forked from seandavi/awesome-single-cell
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathsingle-cell-software.json
1561 lines (1561 loc) · 58.2 KB
/
single-cell-software.json
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
[
{
"Name": "BASIC",
"Platform": "Python",
"DOI": "10.1093/bioinformatics/btw631",
"Pub Date": "2016-09-28",
"Code": "http://ttic.uchicago.edu/~aakhan/BASIC/",
"Description": "BASIC is a semi-de novo assembly method to determine the full-length sequence of the BCR in single B cells from scRNA-seq data.",
"License": "MIT",
"Added": "2016-10-10",
"Updated": "2016-10-10",
"DOI_url": "http://dx.doi.org/10.1093/bioinformatics/btw631",
"Github": false,
"Bioconductor": false,
"CRAN": false,
"categories": ["Assembly"]
},
{
"Name": "BRIE",
"Platform": "Python",
"DOI": "10.1101/098517",
"Code": "https://github.com/huangyh09/brie",
"Description": "BRIE (Bayesian regression for isoform estimate) is a Bayesian method to estimate isoform proportions from RNA-seq data.",
"License": "Apache-2.0",
"Added": "2017-01-10",
"Updated": "2017-01-10",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/098517",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Quantification", "Alternative Splicing"]
},
{
"Name": "demuxlet",
"Platform": "C++",
"DOI": "10.1101/118778",
"Code": "https://github.com/hyunminkang/apigenome",
"Description": "Sample demultiplexing of droplet sc-RNAseq reads.",
"License": "GPL-3",
"Added": "2017-03-29",
"Updated": "2017-03-29",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/118778",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Quantification"]
},
{
"Name": "ESAT",
"Platform": "Java",
"DOI": "10.1101/gr.207902.116",
"Pub Date": "2016-07-28",
"Code": "https://github.com/garber-lab/ESAT",
"Description": "The ESAT toolkit is designed for expression analysis of Digital expression (DGE) libraries that target transcript \"ends\". ESAT takes a set of alignment files (SAM or BAM) with genome alignment coordinates, a file containing transcript coordinates (BED or text file) and outputs read counts for each transcript provided.",
"Added": "2016-09-09",
"Updated": "2016-09-09",
"DOI_url": "http://dx.doi.org/10.1101/gr.207902.116",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Quantification", "Clustering", "Pseudotime", "Dimensionality Reduction"]
},
{
"Name": "Falco",
"Platform": "AWS",
"DOI": "10.1101/064006",
"Code": "https://github.com/VCCRI/Falco/",
"Description": "A Cloud-based Genetic Feature Quantification Analysis Tool",
"License": "GPL-3",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/064006",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Quantification"]
},
{
"Name": "Scater",
"Platform": "R",
"DOI": "10.1093/bioinformatics/btw777",
"Pub Date": "2017-01-14",
"Code": "https://github.com/davismcc/scater",
"Description": "Scater places an emphasis on tools for quality control, visualisation and pre-processing of data before further downstream analysis, filling a useful niche between raw RNA-sequencing count or transcripts-per-million data and more focused downstream modelling tools such as monocle, scLVM, SCDE, edgeR, limma and so on.",
"License": "GPL (>= 2)",
"Added": "2016-09-08",
"Updated": "2017-03-01",
"DOI_url": "http://dx.doi.org/10.1093/bioinformatics/btw777",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Quantification", "QC", "Normalisation", "Dimensionality Reduction", "Visualisation", "Interactive"]
},
{
"Name": "TASC",
"Platform": "C++",
"DOI": "10.1101/116939",
"Code": "https://github.com/scrna-seq/TASC",
"Description": "TASC (Toolkit for Analysis of Single Cell RNA-seq), an empirical Bayes approach to reliably model the cell-specific dropout rates and amplification bias by use of external RNA spike-ins.",
"License": "MIT",
"Added": "2017-03-29",
"Updated": "2017-03-29",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/116939",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Quantification", "Normalisation", "DE"]
},
{
"Name": "UMI-tools",
"Platform": "Python",
"DOI": "10.1101/051755",
"Code": "https://github.com/CGATOxford/UMI-tools",
