BMI - Brain Cortex
sheng Qian
2021-2-6
Last updated: 2022-02-22
Checks: 6 1
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 17941#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1660 1259 1029 722 677 898 1022 619 742 824 1064 869 349 624 606 843
17 18 19 20 21 22
1196 235 1302 618 253 530 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 16639#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.9274#add z scores to results
load(paste0(results_dir, "/", analysis_id, "_expr_z_gene.Rd"))
ctwas_gene_res$z <- z_gene[ctwas_gene_res$intron_id,]$z
z_snp <- z_snp[z_snp$id %in% ctwas_snp_res$id,]
ctwas_snp_res$z <- z_snp$z[match(ctwas_snp_res$id, z_snp$id)]
#merge gene and snp results with added information
ctwas_snp_res$genename=NA
ctwas_snp_res$gene_type=NA
ctwas_snp_res$intron_id=NA
ctwas_res <- rbind(ctwas_gene_res,
ctwas_snp_res[,colnames(ctwas_gene_res)])
#get number of eQTL for geens
num_eqtl <- c()
for (i in 1:22){
load(paste0(results_dir, "/", analysis_id, "_expr_chr", i, ".exprqc.Rd"))
num_eqtl <- c(num_eqtl, unlist(lapply(wgtlist, nrow)))
}
ctwas_gene_res$num_eqtl <- num_eqtl[ctwas_gene_res$intron_id]
#store columns to report
report_cols <- colnames(ctwas_gene_res)[!(colnames(ctwas_gene_res) %in% c("type", "region_tag1", "region_tag2", "cs_index", "gene_type", "z_flag", "id", "chrom", "pos"))]
first_cols <- c("genename", "region_tag")
report_cols <- c(first_cols, report_cols[!(report_cols %in% first_cols)])
report_cols_snps <- c("id", report_cols[-1])
report_cols_snps <- report_cols_snps[!(report_cols_snps %in% "num_eqtl")]
#get number of SNPs from s1 results; adjust for thin argument
ctwas_res_s1 <- data.table::fread(paste0(results_dir, "/", analysis_id, "_ctwas.s1.susieIrss.txt"))
n_snps <- sum(ctwas_res_s1$type=="SNP")/thin
rm(ctwas_res_s1)Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0077201 0.0002893 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
26.53 16.47 #report sample size
print(sample_size)[1] 336107#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 17941 7535010#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01093 0.10681 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.155 16.092Genes with highest PIPs
genename region_tag susie_pip mu2 PVE
17781 MOK 14_53 1.0000 9581.22 2.851e-02
17576 DST 6_42 1.0000 11325.01 3.369e-02
17702 SPG20 13_13 0.9961 15543.69 4.606e-02
17790 TNFSF12-TNFSF13 17_7 0.9399 29.11 8.139e-05
17498 EIF2B5 3_113 0.9073 38.06 1.027e-04
17599 PMS2P3 7_48 0.8810 86.89 2.278e-04
17540 RGS12 4_4 0.8723 37.67 9.778e-05
3625 SERPINI1 3_103 0.7968 23.94 5.676e-05
3805 PCGF3 4_2 0.7888 24.68 5.792e-05
7128 SLC25A37 8_24 0.7886 27.02 6.339e-05
14809 DOT1L 19_3 0.7618 23.25 5.271e-05
16862 BID 22_2 0.7175 27.78 5.930e-05
524 NASP 1_28 0.7128 27.17 5.762e-05
12854 TNRC6A 16_21 0.7071 446.99 9.403e-04
