epithelial cells Thymus-Thymocytes
#> AAA 4941 218827
#> CAA 2662 105918
#> GAA 6591 259913
#> TAA 64 2314
#> ACA 2652 139906
#> CCA 2431 125338
#> Thymus-Vascular endothelial cells Intestine-Chromaffin cells
#> AAA 2744 18674
#> CAA 1256 9315
#> GAA 3316 18963
#> TAA 35 213
#> ACA 1197 10950
#> CCA 1166 9087
#> Intestine-ENS glia Intestine-ENS neurons Intestine-Erythroblasts
#> AAA 286565 210365 3543
#> CAA 137380 105341 1814
#> GAA 218156 194831 5065
#> TAA 1033 2170 53
#> ACA 162294 111556 1640
#> CCA 100810 86397 2241
#> Intestine-Intestinal epithelial cells Intestine-Lymphatic endothelial cells
#> AAA 682907 5093
#> CAA 318641 2689
#> GAA 830938 6742
#> TAA 16172 104
#> ACA 372760 2825
#> CCA 369770 2745
#> Intestine-Lymphoid cells Intestine-Mesothelial cells
#> AAA 66416 6218
#> CAA 29621 3301
#> GAA 77269 8334
#> TAA 945 117
#> ACA 40509 3040
#> CCA 38967 3102
#> Intestine-Myeloid cells Intestine-Smooth muscle cells
#> AAA 62630 36695
#> CAA 28344 18181
#> GAA 73927 54542
#> TAA 1251 729
#> ACA 33699 14400
#> CCA 35498 15171
#> Intestine-Stromal cells Intestine-Vascular endothelial cells
#> AAA 368558 98706
#> CAA 181956 40222
#> GAA 517574 97682
#> TAA 7811 483
#> ACA 175951 42900
#> CCA 176015 37294long_format_codon_usage <- transformFormat(
data = SummarizedExperiment::assay(tTEobject, "CodonUsage"),
normalize = TRUE,
rownames_to_column = "codon", # features (row) of the CodonUsage matrix
names_to = "condition", # conditions (col) of the CodonUsage matrix
values_to = "usage"
) # values of the CodonUsage matrix
# long_format_codon_usage contains 3 columns: codon, condition, usage
# We are going to divide the condition column into tissue and cell_type
long_format_codon_usage <- long_format_codon_usage %>%
tidyr::separate(
.data$condition,
into = c("tissue", "cell_type"), sep = "-"
)head(long_format_codon_usage)
#> codon tissue cell_type usage
#> 1 AAA Adrenal Adrenocortical cells 0.0296446778
#> 2 CAA Adrenal Adrenocortical cells 0.0147430443
#> 3 GAA Adrenal Adrenocortical cells 0.0399063970
#> 4 TAA Adrenal Adrenocortical cells 0.0005426466
#> 5 ACA Adrenal Adrenocortical cells 0.0153240654
#> 6 CCA Adrenal Adrenocortical cells 0.0147100447long_format_AA_demand <- transformFormat(
data = SummarizedExperiment::assay(tTEobject, "AADemand"),
normalize = TRUE,
rownames_to_column = "AA", # features (row) of the AADemand matrix
names_to = "condition", # conditions (col) of the AADemand matrix
values_to = "demand"
) # values of the AADemand matrix
# long_format_AA_demand contains 3 columns: AA, condition, demand
# We are going to divide the condition column into tissue and cell_type
long_format_AA_demand <- long_format_AA_demand %>%
tidyr::separate(.data$condition, into = c("tissue", "cell_type"), sep = "-")2.2. Parameters
To enhance usability, parameters’ names and structures have been kept consistent across functions with most parameters being shared among them. This standardization simplifies customization and ensures an intuitive workflow.
Parameter | Description
|————————|————————————————-| | data | Properly formatted
dataset | | plot | A character string indicating the type
of plot to generate | | ncols | Numeric; Number of columns
for arranging panels. Defaults to 1 | | x_axis_col | Name
of the column in data to use for the x-axis | | y_axis_col
| Name of the column in data to use for the y-axis | |
condition_col | Name of the column in data to use for
coloring/grouping by condition | | targeted_arg | Optional;
A vector defining key feature clusters to highlight or label. | |
color_palette | Optional; A vector of color codes to
customize plot appearance | | filename | Optional; Name for
the saved plot | | show_legend | A character string
specifying the position of the legend. Supported formats: “none”
(default), “top”, “bottom”, “right” and “left” | |
add_titles | Logical; if TRUE, includes titles in the plot.