"Description": "This repository contains tools for dealing with Unique Molecular Identifiers (UMIs)/Random Molecular Tags (RMTs).",
"License": "MIT",
"Added": "2016-09-12",
"Updated": "2016-09-12",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/051755",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Quantification"]
},
{
"Name": "umis",
"Platform": "Python",
"DOI": "10.1038/nmeth.4220",
"Pub Date": "2017-03-06",
"Code": "https://github.com/vals/umis",
"Description": "umis provides tools for estimating expression in RNA-Seq data which performs sequencing of end tags of trancsript, and incorporate molecular tags to correct for amplification bias.",
"License": "MIT",
"Added": "2016-09-12",
"Updated": "2017-03-10",
"DOI_url": "http://dx.doi.org/10.1038/nmeth.4220",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Quantification"]
},
{
"Name": "Cellity",
"Platform": "R",
"DOI": "10.1186/s13059-016-0888-1",
"Pub Date": "2016-02-17",
"Code": "https://github.com/teichlab/cellity",
"Description": "Classification of low quality cells in scRNA-seq data using R",
"License": "GPL (>= 2)",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"DOI_url": "http://dx.doi.org/10.1186/s13059-016-0888-1",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC"]
},
{
"Name": "dropbead",
"Platform": "R",
"DOI": "10.1101/099473",
"Code": "https://github.com/rajewsky-lab/dropbead",
"Description": "It offers a quick and straightfoward way to explore and perform basic analysis of single cell sequencing data coming from droplet sequencing, such as Drop-seq.",
"License": "GPL-3",
"Added": "2017-01-16",
"Updated": "2017-01-16",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/099473",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Visualisation"]
},
{
"Name": "flotilla",
"Platform": "Python",
"Code": "https://github.com/yeolab/flotilla",
"Description": "Reproducible machine learning analysis of gene expression and alternative splicing data",
"License": "Custom",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Clustering", "Gene Networks", "Dimensionality Reduction", "Interactive"]
},
{
"Name": "Granatum",
"Platform": "Virtualbox",
"DOI": "10.1101/110759",
"Code": "https://gitlab.com/uhcclxgg/granatum",
"Description": "This is a graphical single-cell RNA-seq (scRNA-seq) analysis pipeline for genomics scientists.",
"Added": "2017-03-01",
"Updated": "2017-03-01",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/110759",
"Github": false,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Normalisation", "Gene Filtering", "Clustering", "Pseudotime", "DE", "Gene Sets", "Gene Networks", "Dimensionality Reduction", "Visualisation", "Interactive"]
},
{
"Name": "MAGIC",
"Platform": "Python",
"DOI": "10.1101/111591",
"Code": "https://github.com/pkathail/magic/",
"Description": "Markov Affinity-based Graph Imputation of Cells (MAGIC)",
"License": "GPL-2",
"Added": "2017-02-27",
"Updated": "2017-02-17",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/111591",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Imputation", "Dimensionality Reduction", "Visualisation", "Interactive"]
},
{
"Name": "MAST",
"Platform": "R",
"DOI": "10.1186/s13059-015-0844-5",
"Pub Date": "2015-12-10",
"Code": "https://github.com/RGLab/MAST",
"Description": "Model-based Analysis of Single-cell Transcriptomics (MAST) fits a two-part, generalized linear models that are specially adapted for bimodal and/or zero-inflated single cell gene expression data.",
"License": "GPL (>= 2)",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"DOI_url": "http://dx.doi.org/10.1186/s13059-015-0844-5",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Normalisation", "DE", "Gene Sets", "Gene Networks"]
},
{
"Name": "OEFinder",
"Platform": "R",
"DOI": "10.1093/bioinformatics/btw004",
"Pub Date": "2016-01-06",
"Code": "https://github.com/lengning/OEFinder",
"Description": "Identify ordering effect genes in single cell RNA-seq data. OEFinder shiny impelemention depends on packages shiny, shinyFiles, gdata, and EBSeq.",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"DOI_url": "http://dx.doi.org/10.1093/bioinformatics/btw004",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Normalisation", "Interactive"]
