lichen-lab / gmpr Goto Github PK
View Code? Open in Web Editor NEWGMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data
Home Page: https://github.com/lichen-lab/GMPR
gmpr's Issues
New error message after installing r version 4.2.0 running the tutorial example
Error in if (!(class(OTUmatrix) %in% c("data.frame", "matrix"))) stop("Unknown datatype of object "OTUmatrix".") :
the condition has length > 1
require(GUniFrac)
require(vegan)
require(DESeq2)
library(GMPR)
data(throat.otu.tab)
data(throat.meta)
tu.tab <- t(throat.otu.tab)
gmpr.size.factor <- GMPR(t(otu.tab))
How to deal with the NA values?
Hi,
Thank you for developing this useful tool for microbiota data normalization!
I'm trying to perform the demo data analysis, but there were some errors when I performed the analysis.
when perform the analysis through library:
require(GUniFrac)
require(vegan)
require(DESeq2)
library(GMPR)
data(throat.otu.tab)
data(throat.meta)
###########################################################################################################Calculate GMPR size factor
Row - features, column - samples
otu.tab <- t(throat.otu.tab)
gmpr.size.factor <- GMPR(t(otu.tab))
Warning message:
In if (!(class(OTUmatrix) %in% c("data.frame", "matrix"))) stop("Unknown datatype of object "OTUmatrix".") :
条件的长度大于一，因此只能用其第一元素
when perform the analysis through source the function:
source("C:/Users/Administrator/Linux/Scripts/GMPR-master/GMPR.R")
gmpr.size.factor <- GMPR(t(otu.tab))
Begin GMPR size factor calculation ...
50
100
150
200
250
300
350
400
450
500
550
600
650
700
750
800
850
Completed!
Please watch for the samples with limited sharing with other samples based on NSS! They may be outliers!
Warning message:
In GMPR(t(otu.tab)) : The following samples
4695
2983
2554
3315
879
1313
5661
4125
2115
3309
3225
514
3427
484
2894
5523
652
5160
3349
4526
4925
3202
4716
3015
5046
1477
1873
1583
1203
5047
3428
4399
642
4499
2740
5334
3067
4883
5162
1655
4220
39
2371
3600
378
4864
5291
1651
3574
1635
4472
5456
3026
189
784
1091
742
368
4564
420
501
5522
4306
1026
2981
5414
232
1995
3231
1044
1639
5280
1037
5483
3546
1623
3352
4969
5217
3992
3097
2556
2239
263
1592
5054
4497
1867
1219
1986
4733
4666
4847
5201
2906
1903
571
2660
4697
3302
4831
1981
1796
765
2313
2098
2002
5547
5655
5675
911
991
3699
3353
2743
978
285
4771
1596
4494
2198
4022
1755
2982
323
4608
1278
4877
667
2761
1160
3758
483
2757
3075
2122
3715
4708
502
777
906
3020
3492
1419
351
2729
440
4967
2836
607
3703
4840
1283
2570
5581
4417
65
231
5308
2718
5227
5286
3039
5679
2046
1421
3390
679
2582
4589
5119
2398
955
2263
4354
1448
308
4424
4298
2334
2153
4721
2301
2595
3938
1795
4775
2025
4478
1476
3969
4693
943
4248
1561
5269
467
2481
933
5167
4013
2980
3865
44 [... truncated]
otu.tab.norm <- t(t(otu.tab) / gmpr.size.factor)
View(otu.tab.norm)
head(otu.tab.norm)
ESC_1.1_OPL ESC_1.3_OPL ESC_1.4_OPL ESC_1.5_OPL ESC_1.6_OPL ESC_1.10_OPL ESC_1.11_OPL
4695 NA NA NA NA NA NA NA
2983 NA NA NA 0 NA NA NA
2554 0 NA 0 NA NA NA 0
3315 NA NA NA NA NA NA NA
879 NA 0 NA NA NA NA 0
1313 NA NA NA NA NA NA NA
ESC_1.12_OPL ESC_1.13_OPL ESC_1.14_OPL ESC_1.15_OPL ESC_1.18_OPL ESC_1.19_OPL ESC_1.20_OPL
4695 NA NA NA NA NA NA NA
2983 0 NA NA NA NA NA NA
2554 NA NA 0 NA NA NA NA
3315 NA NA NA NA NA NA NA
879 NA NA NA NA NA NA NA
1313 NA NA 0 NA NA NA NA
ESC_1.21_OPL ESC_1.22_OPL ESC_1.23_OPL ESC_1.24_OPL ESC_1.25_OPL ESC_1.26_OPL ESC_1.27_OPL
4695 0 NA NA NA NA NA 0
2983 NA NA NA NA NA NA 0
2554 NA NA NA NA NA NA NA
3315 NA NA NA NA NA NA NA
879 0 NA NA 0 0 NA NA
1313 NA NA NA 0 NA NA NA
ESC_1.28_OPL ESC_1.29_OPL ESC_1.30_OPL ESC_1.31_OPL ESC_1.32_OPL ESC_1.33_OPL ESC_1.34_OPL
4695 NA NA NA NA NA 0 NA
2983 NA NA 0 NA NA NA 0.0000000
2554 NA NA NA NA 0 NA NA
3315 NA NA NA NA NA NA 0.4716809
879 NA NA NA NA NA NA NA
1313 NA NA NA NA 0 NA NA
ESC_1.35_OPL ESC_1.36_OPL ESC_1.37_OPL ESC_1.39_OPL ESC_1.40_OPL ESC_1.42_OPL ESC_1.43_OPL
4695 NA NA NA NA 0 NA NA
2983 NA NA NA NA NA 0 NA
2554 NA NA NA NA NA NA NA
3315 NA NA NA 0 NA NA NA
879 NA NA NA NA 0 NA NA
1313 NA NA NA NA NA NA NA
ESC_1.44_OPL ESC_1.45_OPL ESC_1.46_OPL ESC_1.47_OPL ESC_1.48_OPL ESC_1.49_OPL ESC_1.50_OPL
4695 NA NA NA NA NA NA NA
2983 NA NA NA NA 0 NA NA
2554 NA NA 0 0 NA NA 1.141567
3315 NA NA NA NA NA 0 0.000000
879 NA NA NA NA NA 0 0.000000
1313 NA 0 NA NA NA NA NA
ESC_1.51_OPL ESC_1.52_OPL ESC_1.53_OPL ESC_1.55_OPL ESC_1.56_OPL ESC_1.57_OPL ESC_1.58_OPL
4695 NA NA 8.252633 NA 0 NA NA
2983 NA 0 NA 0 NA NA NA
2554 NA NA NA NA NA 0 0
3315 0 NA NA NA NA NA NA
879 NA 0 NA NA NA NA NA
1313 NA NA 0.000000 NA NA NA NA
ESC_1.59_OPL ESC_1.60_OPL ESC_1.61_OPL ESC_1.62_OPL ESC_1.63_OPL ESC_1.64_OPL ESC_1.65_OPL
4695 0 NA NA NA NA 0 NA
2983 NA NA NA NA NA NA 0
2554 0 NA NA NA 2.110897 NA NA
3315 NA 0 NA NA NA NA 0
879 0 NA NA NA 0.000000 NA 0
1313 NA NA NA NA NA NA NA
ESC_1.67_OPL ESC_1.68_OPL ESC_1.69_OPL ESC_1.70_OPL
4695 NA NA NA 0
2983 NA NA NA NA
2554 0 NA NA NA
3315 NA NA NA NA
879 NA NA NA NA
1313 NA NA NA NA
What should I deal with these NAs in the normalized table?
Thank you!
Hongbin liu
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