Defaults to TRUE |
3. Visualization options
The plotDistribution() function
generates jitter plots, barplots or
boxplots to visualize data distributions (e.g. raw or
normalize codon usage) across features (e.g. codons). This function
provides an intuitive representation of how usage patterns vary across
conditions. The type of plot can be specified via the plot
argument, while layout and grouping can be controlled using the
panels parameter.
In the example below, we visualize normalized codon usage across conditions to explore how codon preferences fluctuate. The dataset is a single-cell dataset, where each cell is annotated by its type and tissue of origin. As a general guideline, the jitter plot mode is well-suited for large datasets, as it allows users to observe the distribution of values at a general level and identify patterns across groups.
# Codon usage distribution plot (jitter)
plotDistribution(
data = long_format_codon_usage, plot = "jitter", x_axis_col = "codon",
y_axis_col = "usage", condition_col = "tissue", show_legend = "right",
add_titles = FALSE
)
Alternatively, to reduce dataset complexity and enable more in-depth analysis, we can focus on a subset of target conditions. In this example, we restrict the analysis to spleen-derived cells, specifically selecting those belonging to the lymphoid and myeloid lineages. For this approach we can use a barplot plot mode.
# Data subset: spleen tissue and selected cell types (lymphoid and myeloid)
spleen_indexes <- grep("Spleen", long_format_codon_usage$tissue, fixed = TRUE)
long_format_cu_spleen <- long_format_codon_usage[spleen_indexes, ]
lymphoid_indexes <- grep(
"Lymphoid", long_format_cu_spleen$cell_type,
fixed = TRUE
)
myeloid_indexes <- grep(
"Myeloid", long_format_cu_spleen$cell_type,
fixed = TRUE
)
cells_indexes <- c(lymphoid_indexes, myeloid_indexes)
long_format_codon_usage_subset <- long_format_cu_spleen[cells_indexes, ]To facilitate visualization, we enable the panels
parameter in plotDistribution, which
arranges each condition in a separate facet. This separation allows for
easier comparison across groups. Additionally, the ncols
parameter controls the number of columns used in the
facet layout, helping optimize the plot’s readability, especially when
working with multiple conditions.
# Codon usage distribution plot (barplot)
plotDistribution(
data = long_format_codon_usage_subset, plot = "barplot", ncols = 1,
facet_col = "cell_type",
x_axis_col = "codon", y_axis_col = "usage", condition_col = "cell_type",
show_legend = "none", add_titles = FALSE
)
If, instead of comparing the distributions of two cell types within the same tissue, we want to explore how distributions vary across two different tissues, we can switch to the boxplot mode. This plot type summarizes the variation and central tendency of the data, making it easier to compare distribution between broader biological groups.
# Data subset: specific codons in spleen and heart tissues
spleen_indexes <- grep("Spleen", long_format_codon_usage$tissue, fixed = TRUE)
heart_indexes <- grep("Heart", long_format_codon_usage$tissue, fixed = TRUE)
tissue_indexes <- c(spleen_indexes, heart_indexes)
long_format_cu_tissues <- long_format_codon_usage[tissue_indexes, ]
selected_codons <- c(
"CAA", "CAC", "CAG", "CAT", "CCA", "CCC", "CCG", "CCT", "CGA", "CGC",
"CGG", "CGT", "CTA", "CTC", "CTG", "CTT", "GAA", "GAC", "GAG", "GAT",
"GCA", "GCC", "GCG", "GCT", "GGA", "GGC", "GGG", "GGT", "GTA", "GTC",
"GTG", "GTT"
)
codons_indexes <- which(long_format_cu_tissues$codon %in% selected_codons)
long_format_cu_tissues <- long_format_cu_tissues[codons_indexes, ]In this example, we introduce a pre-defined
color_palette to explicitly assign specific colors to each
of the tissues being analyzed.