},
{
"Name": "SCell",
"Platform": "Matlab",
"DOI": "10.1093/bioinformatics/btw201",
"Pub Date": "2016-04-19",
"Code": "https://github.com/diazlab/SCell",
"Description": "SCell is an integrated software tool for quality filtering, normalization, feature selection, iterative dimensionality reduction, clustering and the estimation of gene-expression gradients from large ensembles of single-cell RNA-seq datasets. SCell is open source, and implemented with an intuitive graphical interface.",
"License": "GNU",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"DOI_url": "http://dx.doi.org/10.1093/bioinformatics/btw201",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Normalisation", "Gene Filtering", "Clustering", "Pseudotime", "DE", "Dimensionality Reduction", "Visualisation", "Interactive"]
},
{
"Name": "SCONE",
"Platform": "R",
"Code": "https://github.com/YosefLab/scone",
"Description": "SCONE (Single-Cell Overview of Normalized Expression), a package for single-cell RNA-seq data quality control (QC) and normalization. This data-driven framework uses summaries of expression data to assess the efficacy of normalization workflows.",
"License": "Artistic-2.0",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Normalisation", "Visualisation", "Interactive"]
},
{
"Name": "scTDA",
"Platform": "Python",
"DOI": "10.1038/nbt.3854",
"Pub Date": "2017-05-01",
"Code": "https://github.com/RabadanLab/scTDA",
"Description": "scTDA is an object oriented python library for topological data analysis of high-throughput single-cell RNA-seq data. It includes tools for the preprocessing, analysis, and exploration of single-cell RNA-seq data based on topological representations.",
"License": "GPL-3",
"Added": "2017-05-09",
"Updated": "2017-05-09",
"DOI_url": "http://dx.doi.org/10.1038/nbt.3854",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Pseudotime", "Visualisation"]
},
{
"Name": "Sincera",
"Platform": "R",
"DOI": "10.1371/journal.pcbi.1004575",
"Pub Date": "2015-11-04",
"Code": "https://research.cchmc.org/pbge/sincera.html",
"Description": "R-based pipeline for single-cell analysis including clustering and visualization.",
"License": "GPL-3",
"Added": "2016-09-08",
"Updated": "2017-04-18",
"DOI_url": "http://dx.doi.org/10.1371/journal.pcbi.1004575",
"Github": false,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC", "Normalisation", "Gene Filtering", "Clustering", "DE", "Marker Genes"]
},
{
"Name": "SinQC",
"Platform": "R/Python",
"DOI": "10.1093/bioinformatics/btw176",
"Pub Date": "2016-04-10",
"Code": "http://www.morgridge.net/SinQC.html",
"Description": "A Method and Tool to Control Single-cell RNA-seq Data Quality.",
"Added": "2016-09-08",
"Updated": "2017-04-18",
"DOI_url": "http://dx.doi.org/10.1093/bioinformatics/btw176",
"Github": false,
"Bioconductor": false,
"CRAN": false,
"categories": ["QC"]
},
{
"Name": "BASiCs",
"Platform": "R",
"DOI": "10.1371/journal.pcbi.1004333",
"Pub Date": "2015-06-01",
"Code": "https://github.com/catavallejos/BASiCS",
"Description": "Bayesian Analysis of single-cell RNA-seq data. Estimates cell-specific normalization constants. Technical variability is quantified based on spike-in genes. The total variability of the expression counts is decomposed into technical and biological components.",
"License": "GPL (>= 2)",
"Added": "2016-09-08",
"Updated": "2016-09-13",
"DOI_url": "http://dx.doi.org/10.1371/journal.pcbi.1004333",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Normalisation", "DE", "Variable Genes"]
},
{
"Name": "Citrus (scPLS)",
"Platform": "R",
"DOI": "10.1101/045070",
"Code": "https://github.com/ChenMengjie/Citrus",
"Description": "A normalization method to remove unwanted variation using both control and target genes.",
"License": "GPL (>=2)",
"Added": "2016-09-09",
"Updated": "2016-09-09",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/045070",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Normalisation"]
},
{
"Name": "GiniClust",
"Platform": "R/Python",
"DOI": "10.1186/s13059-016-1010-4",