17659 RP11-386G11.5 12_31 0.7017 1484.82 3.100e-03
8195 PRRC2B 9_69 0.6941 22.21 4.586e-05
3263 SLC25A26 3_45 0.6891 24.24 4.970e-05
10318 SLCO1A2 12_16 0.6849 25.49 5.194e-05
5985 FLJ44511 7_1 0.6822 22.46 4.558e-05
6911 KCNH2 7_93 0.6671 39.68 7.875e-05
intron_id z num_eqtl
17781 intron_14_102265912_102283478 2.804 1
17576 intron_6_56464756_56466078 2.881 1
17702 intron_13_36312477_36313968 2.776 1
17790 intron_17_7559702_7560049 -4.225 1
17498 intron_3_184278313_184283785 6.068 2
17599 intron_7_75514779_75515718 9.959 1
17540 intron_4_3293099_3316070 6.796 1
3625 intron_3_167735823_167789111 -4.405 2
3805 intron_4_731110_732400 -4.645 2
7128 intron_8_23529212_23566108 5.008 1
14809 intron_19_2199939_2202700 4.106 1
16862 intron_22_17739488_17773610 5.130 1
524 intron_1_45600457_45602255 5.388 1
12854 intron_16_24730300_24750726 5.887 1
17659 intron_12_49005543_49005743 -4.788 1
8195 intron_9_131488096_131491422 3.949 1
3263 intron_3_66263379_66304453 -4.417 1
10318 intron_12_21319579_21324594 -4.572 1
5985 intron_7_524280_524854 3.984 2
6911 intron_7_150951120_150951448 -6.291 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE
17371 MMP23B 1_1 0.000e+00 28198 0.000e+00
17375 NADK 1_1 0.000e+00 27440 0.000e+00
15171 MAST3 19_14 0.000e+00 24187 0.000e+00
15169 MAST3 19_14 0.000e+00 23926 0.000e+00
15170 MAST3 19_14 0.000e+00 23868 0.000e+00
17642 BDNF-AS 11_19 0.000e+00 23839 0.000e+00
15172 MAST3 19_14 0.000e+00 23484 0.000e+00
9798 MRPL21 11_38 4.880e-10 20928 3.039e-11
9802 IGHMBP2 11_38 1.722e-07 20831 1.068e-08
9801 IGHMBP2 11_38 6.743e-07 20789 4.171e-08
2449 PLCL1 2_117 1.001e-08 17642 5.257e-10
3165 HEMK1 3_35 0.000e+00 17319 0.000e+00
3164 HEMK1 3_35 0.000e+00 17318 0.000e+00
17473 WDSUB1 2_96 0.000e+00 16665 0.000e+00
17702 SPG20 13_13 9.961e-01 15544 4.606e-02
17703 SPG20 13_13 5.563e-04 15516 2.568e-05
17704 SPG20 13_13 4.241e-02 15514 1.958e-03
17705 SPG20 13_13 4.241e-02 15514 1.958e-03
17706 SPG20 13_13 4.241e-02 15514 1.958e-03
17380 GNB1 1_1 0.000e+00 15433 0.000e+00
intron_id z num_eqtl
17371 intron_1_1676846_1692449 4.905 1
17375 intron_1_1755476_1756258 -4.601 1
15171 intron_19_18147017_18147443 -5.270 2
15169 intron_19_18107618_18121685 5.673 1
15170 intron_19_18110366_18110652 5.647 1
17642 intron_11_27659228_27676982 -1.202 1
15172 intron_19_18147044_18147443 5.187 2
9798 intron_11_68898012_68900535 4.240 2
9802 intron_11_68930434_68933299 -4.493 1
9801 intron_11_68929357_68933299 4.575 2
2449 intron_2_197805339_198001952 -5.642 1
3165 intron_3_50577573_50577826 4.701 1
3164 intron_3_50572208_50577052 -4.704 1
17473 intron_2_159257985_159259810 4.164 1
17702 intron_13_36312477_36313968 2.776 1
17703 intron_13_36312477_36314227 -2.722 2
17704 intron_13_36335832_36346225 -2.819 1
17705 intron_13_36335832_36346240 -2.819 1
17706 intron_13_36335832_36346582 2.819 1
