Ruochen Jiang, Wei Vivian Li, and Jingyi Jessica Li 2021-08-18
The goal of mbImpute is to impute false zero counts in microbiome sequencing data, i.e., a sample-by-taxon count matrix, by jointly borrowing information from similar samples, similar taxa and optional metadata including sample covariates and taxon phylogeny.
Please install the following R packages first.
install.packages("glmnet")
install.packages("devtools")
install.packages("Matrix")Then you can use the following R command to directly install the mbImpute package from GitHub:
library(devtools)
install_github("ruochenj/mbImpute/mbImpute R package")We use the microbiome dataset from Karlsson et al (2013) as an example to demonstrate the use of mbImpute:
# Load the R packages
library(mbImpute)
library(glmnet)
#> Loading required package: Matrix
#> Loaded glmnet 4.1-2
library(Matrix)
# Display part of the OTU table
otu_tab[1:6, 1:6]
#> s__Clostridium_sp_L2_50 s__Faecalibacterium_prausnitzii
#> S112 3954419 2602398
#> S118 0 1169731
#> S121 550000 3162050
#> S126 0 986563
#> S127 0 3940520
#> S131 0 502030
#> s__Dialister_invisus s__Dorea_longicatena s__Ruminococcus_obeum
#> S112 1671620 1440718 1112728
#> S118 1412991 623343 190968
#> S121 827400 855614 274969
#> S126 2411487 163956 112493
#> S127 0 554154 286616
#> S131 0 159910 802850
#> s__Coprococcus_comes
#> S112 947723
#> S118 58660
#> S121 281024
#> S126 148430
#> S127 210698
#> S131 450721
# Display part of the taxon phylogenetic distance matrix, whose rows and columns correspond to the columns in otu_tab
D[1:6, 1:6]
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 0 2 9 10 10 8
#> [2,] 2 0 9 10 10 8
#> [3,] 9 9 0 3 3 3
#> [4,] 10 10 3 0 2 4
#> [5,] 10 10 3 2 0 4
#> [6,] 8 8 3 4 4 0
# Display part of the (optional) meta data, i.e., the sample covariate matrix with rows representing samples and corresponding to the rows in otu_tab
meta_data[1:6, 1:6]
#> study_condition age number_reads triglycerides hba1c ldl
#> S112 IGT 1.293993 0.6475183 0.9926486 1.2575721 1.0022890
#> S118 control 2.587987 1.4075527 0.2357540 0.8803004 2.8883168
#> S121 control 1.293993 0.9558486 0.1613054 1.2575721 0.9915117
#> S126 control 1.293993 1.2244933 0.7072621 1.3833293 2.4895566
#> S127 control 2.587987 0.9428587 0.9057918 1.2575721 3.2116358
#> S131 IGT 1.293993 1.0145444 1.8239918 1.5090865 1.7351455
study_condition = meta_data[,1]
meta_data <- as.data.frame(unclass(meta_data))
meta_data <- meta_data[,-1]
# Demo 1: run mbImpute on a single core
imputed_count_mat_list <- mbImpute(condition = study_condition, otu_tab = otu_tab, metadata = meta_data, D = D)
#> [1] 1
#> [1] 0
#> [1] 11230 23280
#> [1] 0.7156728 5.0000000 5.0000000 5.0000000
#> [1] 11230 23280
#> [1] 0.7156728 0.5568270 5.0000000 5.0000000
#> [1] 11230 23280
#> [1] 0.7156728 0.5568270 0.5841656 5.0000000
#> [1] 11230 23280
#> [1] 0.7156728 0.5568270 0.5841656 0.6362045
#> [1] 10000
#> [1] "Finished."