"Pub Date": "2016-07-01",
"Code": "https://github.com/lanjiangboston/GiniClust",
"Description": "GiniClust is a clustering method implemented in Python and R for detecting rare cell-types from large-scale single-cell gene expression data.",
"License": "MIT",
"Added": "2016-09-09",
"Updated": "2016-09-09",
"DOI_url": "http://dx.doi.org/10.1186/s13059-016-1010-4",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Normalisation", "Clustering", "Dimensionality Reduction", "Rare Cells", "Interactive"]
},
{
"Name": "GRM",
"Platform": "R",
"DOI": "10.1093/bioinformatics/btv122",
"Pub Date": "2015-07-01",
"Code": "http://wanglab.ucsd.edu/star/GRM/",
"Description": "Normalization and noise reduction for singlecell RNA-seq experiments",
"Added": "2016-09-09",
"Updated": "2017-04-18",
"DOI_url": "http://dx.doi.org/10.1093/bioinformatics/btv122",
"Github": false,
"Bioconductor": false,
"CRAN": false,
"categories": ["Normalisation"]
},
{
"Name": "Linnorm",
"Platform": "R",
"Code": "https://github.com/Bioconductor-mirror/Linnorm",
"Description": "Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIP-seq count data or any large scale count data.",
"License": "MIT",
"Added": "2017-02-01",
"Updated": "2017-02-01",
"Github": true,
"Bioconductor": true,
"CRAN": false,
"categories": ["Normalisation", "Imputation", "Gene Filtering", "Clustering", "DE", "Variable Genes", "Gene Networks", "Visualisation", "Simulation"]
},
{
"Name": "sake",
"Platform": "R",
"Code": "https://github.com/naikai/sake",
"Description": "Single-cell RNA-Seq Analysis and Klustering Evaluation. The aim of sake is to provide a user-friendly tool for easy analysis of NGS Single-Cell transcriptomic data.",
"License": "GPL (>=2)",
"Added": "2016-10-10",
"Updated": "2016-10-10",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Normalisation", "Gene Filtering", "Clustering", "DE", "Gene Sets", "Gene Networks", "Dimensionality Reduction", "Visualisation", "Interactive"]
},
{
"Name": "SAMstrt",
"Platform": "R",
"DOI": "10.1093/bioinformatics/btt511",
"Pub Date": "2013-11-15",
"Code": "https://github.com/shka/R-SAMstrt",
"Description": "Statistical test for differential expression in single-celltranscriptome with spike-in normalization",
"License": "LGLP-3.0",
"Added": "2016-09-09",
"Updated": "2016-09-09",
"DOI_url": "http://dx.doi.org/10.1093/bioinformatics/btt511",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Normalisation", "DE"]
},
{
"Name": "scLVM",
"Platform": "R/Python",
"DOI": "10.1038/nbt.3102",
"Pub Date": "2015-02-01",
"Code": "https://github.com/PMBio/scLVM",
"Description": "scLVM is a modelling framework for single-cell RNA-seq data that can be used to dissect the observed heterogeneity into different sources, thereby allowing for the correction of confounding sources of variation. scLVM was primarily designed to account for cell-cycle induced variations in single-cell RNA-seq data where cell cycle is the primary soure of variability.",
"License": "Apache-2.0",
"Added": "2016-09-08",
"Updated": "2016-12-08",
"DOI_url": "http://dx.doi.org/10.1038/nbt.3102",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Normalisation", "Variable Genes", "Cell Cycle", "Visualisation"]
},
{
"Name": "SCNorm",
"Platform": "R",
"DOI": "10.1038/nmeth.4263",
"Pub Date": "2017-04-17",
"Code": "https://github.com/rhondabacher/SCnorm",
"Description": "A quantile regression based approach for robust normalization of single cell RNA-seq data.",
"License": "GPL (>= 2)",
"Added": "2016-11-29",
"Updated": "2017-04-27",
"DOI_url": "http://dx.doi.org/10.1038/nmeth.4263",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Normalisation"]
},
{
"Name": "scran",
"Platform": "R",
"DOI": "10.1186/s13059-016-0947-7",
"Pub Date": "2016-04-27",
"Code": "https://github.com/MarioniLab/scran",
"Description": "This package implements a variety of low-level analyses of single-cell RNA-seq data. Methods are provided for normalization of cell-specific biases, pool-based norms to estimate size factors, assignment of cell cycle phase, and detection of highly variable and significantly correlated genes.",