17380 intron_1_1839238_1890820 5.351 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE
17702 SPG20 13_13 0.99607 15543.69 0.0460647
17576 DST 6_42 1.00000 11325.01 0.0336945
17781 MOK 14_53 1.00000 9581.22 0.0285065
1369 TATDN3 1_108 0.34685 3387.57 0.0034958
17659 RP11-386G11.5 12_31 0.70166 1484.82 0.0030997
1366 TATDN3 1_108 0.22233 3386.80 0.0022403
1368 TATDN3 1_108 0.22233 3386.80 0.0022403
17705 SPG20 13_13 0.04241 15513.67 0.0019577
17704 SPG20 13_13 0.04241 15513.67 0.0019577
17706 SPG20 13_13 0.04241 15513.67 0.0019577
1364 TATDN3 1_108 0.17705 3385.74 0.0017834
1365 TATDN3 1_108 0.16655 3385.60 0.0016776
17555 TMEM161B-AS1 5_52 0.12335 4407.43 0.0016175
1359 NSL1 1_108 0.11578 3381.75 0.0011649
12854 TNRC6A 16_21 0.70705 446.99 0.0009403
5247 PGBD1 6_22 0.01893 12742.78 0.0007175
1367 TATDN3 1_108 0.06123 3370.79 0.0006141
8749 BTAF1 10_59 0.33147 267.47 0.0002638
17599 PMS2P3 7_48 0.88105 86.89 0.0002278
3208 ITIH4 3_36 0.43989 173.74 0.0002274
intron_id z num_eqtl
17702 intron_13_36312477_36313968 2.776 1
17576 intron_6_56464756_56466078 2.881 1
17781 intron_14_102265912_102283478 2.804 1
1369 intron_1_212812328_212815013 3.295 1
17659 intron_12_49005543_49005743 -4.788 1
1366 intron_1_212807848_212812227 -3.301 1
1368 intron_1_212807848_212812251 -3.301 1
17705 intron_13_36335832_36346240 -2.819 1
17704 intron_13_36335832_36346225 -2.819 1
17706 intron_13_36335832_36346582 2.819 1
1364 intron_1_212804429_212804596 3.300 1
1365 intron_1_212804651_212807736 3.301 1
17555 intron_5_88270585_88282042 -8.048 1
1359 intron_1_212738686_212739534 -3.259 1
12854 intron_16_24730300_24750726 5.887 1
5247 intron_6_28281918_28283776 -3.377 1
1367 intron_1_212807848_212812248 3.365 2
8749 intron_10_91996570_91997080 -2.915 1
17599 intron_7_75514779_75515718 9.959 1
3208 intron_3_52818536_52819393 6.000 2Genes with largest z scores
genename region_tag susie_pip mu2 PVE
17641 BDNF-AS 11_19 0.000e+00 9001.51 0.000e+00
3145 RBM6 3_35 4.921e-03 843.54 1.235e-05
17640 BDNF-AS 11_19 0.000e+00 9677.57 0.000e+00
1678 DNAJC27-AS1 2_15 1.074e-04 191.78 6.129e-08
1679 DNAJC27-AS1 2_15 1.074e-04 191.78 6.129e-08
1680 DNAJC27-AS1 2_15 1.074e-04 191.78 6.129e-08
3147 RBM6 3_35 2.016e-13 156.43 9.383e-17
15610 GIPR 19_32 5.626e-01 116.75 1.954e-04
12923 INO80E 16_24 2.299e-02 96.99 6.635e-06
15609 GIPR 19_32 3.937e-01 115.96 1.358e-04
12906 SH2B1 16_23 2.396e-01 87.23 6.217e-05
12905 ATXN2L 16_23 2.090e-01 86.83 5.400e-05
12904 ATXN2L 16_23 2.076e-01 86.81 5.361e-05
12902 RP11-57A19.4 16_23 1.828e-01 86.79 4.720e-05
1182 SEC16B 1_87 3.122e-02 95.87 8.907e-06
12896 SULT1A2 16_23 9.591e-02 94.94 2.709e-05
12887 CLN3 16_23 4.117e-02 86.92 1.065e-05
12897 SULT1A2 16_23 7.622e-02 92.01 2.086e-05
5457 C6orf106 6_28 2.666e-05 121.52 9.637e-09
12901 SULT1A1 16_23 2.944e-02 81.25 7.117e-06
intron_id z num_eqtl
17641 intron_11_27658462_27659171 -13.14 1
