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] 22309#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2098 1509 1302 840 867 1173 1246 802 883 992 1373 1119 433 747 756 1088
17 18 19 20 21 22
1486 305 1596 706 291 697 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 20548#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.9211#add z scores to results
load(paste0(results_dir, "/", analysis_id, "_expr_z_gene.Rd"))
ctwas_gene_res$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
0.0072097 0.0002839 #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
18.65 17.68 #report sample size
print(sample_size)[1] 336107#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 22309 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
0.008923 0.112528 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.3274 18.2854Genes with highest PIPs
genename region_tag susie_pip mu2 PVE
22029 JAKMIP3 10_83 1.0000 25835.31 7.687e-02
21929 DST 6_42 0.9989 12849.84 3.819e-02
22204 TNFSF12-TNFSF13 17_7 0.9393 28.54 7.977e-05
21961 PMS2P3 7_48 0.9231 93.18 2.559e-04
22013 CASP7 10_71 0.8357 24.71 6.145e-05
4765 EIF2B5 3_113 0.7968 36.44 8.638e-05
4686 SERPINI1 3_103 0.7921 21.90 5.162e-05
9031 SLC25A37 8_24 0.7797 25.59 5.936e-05
3573 ING5 2_144 0.7714 21.80 5.002e-05
12809 NTM 11_82 0.7609 28.08 6.358e-05
5483 SCLT1 4_85 0.7575 5737.07 1.293e-02
5482 SCLT1 4_85 0.7575 5737.07 1.293e-02
22256 HAP1 17_24 0.7214 23.10 4.958e-05
5838 C5orf64 5_37 0.7070 24.31 5.113e-05
22006 DENND1A 9_63 0.7057 29.38 6.169e-05
18113 RPTOR 17_45 0.7048 47.39 9.939e-05
8447 PTCD1 7_61 0.6991 53.92 1.122e-04
780 NASP 1_28 0.6986 26.26 5.458e-05
7658 FLJ44511 7_1 0.6981 22.19 4.609e-05
12516 CHORDC1 11_50 0.6958 25.08 5.192e-05
intron_id z num_eqtl
22029 intron_10_132164735_132166983 -4.434 1
21929 intron_6_56468999_56469883 2.984 1
22204 intron_17_7559702_7560049 -4.210 2
21961 intron_7_75514779_75515718 10.293 2
22013 intron_10_113679349_113697494 4.584 1
4765 intron_3_184278313_184283785 6.053 2
4686 intron_3_167735823_167789111 -4.049 1
9031 intron_8_23529212_23566108 5.008 1
3573 intron_2_241719612_241719976 -4.094 2
12809 intron_11_131582378_131660929 -4.792 2
5483 intron_4_128959777_128970378 2.051 1
5482 intron_4_128959777_128965227 -2.051 1
22256 intron_17_41727144_41727762 4.260 1
5838 intron_5_61704108_61730046 -3.939 1
22006 intron_9_123757822_123769514 4.837 1
18113 intron_17_80730706_80754010 -6.093 1
8447 intron_7_99429806_99433278 7.174 1
780 intron_1_45600457_45602255 5.388 1
7658 intron_7_524280_524854 4.025 1
12516 intron_11_90206810_90210536 4.423 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE
9706 CCDC171 9_13 0.000000 46173 0.000e+00
19184 MAST3 19_14 0.000000 28716 0.000e+00
19183 MAST3 19_14 0.000000 28527 0.000e+00
22185 TMOD3 15_21 0.502129 28474 4.254e-02
22053 BDNF-AS 11_19 0.000000 26676 0.000e+00
22029 JAKMIP3 10_83 1.000000 25835 7.687e-02
12292 MRPL21 11_38 0.249177 24004 1.780e-02
12291 MRPL21 11_38 0.001352 23853 9.595e-05
12294 IGHMBP2 11_38 0.000000 23082 0.000e+00
21883 FAM193A 4_3 0.000000 22784 0.000e+00
22051 BDNF-AS 11_19 0.000000 22105 0.000e+00
22050 BDNF-AS 11_19 0.000000 21988 0.000e+00
22186 TMOD3 15_21 0.000000 20985 0.000e+00
4092 HEMK1 3_36 0.000000 19351 0.000e+00