"License": "GPL-3",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"DOI_url": "http://dx.doi.org/10.1186/s13059-016-0947-7",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Normalisation", "Variable Genes", "Cell Cycle"]
},
{
"Name": "Seurat",
"Platform": "R",
"DOI": "10.1038/nbt.3192",
"Pub Date": "2015-04-13",
"Code": "https://github.com/satijalab/seurat",
"Description": "It contains easy-to-use implementations of commonly used analytical techniques, including the identification of highly variable genes, dimensionality reduction (PCA, ICA, t-SNE), standard unsupervised clustering algorithms (density clustering, hierarchical clustering, k-means), and the discovery of differentially expressed genes and markers.",
"License": "GPL-3",
"Added": "2016-09-08",
"Updated": "2016-10-07",
"DOI_url": "http://dx.doi.org/10.1038/nbt.3192",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Normalisation", "Gene Filtering", "Clustering", "DE", "Marker Genes", "Variable Genes", "Dimensionality Reduction", "Visualisation"]
},
{
"Name": "ZINB-WaVE",
"Platform": "R",
"DOI": "10.1101/125112",
"Code": "https://github.com/drisso/zinbwave",
"Description": "Zero-inflated Negative Binomial based Wanted Variation Extraction (ZINB-WaVE)",
"Added": "2017-04-27",
"Updated": "2017-04-27",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/125112",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Normalisation", "Dimensionality Reduction"]
},
{
"Name": "CIDR",
"Platform": "R",
"DOI": "10.1101/068775",
"Code": "https://github.com/VCCRI/CIDR",
"Description": "Ultrafast and accurate clustering through imputation and dimensionality reduction for single-cell RNA-seq data.",
"License": "GPL (>=2)",
"Added": "2016-09-09",
"Updated": "2016-11-29",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/068775",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Imputation", "Clustering", "Dimensionality Reduction", "Simulation"]
},
{
"Name": "BackSPIN",
"Platform": "Python",
"DOI": "10.1126/science.aaa1934",
"Pub Date": "2015-03-06",
"Code": "https://github.com/linnarsson-lab/BackSPIN",
"Description": "Biclustering algorithm developed taking into account intrinsic features of single-cell RNA-seq experiments.",
"License": "BSD 2-clause",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"DOI_url": "http://dx.doi.org/10.1126/science.aaa1934",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Gene Filtering", "Clustering"]
},
{
"Name": "DeLorean",
"Platform": "R",
"DOI": "10.1093/bioinformatics/btw372",
"Pub Date": "2016-06-17",
"Code": "https://github.com/JohnReid/DeLorean",
"Description": "R package to model time series accounting for noise in the temporal dimension. Specifically designed for single cell transcriptome experiments.",
"License": "MIT",
"Added": "2016-09-09",
"Updated": "2016-09-09",
"DOI_url": "http://dx.doi.org/10.1093/bioinformatics/btw372",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Gene Filtering", "Pseudotime", "Expression Patterns", "Visualisation"]
},
{
"Name": "BEARscc",
"Platform": "R",
"DOI": "10.1101/118919",
"Code": "https://bitbucket.org/bsblabludwig/bearscc",
"Description": "BEARscc is a noise estimation and injection tool that is designed to assess putative single-cell RNA-seq clusters in the context of experimental noise estimated by ERCC spike-in controls.",
"License": "GPL-3",
"Added": "2017-04-27",
"Updated": "2017-04-27",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/118919",
"Github": false,
"Bioconductor": false,
"CRAN": false,
"categories": ["Clustering", "Simulation"]
},
{
"Name": "clusterExperiment",
"Platform": "R",
"Code": "https://github.com/epurdom/clusterExperiment",
"Description": "Functions for running and comparing many different clusterings of single-cell sequencing data. Meant to work with SCONE and slingshot.",
"License": "Artistic-2.0",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Clustering"]
},
{
"Name": "countClust",
"Platform": "R",
"Code": "https://github.com/kkdey/CountClust",