3145 intron_3_49999513_50046716 12.54 1
17640 intron_11_27640005_27659171 12.11 1
1678 intron_2_24972232_24972626 -11.29 1
1679 intron_2_24972232_24979060 11.29 1
1680 intron_2_24972232_25001415 11.29 1
3147 intron_3_50047326_50048245 10.81 2
15610 intron_19_45677779_45677906 10.80 1
12923 intron_16_30001040_30005221 -10.77 1
15609 intron_19_45677108_45677710 10.77 1
12906 intron_16_28869383_28871780 10.76 1
12905 intron_16_28835399_28835549 -10.74 1
12904 intron_16_28835399_28835546 10.74 1
12902 intron_16_28715434_28723164 -10.72 1
1182 intron_1_177932569_177932698 -10.59 1
12896 intron_16_28592443_28593252 -10.57 1
12887 intron_16_28487541_28487662 -10.48 1
12897 intron_16_28592481_28593252 -10.42 2
5457 intron_6_34654779_34696446 -10.24 1
12901 intron_16_28620133_28623131 10.07 1Comparing z scores and PIPs
[1] 0.01728 genename region_tag susie_pip mu2 PVE
17641 BDNF-AS 11_19 0.000e+00 9001.51 0.000e+00
3145 RBM6 3_35 4.921e-03 843.54 1.235e-05
17640 BDNF-AS 11_19 0.000e+00 9677.57 0.000e+00
1678 DNAJC27-AS1 2_15 1.074e-04 191.78 6.129e-08
1679 DNAJC27-AS1 2_15 1.074e-04 191.78 6.129e-08
1680 DNAJC27-AS1 2_15 1.074e-04 191.78 6.129e-08
3147 RBM6 3_35 2.016e-13 156.43 9.383e-17
15610 GIPR 19_32 5.626e-01 116.75 1.954e-04
12923 INO80E 16_24 2.299e-02 96.99 6.635e-06
15609 GIPR 19_32 3.937e-01 115.96 1.358e-04
12906 SH2B1 16_23 2.396e-01 87.23 6.217e-05
12905 ATXN2L 16_23 2.090e-01 86.83 5.400e-05
12904 ATXN2L 16_23 2.076e-01 86.81 5.361e-05
12902 RP11-57A19.4 16_23 1.828e-01 86.79 4.720e-05
1182 SEC16B 1_87 3.122e-02 95.87 8.907e-06
12896 SULT1A2 16_23 9.591e-02 94.94 2.709e-05
12887 CLN3 16_23 4.117e-02 86.92 1.065e-05
12897 SULT1A2 16_23 7.622e-02 92.01 2.086e-05
5457 C6orf106 6_28 2.666e-05 121.52 9.637e-09
12901 SULT1A1 16_23 2.944e-02 81.25 7.117e-06
intron_id z num_eqtl
17641 intron_11_27658462_27659171 -13.14 1
3145 intron_3_49999513_50046716 12.54 1
17640 intron_11_27640005_27659171 12.11 1
1678 intron_2_24972232_24972626 -11.29 1
1679 intron_2_24972232_24979060 11.29 1
1680 intron_2_24972232_25001415 11.29 1
3147 intron_3_50047326_50048245 10.81 2
15610 intron_19_45677779_45677906 10.80 1
12923 intron_16_30001040_30005221 -10.77 1
15609 intron_19_45677108_45677710 10.77 1
12906 intron_16_28869383_28871780 10.76 1
12905 intron_16_28835399_28835549 -10.74 1
12904 intron_16_28835399_28835546 10.74 1
12902 intron_16_28715434_28723164 -10.72 1
1182 intron_1_177932569_177932698 -10.59 1
12896 intron_16_28592443_28593252 -10.57 1
12887 intron_16_28487541_28487662 -10.48 1
12897 intron_16_28592481_28593252 -10.42 2
5457 intron_6_34654779_34696446 -10.24 1
12901 intron_16_28620133_28623131 10.07 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 41Uploading data to Enrichr... Done.
Querying GO_Biological_Process_2021... Done.
Querying GO_Cellular_Component_2021... Done.
Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"
Term
1 regulation of potassium ion export across plasma membrane (GO:1903764)
2 signal transduction in response to DNA damage (GO:0042770)
Overlap Adjusted.P.value Genes
1 2/7 0.03345 KCNH2;DLG1
2 3/52 0.03345 DOT1L;BID;HINFP
[1] "GO_Cellular_Component_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)DisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio
5 Body Weight 0.03264 2/20
59 Progressive cerebellar ataxia 0.03264 1/20
82 Familial encephalopathy with neuroserpin inclusion bodies 0.03264 1/20
89 NEUROPATHY, HEREDITARY SENSORY AND AUTONOMIC, TYPE VI 0.03264 1/20
92 EPIDERMOLYSIS BULLOSA SIMPLEX, AUTOSOMAL RECESSIVE 2 0.03264 1/20
93 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 28 0.03264 1/20
19 Cardiac Arrest 0.05763 1/20
27 Muscle Spasticity 0.05763 1/20
35 Schizophrenia 0.05763 6/20
38 Torsades de Pointes 0.05763 1/20
BgRatio
5 15/9703
59 1/9703
82 1/9703
89 1/9703
92 1/9703
93 1/9703
19 5/9703
27 5/9703
35 883/9703
38 3/9703WebGestalt enrichment analysis for genes with PIP>0.5
Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet, minNum =
minNum, : No significant gene set is identified based on FDR 0.05!NULLPIP Manhattan Plot
Warning: ggrepel: 8 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
Sensitivity, specificity and precision for silver standard genes
#number of genes in known annotations
print(length(known_annotations))[1] 41#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))[1] 16#significance threshold for TWAS
print(sig_thresh)[1] 4.686#number of ctwas genes
length(ctwas_genes)[1] 7#number of TWAS genes
length(twas_genes)[1] 310#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE
11838 MOK 14_54 3.877e-03 7.674 8.853e-08
17573 DST 6_42 0.000e+00 195.435 0.000e+00
17574 DST 6_42 0.000e+00 1543.673 0.000e+00
17575 DST 6_42 0.000e+00 1552.793 0.000e+00
17576 DST 6_42 1.000e+00 11325.007 3.369e-02
17577 DST 6_42 0.000e+00 368.171 0.000e+00
17702 SPG20 13_13 9.961e-01 15543.691 4.606e-02
17703 SPG20 13_13 5.563e-04 15515.542 2.568e-05
17704 SPG20 13_13 4.241e-02 15513.667 1.958e-03
17705 SPG20 13_13 4.241e-02 15513.667 1.958e-03
17706 SPG20 13_13 4.241e-02 15513.667 1.958e-03
17772 MOK 14_53 6.280e-05 9383.727 1.753e-06
17773 MOK 14_53 1.009e-05 70.695 2.123e-09
17774 MOK 14_53 9.967e-06 70.396 2.088e-09
17775 MOK 14_53 5.234e-06 2072.466 3.228e-08
17776 MOK 14_53 5.234e-06 2072.466 3.228e-08
17777 MOK 14_53 5.234e-06 2072.466 3.228e-08
17778 MOK 14_53 5.040e-06 6505.599 9.755e-08
17779 MOK 14_53 6.032e-06 4031.677 7.236e-08
17780 MOK 14_53 5.941e-06 4039.599 7.140e-08
17781 MOK 14_53 1.000e+00 9581.221 2.851e-02
17790 TNFSF12-TNFSF13 17_7 9.399e-01 29.105 8.139e-05
intron_id z num_eqtl
11838 intron_14_102233789_102250812 -1.2728 1
17573 intron_6_56463156_56463565 0.7310 1
17574 intron_6_56463764_56464685 -1.0970 2
17575 intron_6_56463764_56466078 1.1002 2
17576 intron_6_56464756_56466078 2.8806 1
17577 intron_6_56598006_56600069 -3.7368 1
17702 intron_13_36312477_36313968 2.7757 1
17703 intron_13_36312477_36314227 -2.7224 2
17704 intron_13_36335832_36346225 -2.8188 1
17705 intron_13_36335832_36346240 -2.8188 1
17706 intron_13_36335832_36346582 2.8188 1