# A glance at the imputed result, which includes three matrices
## The first is an imputed matrix on the log10 scale; we recommend users to perform downstream analysis based on normal distributions on this data, whose values in each taxon (column) follows an approximate normal distribution (see our paper for detail)
imputed_count_mat_list$imp_count_mat_lognorm[1:3, 1:2]
#> s__Clostridium_sp_L2_50 s__Faecalibacterium_prausnitzii
#> S112 5.335660 5.153952
#> S118 4.000822 4.721291
#> S121 4.409327 5.168918
## The second is an imputed normalized count matrix, where each sample (row) is set to have the same total of a million reads
imputed_count_mat_list$imp_count_mat_norm[1:3, 1:2]
#> s__Clostridium_sp_L2_50 s__Faecalibacterium_prausnitzii
#> S112 216599 142544
#> S118 10017 52635
#> S121 25663 147541
## The third is an imputed count matrix on the original scale, with each sample (row) having the read count same as that in the original otu_tab
imputed_count_mat_list$imp_count_mat_origlibsize[1:3, 1:2]
#> s__Clostridium_sp_L2_50 s__Faecalibacterium_prausnitzii
#> S112 3954404 2602397
#> S118 222608 1169709
#> S121 549997 3162029
# Demo 2: if you have multiple (e.g., 4) cores and would like to do parallel computing
imputed_count_mat_list <- mbImpute(condition = meta_data$study_condition, otu_tab = otu_tab, metadata = meta_data, D = D, parallel = TRUE, ncores = 4)
#> [1] 1
#> [1] 0
#> [1] 11230 23280
#> [1] 0.7156728 5.0000000 5.0000000 5.0000000
#> [1] 11230 23280
#> [1] 0.7156728 0.5568270 5.0000000 5.0000000
#> [1] 11230 23280
#> [1] 0.7156728 0.5568270 0.5841656 5.0000000
#> [1] 11230 23280
#> [1] 0.7156728 0.5568270 0.5841656 0.6362045
#> [1] 10000
#> [1] "Finished."
# A glance at the imputed result, which includes three matrices
## The first is an imputed matrix on the log10 scale; we recommend users to perform downstream analysis based on normal distributions on this data, whose values in each taxon (column) follows an approximate normal distribution (see our paper for detail)
imputed_count_mat_list$imp_count_mat_lognorm[1:3, 1:2]
#> s__Clostridium_sp_L2_50 s__Faecalibacterium_prausnitzii
#> S112 5.335660 5.153952
#> S118 4.000822 4.721291
#> S121 4.409327 5.168918
## The second is an imputed normalized count matrix, where each sample (row) is set to have the same total of a million reads
imputed_count_mat_list$imp_count_mat_norm[1:3, 1:2]
#> s__Clostridium_sp_L2_50 s__Faecalibacterium_prausnitzii
#> S112 216599 142544
#> S118 10017 52635
#> S121 25663 147541
## The third is an imputed count matrix on the original scale, with each sample (row) having the read count same as that in the original otu_tab
imputed_count_mat_list$imp_count_mat_origlibsize[1:3, 1:2]
#> s__Clostridium_sp_L2_50 s__Faecalibacterium_prausnitzii
#> S112 3954404 2602397
#> S118 222608 1169709
#> S121 549997 3162029
# Demo 3: if you do not have meta data or phylogenetic information, and the samples belong to one condition
otu_tab_T2D <- otu_tab[study_condition == "T2D",]
imputed_count_matrix_list <- mbImpute(otu_tab = otu_tab_T2D)
#> [1] "Meta data information unavailable"
#> [1] "Phylogenentic information unavailable"
#> [1] 4204 3707
#> [1] 1.055828 5.000000 5.000000 5.000000
#> [1] 4204 3707
#> [1] 1.055828 1.053811 5.000000 5.000000
#> [1] 4204 3707
#> [1] 1.055828 1.053811 0.962270 5.000000
#> [1] 4204 3707
#> [1] 1.0558281 1.0538114 0.9622700 0.7518937
#> [1] "Finished."
# A glance at the imputed result, which includes three matrices
## The first is an imputed matrix on the log10 scale; we recommend users to perform downstream analysis based on normal distributions on this data, whose values in each taxon (column) follows an approximate normal distribution (see our paper for detail)
imputed_count_mat_list$imp_count_mat_lognorm[1:3, 1:2]
#> s__Clostridium_sp_L2_50 s__Faecalibacterium_prausnitzii
#> S112 5.335660 5.153952
#> S118 4.000822 4.721291
#> S121 4.409327 5.168918
## The second is an imputed normalized count matrix, where each sample (row) is set to have the same total of a million reads
imputed_count_mat_list$imp_count_mat_norm[1:3, 1:2]
#> s__Clostridium_sp_L2_50 s__Faecalibacterium_prausnitzii
#> S112 216599 142544
#> S118 10017 52635
#> S121 25663 147541
## The third is an imputed count matrix on the original scale, with each sample (row) having the read count same as that in the original otu_tab
imputed_count_mat_list$imp_count_mat_origlibsize[1:3, 1:2]
#> s__Clostridium_sp_L2_50 s__Faecalibacterium_prausnitzii
#> S112 3954404 2602397
#> S118 222608 1169709
#> S121 549997 3162029Reference:
Karlsson, F. H., Tremaroli, V., Nookaew, I., Bergström, G., Behre, C. J., Fagerberg, B., … & Bäckhed, F. (2013). Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature, 498(7452), 99-103.