"Description": "Clustering and Visualizing RNA-Seq Expression Data using Grade of Membership Models. Fits grade of membership models (GoM, also known as admixture models) to cluster RNA-seq gene expression count data, identifies characteristic genes driving cluster memberships, and provides a visual summary of the cluster memberships.",
"License": "GPL (>= 2)",
"Added": "2016-09-12",
"Updated": "2016-09-12",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Clustering", "DE", "Variable Genes", "Visualisation"]
},
{
"Name": "ECLAIR",
"Platform": "Python",
"DOI": "10.1101/036533",
"Code": "https://github.com/GGiecold/ECLAIR",
"Description": "ECLAIR stands for Ensemble Clustering for Lineage Analysis, Inference and Robustness. Robust and scalable inference of cell lineages from gene expression data.",
"License": "MIT",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/036533",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Clustering", "Pseudotime", "Visualisation"]
},
{
"Name": "pcaReduce",
"Platform": "R",
"DOI": "10.1186/s12859-016-0984-y",
"Pub Date": "2016-03-22",
"Code": "https://github.com/JustinaZ/pcaReduce",
"Description": "Hierarchical clustering of single cell transcriptional profiles",
"License": "GPL (>= 2)",
"Added": "2016-09-09",
"Updated": "2016-09-09",
"DOI_url": "http://dx.doi.org/10.1186/s12859-016-0984-y",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Clustering"]
},
{
"Name": "RCA",
"Platform": "R",
"DOI": "10.1038/ng.3818",
"Pub Date": "2017-03-20",
"Code": "https://github.com/GIS-SP-Group/RCA",
"Description": "RCA, short for Reference Component Analysis, is an R package for robust clustering analysis of single cell RNA sequencing data (scRNAseq).",
"License": "MIT",
"Added": "2017-03-27",
"Updated": "2017-03-27",
"DOI_url": "http://dx.doi.org/10.1038/ng.3818",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Clustering", "Visualisation"]
},
{
"Name": "SC3",
"Platform": "R",
"DOI": "10.1101/036558",
"Code": "https://github.com/hemberg-lab/sc3",
"Description": "SC3 is an interactive tool for the unsupervised clustering of cells from single cell RNA-Seq experiments.",
"License": "GPL-3",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/036558",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Clustering", "Interactive"]
},
{
"Name": "SIMLR",
"Platform": "R",
"DOI": "10.1038/nmeth.4207",
"Pub Date": "2017-03-06",
"Code": "https://github.com/BatzoglouLabSU/SIMLR",
"Description": "Single-cell Interpretation via Multi-kernel LeaRning which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization.",
"License": "GPL-3",
"Added": "2016-09-09",
"Updated": "2017-03-10",
"DOI_url": "http://dx.doi.org/10.1038/nmeth.4207",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Clustering", "Dimensionality Reduction", "Visualisation"]
},
{
"Name": "sincell",
"Platform": "R",
"DOI": "10.1101/014472",
"Code": "https://github.com/Bioconductor-mirror/sincell",
"Description": "Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework.",
"License": "GPL (>= 2)",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/014472",
"Github": true,
"Bioconductor": true,
"CRAN": false,
"categories": ["Clustering", "Pseudotime", "Dimensionality Reduction", "Visualisation"]
},
{
"Name": "SPADE",
"Platform": "R",
"DOI": "10.1038/nprot.2016.066",
"Pub Date": "2016-06-16",
"Code": "https://github.com/nolanlab/spade",
"Description": "Visualization and cellular hierarchy inference of single-cell data using SPADE.",
"License": "GPL (>=2)",
"Added": "2016-09-09",
"Updated": "2017-04-18",
"DOI_url": "http://dx.doi.org/10.1038/nprot.2016.066",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Clustering", "Pseudotime", "Marker Genes", "Dimensionality Reduction", "Visualisation"]
},
{
"Name": "SSrGE",
"Platform": "Python",
"DOI": "10.1101/095810",
"Code": "https://github.com/lanagarmire/SSrGE",
"Description": "This procedure aims to fit sparse linear models using a binary matrix (n_samples x n_SNV) as features matrix and a gene expression matrix (n_genes x n_samples) as response.",
"License": "MIT",
"Added": "2017-01-10",
"Updated": "2017-01-10",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/095810",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Clustering", "Variants"]