17772 intron_14_102226408_102232535 2.9326 1
17773 intron_14_102229657_102231707 -1.2704 1
17774 intron_14_102229657_102232535 1.2592 1
17775 intron_14_102250987_102251756 0.1934 1
17776 intron_14_102250990_102251756 0.1934 1
17777 intron_14_102251804_102251917 0.1934 1
17778 intron_14_102251995_102263546 0.7714 2
17779 intron_14_102251995_102283478 0.1647 1
17780 intron_14_102263616_102283478 -0.1437 2
17781 intron_14_102265912_102283478 2.8041 1
17790 intron_17_7559702_7560049 -4.2247 1#sensitivity / recall
print(sensitivity)ctwas TWAS
0.000 0.122 #specificity
print(specificity) ctwas TWAS
0.9974 0.9643 #precision / PPV
print(precision) ctwas TWAS
0.00000 0.01613 
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.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] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] readxl_1.3.1 forcats_0.5.1
[3] stringr_1.4.0 dplyr_1.0.7
[5] purrr_0.3.4 readr_2.1.1
[7] tidyr_1.1.4 tidyverse_1.3.1
[9] tibble_3.1.6 WebGestaltR_0.4.4
[11] disgenet2r_0.99.2 enrichR_3.0
[13] cowplot_1.0.0 ggplot2_3.3.5
[15] EnsDb.Hsapiens.v79_2.99.0 ensembldb_2.8.0
[17] AnnotationFilter_1.8.0 GenomicFeatures_1.36.3
[19] AnnotationDbi_1.46.0 Biobase_2.44.0
[21] GenomicRanges_1.36.1 GenomeInfoDb_1.20.0
[23] IRanges_2.18.1 S4Vectors_0.22.1
[25] BiocGenerics_0.30.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] ggbeeswarm_0.6.0 colorspace_2.0-2
[3] rjson_0.2.20 ellipsis_0.3.2
[5] rprojroot_2.0.2 XVector_0.24.0
[7] fs_1.5.2 rstudioapi_0.13
[9] farver_2.1.0 ggrepel_0.9.1
[11] bit64_4.0.5 lubridate_1.8.0
[13] fansi_0.5.0 xml2_1.3.3
[15] codetools_0.2-16 doParallel_1.0.16
[17] cachem_1.0.6 knitr_1.36
[19] jsonlite_1.7.2 apcluster_1.4.8
[21] Cairo_1.5-12.2 Rsamtools_2.0.0
[23] broom_0.7.10 dbplyr_2.1.1
[25] compiler_3.6.1 httr_1.4.2
[27] backports_1.4.1 assertthat_0.2.1
[29] Matrix_1.2-18 fastmap_1.1.0
[31] lazyeval_0.2.2 cli_3.1.0
[33] later_0.8.0 htmltools_0.5.2
[35] prettyunits_1.1.1 tools_3.6.1
[37] igraph_1.2.10 gtable_0.3.0
[39] glue_1.5.1 GenomeInfoDbData_1.2.1
[41] reshape2_1.4.4 doRNG_1.8.2
[43] Rcpp_1.0.7 cellranger_1.1.0
[45] jquerylib_0.1.4 vctrs_0.3.8
[47] Biostrings_2.52.0 svglite_1.2.2
[49] rtracklayer_1.44.4 iterators_1.0.13
[51] xfun_0.29 rvest_1.0.2
[53] lifecycle_1.0.1 rngtools_1.5.2
[55] XML_3.99-0.3 zlibbioc_1.30.0
[57] scales_1.1.1 vroom_1.5.7
[59] hms_1.1.1 promises_1.0.1
[61] ProtGenerics_1.16.0 SummarizedExperiment_1.14.1
[63] yaml_2.2.1 curl_4.3.2
[65] memoise_2.0.1 ggrastr_1.0.1
[67] gdtools_0.1.9 biomaRt_2.40.1
[69] stringi_1.7.6 RSQLite_2.2.8
[71] highr_0.9 foreach_1.5.1
[73] BiocParallel_1.18.0 rlang_0.4.12
[75] pkgconfig_2.0.3 matrixStats_0.57.0
[77] bitops_1.0-7 evaluate_0.14
[79] lattice_0.20-38 GenomicAlignments_1.20.1
[81] labeling_0.4.2 bit_4.0.4
[83] tidyselect_1.1.1 plyr_1.8.6
[85] magrittr_2.0.1 R6_2.5.1
[87] generics_0.1.1 DelayedArray_0.10.0
[89] DBI_1.1.1 haven_2.4.3
[91] pillar_1.6.4 whisker_0.3-2
[93] withr_2.4.3 RCurl_1.98-1.5
[95] modelr_0.1.8 crayon_1.4.2
[97] utf8_1.2.2 tzdb_0.2.0
[99] rmarkdown_2.11 progress_1.2.2
[101] grid_3.6.1 data.table_1.14.2
[103] blob_1.2.2 git2r_0.26.1
[105] reprex_2.0.1 digest_0.6.29
[107] httpuv_1.5.1 munsell_0.5.0
[109] beeswarm_0.2.3 vipor_0.4.5