4091 C3orf18 3_36 0.000000 19347 0.000e+00
21881 FAM193A 4_3 0.000000 18953 0.000e+00
2988 CCNT2 2_80 0.644623 18918 3.628e-02
12293 MRPL21 11_38 0.000000 17846 0.000e+00
15218 ZSCAN29 15_16 0.000000 17150 0.000e+00
15220 ZSCAN29 15_16 0.000000 17107 0.000e+00
intron_id z num_eqtl
9706 intron_9_15888088_15888983 -7.9030 1
19184 intron_19_18147044_18147443 5.4612 2
19183 intron_19_18147017_18147443 -5.4419 2
22185 intron_15_51924578_51931031 -4.5865 1
22053 intron_11_27659228_27676982 -1.2015 1
22029 intron_10_132164735_132166983 -4.4341 1
12292 intron_11_68898012_68900535 4.3717 1
12291 intron_11_68892993_68893403 -4.4523 1
12294 intron_11_68929357_68933299 4.6259 2
21883 intron_4_2708268_2716023 -4.4114 1
22051 intron_11_27640005_27659171 -0.1817 2
22050 intron_11_27640005_27658241 5.8453 1
22186 intron_15_51938260_51947292 -2.4271 2
4092 intron_3_50572208_50577052 -4.7035 1
4091 intron_3_50569574_50570931 4.6818 1
21881 intron_4_2695129_2696363 5.4355 1
2988 intron_2_134942674_134946101 -3.6982 1
12293 intron_11_68898012_68900548 -3.9727 2
15218 intron_15_43366808_43368923 -3.3590 1
15220 intron_15_43369127_43369596 -3.4315 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE
22029 JAKMIP3 10_83 1.00000 25835.31 0.0768663
22185 TMOD3 15_21 0.50213 28473.69 0.0425385
21929 DST 6_42 0.99888 12849.84 0.0381885
2988 CCNT2 2_80 0.64462 18918.06 0.0362831
12292 MRPL21 11_38 0.24918 24003.54 0.0177953
13995 SPG20 13_13 0.50180 10493.10 0.0156661
13994 SPG20 13_13 0.50180 10493.10 0.0156661
5483 SCLT1 4_85 0.75754 5737.07 0.0129306
5482 SCLT1 4_85 0.75754 5737.07 0.0129306
5485 SCLT1 4_85 0.61284 5679.18 0.0103551
18960 ZNF561-AS1 19_9 0.35158 8652.60 0.0090511
5473 MFSD8 4_84 0.37933 6760.78 0.0076303
21928 DST 6_42 0.06431 12843.37 0.0024575
6351 SFXN1 5_105 0.32228 1037.44 0.0009948
15423 BBS4 15_34 0.29877 821.07 0.0007299
16224 TNRC6A 16_21 0.39875 488.50 0.0005795
16225 TNRC6A 16_21 0.39875 488.50 0.0005795
11033 TNKS2 10_59 0.46068 406.11 0.0005566
21961 PMS2P3 7_48 0.92308 93.18 0.0002559
6908 VPS52 6_28 0.46106 126.25 0.0001732
intron_id z num_eqtl
22029 intron_10_132164735_132166983 -4.434 1
22185 intron_15_51924578_51931031 -4.586 1
21929 intron_6_56468999_56469883 2.984 1
2988 intron_2_134942674_134946101 -3.698 1
12292 intron_11_68898012_68900535 4.372 1
13995 intron_13_36335832_36346582 2.819 1
13994 intron_13_36335832_36346240 -2.819 1
5483 intron_4_128959777_128970378 2.051 1
5482 intron_4_128959777_128965227 -2.051 1
5485 intron_4_129039096_129043395 2.284 1
18960 intron_19_9632417_9645074 -2.621 1
5473 intron_4_127957592_127965072 2.508 1
21928 intron_6_56466195_56468982 2.949 1
6351 intron_5_175516663_175521919 3.518 1
15423 intron_15_72686251_72695177 -6.333 1
16224 intron_16_24805151_24805605 -5.878 1
16225 intron_16_24805151_24805674 5.878 1
11033 intron_10_91817229_91819270 2.515 2
21961 intron_7_75514779_75515718 10.293 2
6908 intron_6_33269819_33270199 -1.561 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE
4064 RBM6 3_36 0.0013864 919.71 3.794e-06
4065 RBM6 3_36 0.0011915 919.32 3.259e-06
4068 RBM6 3_36 0.0011541 919.81 3.158e-06
4069 RBM6 3_36 0.0011541 919.81 3.158e-06
2228 DNAJC27-AS1 2_15 0.0000442 187.59 2.467e-08
2229 DNAJC27-AS1 2_15 0.0000442 187.59 2.467e-08