},
{
"Name": "TSCAN",
"Platform": "R",
"DOI": "10.1093/nar/gkw430",
"Pub Date": "2016-05-13",
"Code": "https://github.com/zji90/TSCAN",
"Description": "Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis.",
"License": "GPL (>=2)",
"Added": "2016-09-09",
"Updated": "2016-09-09",
"DOI_url": "http://dx.doi.org/10.1093/nar/gkw430",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Clustering", "Pseudotime", "Marker Genes", "Visualisation", "Interactive"]
},
{
"Name": "CellTree",
"Platform": "R",
"DOI": "10.1186/s12859-016-1175-6",
"Pub Date": "2016-08-13",
"Code": "https://github.com/Bioconductor-mirror/cellTree",
"Description": "Cell population analysis and visualization from single cell RNA-seq data using a Latent Dirichlet Allocation model.",
"License": "Artistic-2.0",
"Added": "2016-09-08",
"Updated": "2016-09-19",
"DOI_url": "http://dx.doi.org/10.1186/s12859-016-1175-6",
"Github": true,
"Bioconductor": true,
"CRAN": false,
"categories": ["Pseudotime", "Gene Sets", "Visualisation"]
},
{
"Name": "DPT",
"Platform": "R/Matlab",
"DOI": "10.1038/nmeth.3971",
"Pub Date": "2019-08-29",
"Code": "http://www.helmholtz-muenchen.de/icb/research/groups/machine-learning/projects/dpt/index.html",
"Description": "Our DPT software implementations make it possible to reconstruct the developmental progression of cells and identify transient or metastable states, branching decisions and differentiation endpoints.",
"License": "GPL-3",
"Added": "2016-09-22",
"Updated": "2016-09-22",
"DOI_url": "http://dx.doi.org/10.1038/nmeth.3971",
"Github": false,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "Expression Patterns", "Visualisation"]
},
{
"Name": "embeddr",
"Platform": "R",
"DOI": "10.1101/027219",
"Code": "https://github.com/kieranrcampbell/embeddr",
"Description": "Embeddr creates a reduced dimensional representation of the gene space using a high-variance gene correlation graph and laplacian eigenmaps. It then fits a smooth pseudotime trajectory using principal curves.",
"License": "GPL-3",
"Added": "2016-09-12",
"Updated": "2016-09-12",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/027219",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime"]
},
{
"Name": "GPfates",
"Platform": "Python",
"DOI": "10.1126/sciimmunol.aal2192",
"Pub Date": "2017-03-03",
"Code": "https://github.com/Teichlab/GPfates",
"Description": "Model transcriptional cell fates as mixtures of Gaussian Processes",
"License": "MIT",
"Added": "2016-09-19",
"Updated": "2017-03-10",
"DOI_url": "http://dx.doi.org/10.1126/sciimmunol.aal2192",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "Visualisation"]
},
{
"Name": "k-branches",
"Platform": "R",
"DOI": "10.1101/094532",
"Code": "https://github.com/theislab/kbranches",
"Description": "The main idea behind the K-Branches method is to identify regions of interest (branching regions and tips) in differentiation trajectories of single cells.",
"License": "GPL-3",
"Added": "2016-12-22",
"Updated": "2016-12-22",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/094532",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime"]
},
{
"Name": "LEAP",
"Platform": "R",
"DOI": "10.1093/bioinformatics/btw729",
"Pub Date": "2016-12-19",
"Code": "https://cran.r-project.org/web/packages/LEAP/index.html",
"Description": "Constructing Gene Co-Expression Networks for Single-Cell RNA-Sequencing Data Using Pseudotime Ordering.",
"License": "GPL-2",
"Added": "2016-09-12",
"Updated": "2017-04-18",
"DOI_url": "http://dx.doi.org/10.1093/bioinformatics/btw729",
"Github": false,
"Bioconductor": false,
"CRAN": true,
"categories": ["Pseudotime", "Gene Networks"]
},
{
"Name": "MFA",
"Platform": "R",
"DOI": "10.12688/wellcomeopenres.11087.1",
"Pub Date": "2017-03-15",
"Code": "https://github.com/kieranrcampbell/mfa",
"Description": "mfa is an R package implementing Gibbs sampling for a Bayesian hierarchichal mixture of factor analysers for inference of bifurcations in single-cell data.",
"License": "GPL (>= 2)",
"Added": "2017-03-20",
"Updated": "2017-03-20",
"DOI_url": "http://dx.doi.org/10.12688/wellcomeopenres.11087.1",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "Visualisation", "Simulation"]