# Codon usage distribution plot (barplot)
plotDistribution(
data = long_format_cu_tissues, plot = "boxplot",
x_axis_col = "codon", y_axis_col = "usage", add_stats = FALSE,
condition_col = "tissue",
color_palette = c(Heart = "#de77ae", Spleen = "#7fbc41"),
show_legend = "bottom", add_titles = FALSE
)
The plotTargetComparison() function
extends the functionality of
plotDistribution() by allowing direct
comparison between a target condition and the overall mean. This
visualization helps identify how codon, anticodon, or amino acid usage
in the selected condition deviates from the average profile.
Function-specific parameters:
mean - A numeric vector containing the mean values of the
codons/anticodon/amino acids present in data.
show_difference - Logical; if TRUE, displays the
differences between the mean and the targeted values.
In the following example, we focus on cells from the cerebellum. To
enable a proper comparison, we first subset the dataset to include only
cerebellum-specific data and then compute the mean codon usage using
computeMeanUsage(). The overall mean codon
usage was previously obtained during the initial execution of
runPipeline().
# Data subset: mean codon usage across cerebellum cell types
codon_usage <- SummarizedExperiment::assay(tTEobject, "CodonUsage")
cerebellum_indexes <- grep("Cerebellum", colnames(codon_usage), fixed = TRUE)
cerebrum_indexes <- grep("Cerebrum", colnames(codon_usage), fixed = TRUE)
brain_indexes <- c(cerebellum_indexes, cerebrum_indexes)
brain_codon_usage <- codon_usage[, brain_indexes]
# Define the metadata
metadata_brain <- data.frame(
label = colnames(brain_codon_usage), stringsAsFactors = FALSE
)
metadata_brain <- tidyr::separate(
metadata_brain, label,
into = c("tissue", "cell.type"), sep = "-"
)
metadata_brain$conditions <- colnames(brain_codon_usage)
mean_brain_codon_usage <- computeMeanUsage(
data = brain_codon_usage, metadata = metadata_brain,
batch = "tissue"
)
#> A . Evaluating the input.
#> A . COMPLETED
#> B . Calculating the mean usage across conditions.
#> - Size-correcting the matrix to account for sequencing depth.
#> - Detected metadata column: 'conditions'
#> - Final dataset contains 18 unique samples.
#> - The 'data', 'metadata' and 'batch' parameters have been properly loaded.
#> - Size-correcting the counts using DESeq2.
#> - Filtering (if necessary) the 'data' and 'metadata' for common features.
#> - Detected metadata column: 'conditions'
#> - Final dataset contains 18 unique samples.
#> - Building exploratory model based on batch: ~ tissue
#> - Running DESeq2 pipeline...
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
#> - Extracting the size-corrected counts.
#> - Normalizing the usage matrix.
#> B . COMPLETEDBased on the calculations above, the plot illustrates: (i) the mean codon usage of the cerebellum cells as purple dots, (ii) the overall mean codon usage across conditions as grey bars, and (iii) the difference between the two as blue lines.
meta <- S4Vectors::metadata(tTEobject)
additional_metrics <- meta[["CodonUsage_AdditionalMetrics"]]
plotTargetComparison(
target_data = data.frame(mean_brain_codon_usage),
overall_data = data.frame(additional_metrics$MeanCodonUsage),
x_axis_col = "feature", y_axis_col = "mean_usage_across_conditions",
show_difference = TRUE, add_titles = FALSE
)
The plotProportion() function provides
a summary visualization of a specific metrics’ distribution across
features or conditions. It supports multiple display modes, including
bar, radar or donut plots, allowing users to
choose the most effective format for their data.
The choice of plot type in this and other functions within tTEscanR is left to the user’s discretion, depending on the nature of the data (e.g. number of features, distribution of counts) and the specific research question being addressed.
mean_codon_usage <- additional_metrics$MeanCodonUsage
mean_codon_usage$codon <- mean_codon_usage$feature
mean_codon_usage <- featuresToAA(
data = mean_codon_usage, position = "feature",
notation_from = "codon", notation_to = "aa"
)
#> --- Translating the input features ---
#> 1 . Checking the format of the input data.
#> 1 . COMPLETED
#> 2 . Extracting the features to translate.