2230 DNAJC27-AS1 2_15 0.0000442 187.59 2.467e-08
22052 BDNF-AS 11_19 0.0000000 2233.50 0.000e+00
6938 C6orf106 6_28 0.0146671 114.88 5.013e-06
16277 SH2B1 16_23 0.1309648 62.96 2.453e-05
16313 DOC2A 16_24 0.0237613 93.13 6.584e-06
16279 SH2B1 16_23 0.1278130 62.85 2.390e-05
16274 ATXN2L 16_23 0.1157434 62.41 2.149e-05
16275 ATXN2L 16_23 0.1141648 62.35 2.118e-05
16308 TAOK2 16_24 0.0229805 92.37 6.316e-06
16273 ATXN2L 16_23 0.0856437 61.05 1.556e-05
16283 SH2B1 16_23 0.0763835 60.48 1.375e-05
1639 SEC16B 1_87 0.0319104 93.11 8.840e-06
16255 NPIPB7 16_23 0.0641576 61.39 1.172e-05
16322 MAPK3 16_24 0.0266509 89.97 7.134e-06
intron_id z num_eqtl
4064 intron_3_49962685_49967470 12.55 1
4065 intron_3_49999513_50048245 -12.54 1
4068 intron_3_50059746_50060801 -12.54 1
4069 intron_3_50059746_50060956 12.54 1
2228 intron_2_24972232_24972626 -11.29 1
2229 intron_2_24972232_24979060 11.29 1
2230 intron_2_24972232_25001415 11.29 1
22052 intron_11_27658462_27659171 -11.02 2
6938 intron_6_34688933_34696446 10.90 1
16277 intron_16_28863754_28866012 -10.76 1
16313 intron_16_30007090_30007173 -10.76 1
16279 intron_16_28865624_28866012 -10.76 1
16274 intron_16_28835399_28835546 10.74 1
16275 intron_16_28835399_28835549 -10.74 1
16308 intron_16_29989805_29990543 10.73 1
16273 intron_16_28826986_28829401 -10.68 1
16283 intron_16_28872758_28873170 10.65 1
1639 intron_1_177932569_177932698 -10.59 1
16255 intron_16_28466902_28477464 10.51 1
16322 intron_16_30114708_30115595 10.48 1Comparing z scores and PIPs
[1] 0.01959 genename region_tag susie_pip mu2 PVE
4064 RBM6 3_36 0.0013864 919.71 3.794e-06
4065 RBM6 3_36 0.0011915 919.32 3.259e-06
4068 RBM6 3_36 0.0011541 919.81 3.158e-06
4069 RBM6 3_36 0.0011541 919.81 3.158e-06
2228 DNAJC27-AS1 2_15 0.0000442 187.59 2.467e-08
2229 DNAJC27-AS1 2_15 0.0000442 187.59 2.467e-08
2230 DNAJC27-AS1 2_15 0.0000442 187.59 2.467e-08
22052 BDNF-AS 11_19 0.0000000 2233.50 0.000e+00
6938 C6orf106 6_28 0.0146671 114.88 5.013e-06
16277 SH2B1 16_23 0.1309648 62.96 2.453e-05
16313 DOC2A 16_24 0.0237613 93.13 6.584e-06
16279 SH2B1 16_23 0.1278130 62.85 2.390e-05
16274 ATXN2L 16_23 0.1157434 62.41 2.149e-05
16275 ATXN2L 16_23 0.1141648 62.35 2.118e-05
16308 TAOK2 16_24 0.0229805 92.37 6.316e-06
16273 ATXN2L 16_23 0.0856437 61.05 1.556e-05
16283 SH2B1 16_23 0.0763835 60.48 1.375e-05
1639 SEC16B 1_87 0.0319104 93.11 8.840e-06
16255 NPIPB7 16_23 0.0641576 61.39 1.172e-05
16322 MAPK3 16_24 0.0266509 89.97 7.134e-06
intron_id z num_eqtl
4064 intron_3_49962685_49967470 12.55 1
4065 intron_3_49999513_50048245 -12.54 1
4068 intron_3_50059746_50060801 -12.54 1
4069 intron_3_50059746_50060956 12.54 1
2228 intron_2_24972232_24972626 -11.29 1
2229 intron_2_24972232_24979060 11.29 1
2230 intron_2_24972232_25001415 11.29 1
22052 intron_11_27658462_27659171 -11.02 2
6938 intron_6_34688933_34696446 10.90 1
16277 intron_16_28863754_28866012 -10.76 1
16313 intron_16_30007090_30007173 -10.76 1
16279 intron_16_28865624_28866012 -10.76 1
16274 intron_16_28835399_28835546 10.74 1
16275 intron_16_28835399_28835549 -10.74 1
16308 intron_16_29989805_29990543 10.73 1
16273 intron_16_28826986_28829401 -10.68 1
16283 intron_16_28872758_28873170 10.65 1
1639 intron_1_177932569_177932698 -10.59 1
16255 intron_16_28466902_28477464 10.51 1