},
{
"Name": "Monocle",
"Platform": "R",
"DOI": "10.1038/nbt.2859",
"Pub Date": "2014-04-01",
"Code": "https://github.com/cole-trapnell-lab/monocle-release",
"Description": "Differential expression and time-series analysis for single-cell RNA-Seq.",
"License": "Artistic-2.0",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"DOI_url": "http://dx.doi.org/10.1038/nbt.2859",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "DE", "Expression Patterns", "Dimensionality Reduction", "Visualisation"]
},
{
"Name": "Mpath",
"Platform": "R",
"DOI": "10.1038/ncomms11988",
"Pub Date": "2016-06-30",
"Code": "https://github.com/JinmiaoChenLab/Mpath",
"Description": "Mpath: an algorithm for constructing multi-branching cell lineages from single-cell data",
"Added": "2016-09-09",
"Updated": "2016-09-09",
"DOI_url": "http://dx.doi.org/10.1038/ncomms11988",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime"]
},
{
"Name": "Ouija",
"Platform": "R",
"DOI": "10.1101/060442",
"Code": "https://github.com/kieranrcampbell/ouija",
"Description": "Incorporate prior information into single-cell trajectory (pseudotime) analyses using Bayesian nonlinear factor analysis.",
"License": "GPL (>=3)",
"Added": "2016-09-08",
"Updated": "2016-09-13",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/060442",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "Expression Patterns", "Visualisation"]
},
{
"Name": "pseudogp",
"Platform": "R",
"DOI": "10.1101/047365",
"Code": "https://github.com/kieranrcampbell/pseudogp",
"Description": "pseudogp is an R package for Bayesian inference of Gaussian Process Latent Variable models learning pseudotimes from single-cell RNA-seq.",
"License": "MIT",
"Added": "2016-09-09",
"Updated": "2016-09-09",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/047365",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "Visualisation"]
},
{
"Name": "SCENT",
"Platform": "R",
"DOI": "10.1101/084202",
"Code": "https://github.com/aet21/SCENT",
"Description": "scent_1.0 is an R-package for analysis of single-cell RNA-Seq data. It uses single-cell entropy to help analyse and interpret such data.",
"License": "GPL-3",
"Added": "2016-11-09",
"Updated": "2017-04-18",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/084202",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime"]
},
{
"Name": "SCIMITAR",
"Platform": "Python",
"DOI": "10.1101/070151",
"Code": "https://github.com/dimenwarper/scimitar",
"Description": "SCIMITAR provides a variety of tools to analyze trajectory maps of single-cell measurements.",
"Added": "2016-09-12",
"Updated": "2016-09-12",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/070151",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "Expression Patterns", "Gene Networks"]
},
{
"Name": "SCODE",
"Platform": "R",
"DOI": "10.1101/088856",
"Code": "https://github.com/hmatsu1226/SCODE",
"Description": "SCODE : an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation.",
"License": "MIT",
"Added": "2016-11-29",
"Updated": "2016-11-29",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/088856",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "Gene Networks"]
},
{
"Name": "SCORPIUS",
"Platform": "R",
"DOI": "10.1101/079509",
"Code": "https://github.com/rcannood/SCORPIUS",
"Description": "SCORPIUS an unsupervised approach for inferring developmental chronologies from single-cell RNA sequencing data.",
"License": "GPL-3",
"Added": "2016-10-11",
"Updated": "2016-10-11",
"Preprint": true,
"DOI_url": "http://dx.doi.org/10.1101/079509",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime", "Expression Patterns", "Gene Networks", "Dimensionality Reduction", "Visualisation"]
},
{
"Name": "SCOUP",
"Platform": "C++",
"Code": "https://github.com/hmatsu1226/SCOUP",
"Description": "Uses probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation.",
"License": "MIT",
"Added": "2016-09-08",
"Updated": "2016-09-08",
"Github": true,
"Bioconductor": false,
"CRAN": false,
"categories": ["Pseudotime"]
},
{
"Name": "SCUBA",
"Platform": "Matlab",
"DOI": "10.1073/pnas.1408993111",