#> 2 . COMPLETED
#> 3 . Translating the features.
#> - 64 out of 64 will be translated.
#> 3 . COMPLETED
#> --- The features have been successfully translated ---plotProportion(
data = mean_codon_usage, plot = "bar",
var_numerical = "mean_usage_across_conditions",
var_categorical = "codon", var_color = "feature", show_legend = "none"
) # Here feature corresponds to AA
#> $plot
# Data subset: mean codon usage across cerebellum cell types
aa_demand <- SummarizedExperiment::assay(tTEobject, "AADemand")
cerebellum_indexes_AA <- grep("Cerebellum", colnames(aa_demand), fixed = TRUE)
cerebrum_indexes_AA <- grep("Cerebrum", colnames(aa_demand), fixed = TRUE)
brain_indexes_AA <- c(cerebellum_indexes_AA, cerebrum_indexes_AA)
brain_AAdemand <- aa_demand[, brain_indexes_AA]
mean_brain_AAdemand <- computeMeanUsage(
data = brain_AAdemand, metadata = metadata_brain, batch = "tissue"
)
#> A . Evaluating the input.
#> A . COMPLETED
#> B . Calculating the mean usage across conditions.
#> - Size-correcting the matrix to account for sequencing depth.
#> - Detected metadata column: 'conditions'
#> - Final dataset contains 18 unique samples.
#> - The 'data', 'metadata' and 'batch' parameters have been properly loaded.
#> - Size-correcting the counts using DESeq2.
#> - Filtering (if necessary) the 'data' and 'metadata' for common features.
#> - Detected metadata column: 'conditions'
#> - Final dataset contains 18 unique samples.
#> - Building exploratory model based on batch: ~ tissue
#> - Running DESeq2 pipeline...
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#> function: y = a/x + b, and a local regression fit was automatically substituted.
#> specify fitType='local' or 'mean' to avoid this message next time.
#> final dispersion estimates
#> fitting model and testing
#> - Extracting the size-corrected counts.
#> - Normalizing the usage matrix.
#> B . COMPLETEDplotProportion(
data = mean_brain_AAdemand, plot = "donut",
var_numerical = "mean_usage_across_conditions",
var_categorical = "feature", show_legend = "none"
) # Here feature corresponds to AA
#> $plot
The plotTEscore() function generates a
violin plot to compare the tTE scores
between a specified target condition and all other conditions in the
dataset. This visualization helps assess how distinct the translation
efficiency profile of the target group is relative to the rest.
As plotTEscore() uses violin plots, it is important to ensure that each group, especially the targeted condition, has a sufficient number of data points. Violin plots may not provide a reliable representation of the distribution if the sample size is too small. Alternatively, plotDistribution and plotProportion() could be used.
tTEobject_meta <- S4Vectors::metadata(tTEobject)[["tTE_results"]]
tTEresults_codon <- tTEobject_meta[["tTEresults_codon"]]
tTEresults_AA <- tTEobject_meta[["tTEresults_AA"]]
conditions_metadata <- as.data.frame(SummarizedExperiment::colData(tTEobject))plotTEscore(
data = tTEresults_codon, metadata = conditions_metadata,
index_col = "conditions", class_col = "tissue", add_stats = FALSE
)
#> Warning in defineMergedData(data = data, meta = metadata, index = index_col, :
#> One or more groups have fewer than 2 samples. Statistics (p-values) will return
#> NA.