16322 intron_16_30114708_30115595 10.48 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 54Uploading 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"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[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
Warning in disease_enrichment(entities = genes, vocabulary = "HGNC", database =
"CURATED"): Removing duplicates from input list. Description FDR Ratio
34 Progressive cerebellar ataxia 0.02348 1/17
56 Familial encephalopathy with neuroserpin inclusion bodies 0.02348 1/17
61 NEUROPATHY, HEREDITARY SENSORY AND AUTONOMIC, TYPE VI 0.02348 1/17
63 EPIDERMOLYSIS BULLOSA SIMPLEX, AUTOSOMAL RECESSIVE 2 0.02348 1/17
67 CORTICAL DYSPLASIA, COMPLEX, WITH OTHER BRAIN MALFORMATIONS 9 0.02348 1/17
13 Muscle Spasticity 0.05011 1/17
17 Orofaciodigital Syndromes 0.05011 1/17
24 Epidermolysis Bullosa Simplex 0.05011 1/17
27 Lissencephaly 0.05011 1/17
28 Pachygyria 0.05011 1/17
BgRatio
34 1/9703
56 1/9703
61 1/9703
63 1/9703
67 1/9703
13 5/9703
17 6/9703
24 5/9703
27 6/9703
28 5/9703WebGestalt 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: 15 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$genename))[1] 19#significance threshold for TWAS
print(sig_thresh)[1] 4.73#number of ctwas genes
length(ctwas_genes)[1] 5#number of TWAS genes
length(twas_genes)[1] 437#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE
21924 DST 6_42 1.180e-04 12823.378 4.500e-06
21925 DST 6_42 0.000e+00 310.862 0.000e+00
21926 DST 6_42 0.000e+00 231.503 0.000e+00
21927 DST 6_42 4.817e-05 12822.035 1.838e-06
21928 DST 6_42 6.431e-02 12843.368 2.457e-03
21929 DST 6_42 9.989e-01 12849.838 3.819e-02
21930 DST 6_42 0.000e+00 408.243 0.000e+00
21931 DST 6_42 0.000e+00 138.964 0.000e+00
22013 CASP7 10_71 8.357e-01 24.712 6.145e-05
22014 CASP7 10_71 1.439e-02 8.373 3.585e-07
22028 JAKMIP3 10_83 0.000e+00 1620.964 0.000e+00
22029 JAKMIP3 10_83 1.000e+00 25835.308 7.687e-02
22203 TNFSF12-TNFSF13 17_7 6.901e-03 6.604 1.356e-07
22204 TNFSF12-TNFSF13 17_7 9.393e-01 28.544 7.977e-05
22205 TNFSF12-TNFSF13 17_7 8.303e-03 9.325 2.304e-07
22206 TNFSF12-TNFSF13 17_7 1.294e-02 10.134 3.902e-07
22207 TNFSF12-TNFSF13 17_7 1.294e-02 10.134 3.902e-07
22208 TNFSF12-TNFSF13 17_7 1.294e-02 10.134 3.902e-07
intron_id z num_eqtl
21924 intron_6_56463156_56463565 2.9051 1
21925 intron_6_56463764_56464685 -0.4804 2
21926 intron_6_56463764_56466078 0.4175 2
21927 intron_6_56464756_56466078 2.8924 1
21928 intron_6_56466195_56468982 2.9490 1
21929 intron_6_56468999_56469883 2.9842 1
21930 intron_6_56598006_56598476 3.7368 1
21931 intron_6_56851604_56900421 1.5511 1
22013 intron_10_113679349_113697494 4.5843 1
22014 intron_10_113725537_113726305 -0.5169 1
22028 intron_10_132138178_132140451 -3.2870 1
22029 intron_10_132164735_132166983 -4.4341 1
22203 intron_17_7559297_7559846 1.6368 2
22204 intron_17_7559702_7560049 -4.2095 2
22205 intron_17_7559893_7560049 1.3318 1
22206 intron_17_7560488_7560724 1.2342 1
22207 intron_17_7560817_7560999 -1.2342 1
22208 intron_17_7560872_7560999 -1.2342 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.0000 0.1707 #specificity
print(specificity) ctwas TWAS
0.9991 0.9583 #precision / PPV
print(precision) ctwas TWAS
0.00000 0.01602 
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