#> $plot
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#>
#> $stats
#> NULL# Targeted condition: lymphoid and myeloid cells
conditions_metadata$group <- "other"
conditions_metadata$group[
grep("Myeloid", conditions_metadata$conditions, fixed = TRUE)
] <- "myeloid"
conditions_metadata$group[
grep("Lymphoid", conditions_metadata$conditions, fixed = TRUE)
] <- "lymphoid"# input data has 4 columns: condition, tTE, p_value, neg_log10_tTE_p_value
# Codon-anticodon tTE score
plotTEscore(
data = tTEresults_codon, metadata = conditions_metadata,
index_col = "conditions", class_col = "group",
color_palette = c(
myeloid = "#abd9e9", lymphoid = "#f1b6da", other = "#fee0b6"
),
add_stats = TRUE
)
#> $plot
#>
#> $stats
#> group1 group2 p_value comparison p_signif class
#> 1 lymphoid myeloid 0.7922078 lymphoid_vs_myeloid ns myeloid
#> 2 lymphoid other 0.5128688 lymphoid_vs_other ns other
#> 3 myeloid other 0.2770987 myeloid_vs_other ns other
# AA demand-supply tTE score
plotTEscore(
data = tTEresults_AA, metadata = conditions_metadata,
index_col = "conditions", class_col = "group",
color_palette = c(
myeloid = "#abd9e9", lymphoid = "#f1b6da", other = "#fee0b6"
),
add_stats = FALSE
)
#> $plot
#>
#> $stats
#> NULLIn cases where we want to refine the selection of target cell types,
such as including or excluding specific subtypes, it is essential to
encode this distinction explicitly in the input metadata
(targets) data frame.
For instance, if we aim to focus on neurons but exclude cells labeled as ENS neurons, we need to assign those excluded cells to a different category, referred to as “other” in the metadata. This ensures the function correctly recognizes which cells belong to the target group and which do not.
# Targeted condition: neurons
conditions_metadata$group <- "other"
conditions_metadata$group[grep(
"neuron", conditions_metadata$conditions,
fixed = TRUE
)] <- "neurons"
conditions_metadata$group[grep(
"ENS neuron", conditions_metadata$conditions,
fixed = TRUE
)] <- "other"# AA demand-supply tTE score
plotTEscore(
data = tTEresults_AA, metadata = conditions_metadata,
index_col = "conditions", class_col = "group",
color_palette = c(neurons = "#8073ac", other = "#fee0b6"),
add_stats = TRUE
)
#> $plot
#>
#> $stats
#> group1 group2 p_value comparison p_signif class
#> 1 neurons other 0.02818374 neurons_vs_other * other4. References
#> R version 4.6.2 (2026-10-29)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so; LAPACK version 3.12.0
#>
#> 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
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] Seurat_5.6.0 SeuratObject_5.4.0 sp_2.2-4
#> [4] Signac_1.17.1 Rsamtools_2.29.0 Biostrings_2.81.9
#> [7] XVector_0.53.0 GenomicRanges_1.65.4 IRanges_2.47.5
#> [10] S4Vectors_0.51.10 Seqinfo_1.3.2 BiocGenerics_0.59.12
#> [13] generics_0.1.4 Matrix_1.7-6 dplyr_1.2.1
#> [16] biomaRt_2.69.6 tTEscanR_0.99.2 BiocStyle_2.41.0
#>
#> loaded via a namespace (and not attached):
#> [1] RcppAnnoy_0.0.23 later_1.4.8
#> [3] splines_4.6.2 bitops_1.1-0
#> [5] filelock_1.0.3 tibble_3.3.1
#> [7] polyclip_1.10-7 fastDummies_1.7.6
#> [9] lifecycle_1.0.5 httr2_1.3.0
#> [11] rstatix_1.1.0 doParallel_1.0.17
#> [13] globals_0.20.0 lattice_0.23-1
#> [15] MASS_7.3-66 MultiAssayExperiment_1.39.1
#> [17] backports_1.5.1 magrittr_2.0.5
#> [19] plotly_4.12.1 sass_0.4.10
#> [21] rmarkdown_2.32 jquerylib_0.1.4
#> [23] yaml_2.3.12 httpuv_1.6.17
#> [25] otel_0.2.0 sctransform_0.4.3
#> [27] spam_2.11-4 spatstat.sparse_3.2-0
#> [29] reticulate_1.47.0 cowplot_1.2.0
#> [31] pbapply_1.7-5 DBI_1.3.0
#> [33] buildtools_1.0.0 RColorBrewer_1.1-3
#> [35] abind_1.4-8 Rtsne_0.17
#> [37] purrr_1.2.2 circlize_0.4.18
#> [39] ggrepel_0.9.8 irlba_2.4.1
#> [41] spatstat.utils_3.2-5 listenv_1.1.0
#> [43] maketools_1.3.2 goftest_1.2-3
#> [45] RSpectra_0.16-2 spatstat.random_3.5-2
#> [47] fitdistrplus_1.2-6 parallelly_1.50.0
#> [49] codetools_0.2-20 DelayedArray_0.39.8
#> [51] RcppRoll_0.4.0 tidyselect_1.2.1
#> [53] shape_1.4.6.1 UCSC.utils_1.9.0
#> [55] farver_2.1.2 viridis_0.6.5
#> [57] spatstat.explore_3.8-3 matrixStats_1.5.0
#> [59] BiocFileCache_3.3.1 jsonlite_2.0.0
#> [61] GetoptLong_1.1.1 progressr_1.1.0
#> [63] Formula_1.2-6 ggridges_0.5.7
#> [65] survival_3.8-12 iterators_1.0.14
#> [67] foreach_1.5.2 tools_4.6.2
#> [69] progress_1.2.3 ica_1.0-3
#> [71] Rcpp_1.1.2 glue_1.8.1
#> [73] gridExtra_2.3.1 SparseArray_1.13.4
#> [75] BiocBaseUtils_1.15.1 xfun_0.62
#> [77] DESeq2_1.53.7 MatrixGenerics_1.25.0
#> [79] GenomeInfoDb_1.49.2 withr_3.0.3
#> [81] BiocManager_1.30.27 fastmap_1.2.0
#> [83] digest_0.6.39 mime_0.13
#> [85] R6_2.6.1 colorspace_2.1-4
#> [87] scattermore_1.2 tensor_1.5.1
#> [89] spatstat.data_3.1-9 RSQLite_3.53.3
#> [91] tidyr_1.3.2 data.table_1.18.6.1
#> [93] Structstrings_1.29.0 htmlwidgets_1.6.4
#> [95] prettyunits_1.2.0 httr_1.4.9
#> [97] S4Arrays_1.13.2 uwot_0.2.5
#> [99] pkgconfig_2.0.3 gtable_0.3.6
#> [101] blob_1.3.0 ComplexHeatmap_2.29.0
#> [103] lmtest_0.9-40 S7_0.2.2
#> [105] sys_3.4.3 htmltools_0.5.9
#> [107] carData_3.0-6 dotCall64_1.2
#> [109] clue_0.3-68 scales_1.4.0
#> [111] Biobase_2.73.2 dendsort_0.3.4
#> [113] png_0.1-9 spatstat.univar_3.2-0
#> [115] knitr_1.52 reshape2_1.4.5
#> [117] rjson_0.2.23 nlme_3.1-172
#> [119] curl_8.1.0 cachem_1.1.0
#> [121] zoo_1.9-1 GlobalOptions_0.1.4
#> [123] stringr_1.6.0 KernSmooth_2.23-27
#> [125] parallel_4.6.2 miniUI_0.1.2
#> [127] AnnotationDbi_1.75.2 pillar_1.11.1
#> [129] grid_4.6.2 vctrs_0.7.3
#> [131] RANN_2.6.3 promises_1.5.0
#> [133] ggpubr_1.0.0 car_3.1-5
#> [135] dbplyr_2.6.0 xtable_1.8-8
#> [137] cluster_2.1.8.3 evaluate_1.0.5
#> [139] cli_3.6.6 locfit_1.5-9.12
#> [141] compiler_4.6.2 rlang_1.3.0
#> [143] crayon_1.5.3 future.apply_1.21.0
#> [145] ggsignif_0.6.4 labeling_0.4.3
#> [147] plyr_1.8.9 stringi_1.8.9
#> [149] deldir_2.0-4 viridisLite_0.4.3
#> [151] BiocParallel_1.47.0 spatstat.geom_3.8-3
#> [153] RcppHNSW_0.7.0 hms_1.1.4
#> [155] patchwork_1.3.2 sparseMatrixStats_1.25.0
#> [157] bit64_4.8.6 future_1.82.0
#> [159] ggplot2_4.0.3 shiny_1.14.0
#> [161] KEGGREST_1.53.6 SummarizedExperiment_1.43.0
#> [163] ROCR_1.0-12 igraph_2.3.4
#> [165] broom_1.0.13 memoise_2.0.1
#> [167] bslib_0.12.0 fastmatch_1.1-8
#> [169] bit_4.6.0
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