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qclist_all <- list()

qc_files <- paste0(results_dir, "/", list.files(results_dir, pattern="exprqc.Rd"))

for (i in 1:length(qc_files)){
  load(qc_files[i])
  chr <- unlist(strsplit(rev(unlist(strsplit(qc_files[i], "_")))[1], "[.]"))[1]
  qclist_all[[chr]] <- cbind(do.call(rbind, lapply(qclist,unlist)), as.numeric(substring(chr,4)))
}

qclist_all <- data.frame(do.call(rbind, qclist_all))
colnames(qclist_all)[ncol(qclist_all)] <- "chr"

rm(qclist, wgtlist, z_gene_chr)

#number of imputed weights
nrow(qclist_all)
[1] 10862
#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 
1103  776  625  427  501  624  518  377  399  423  648  611  203  368  356  495 
  17   18   19   20   21   22 
 676  177  840  318  126  271 
#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)
[1] 0.6548518
library(ggplot2)
library(cowplot)

********************************************************
Note: As of version 1.0.0, cowplot does not change the
  default ggplot2 theme anymore. To recover the previous
  behavior, execute:
  theme_set(theme_cowplot())
********************************************************
load(paste0(results_dir, "/", analysis_id, "_ctwas.s2.susieIrssres.Rd"))
  
df <- data.frame(niter = rep(1:ncol(group_prior_rec), 2),
                 value = c(group_prior_rec[1,], group_prior_rec[2,]),
                 group = rep(c("Gene", "SNP"), each = ncol(group_prior_rec)))
df$group <- as.factor(df$group)

df$value[df$group=="SNP"] <- df$value[df$group=="SNP"]*thin #adjust parameter to account for thin argument

p_pi <- ggplot(df, aes(x=niter, y=value, group=group)) +
  geom_line(aes(color=group)) +
  geom_point(aes(color=group)) +
  xlab("Iteration") + ylab(bquote(pi)) +
  ggtitle("Prior mean") +
  theme_cowplot()

df <- data.frame(niter = rep(1:ncol(group_prior_var_rec), 2),
                 value = c(group_prior_var_rec[1,], group_prior_var_rec[2,]),
                 group = rep(c("Gene", "SNP"), each = ncol(group_prior_var_rec)))
df$group <- as.factor(df$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
  geom_line(aes(color=group)) +
  geom_point(aes(color=group)) +
  xlab("Iteration") + ylab(bquote(sigma^2)) +
  ggtitle("Prior variance") +
  theme_cowplot()

plot_grid(p_pi, p_sigma2)

Version Author Date
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#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 
0.0208487453 0.0001927855 
#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 
9.646456 8.679052 
#report sample size
print(sample_size)
[1] 1030836
#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)
[1]   10862 6839050
#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 
0.00211918 0.01110076 
#compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))
[1] 0.01225055 0.12523867
#distribution of PIPs
hist(ctwas_gene_res$susie_pip, xlim=c(0,1), main="Distribution of Gene PIPs")

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#genes with PIP>0.8 or 20 highest PIPs
head(ctwas_gene_res[order(-ctwas_gene_res$susie_pip),report_cols], max(sum(ctwas_gene_res$susie_pip>0.8), 20))
          genename region_tag susie_pip        mu2          PVE         z
2293          CAV1       7_70 1.0000000  622.84815 6.042165e-04 15.567870
3275         PRRX1       1_84 0.9998808  119.34663 1.157627e-04 14.667578
4009           DEK       6_14 0.9925129   63.47784 6.111795e-05 -9.000000
3527         CCND2       12_4 0.9906174   27.01637 2.596231e-05 -5.283784
1310           PXN      12_75 0.9826498   29.47170 2.809405e-05 -5.328302
12836 RP11-325L7.2       5_82 0.9826020 1993.73789 1.900449e-03 12.356322
9257         AGAP5      10_49 0.9779202   48.91516 4.640420e-05 11.518590
3523         KLF12      13_36 0.9770829   26.00017 2.464439e-05 -5.072464
6621         AKAP6       14_8 0.9721549   76.81846 7.244551e-05 -9.197368
6914          JAM2       21_9 0.9640543   22.19279 2.075505e-05  4.563232
9639         DLEU1      13_21 0.9586468   23.59091 2.193885e-05  4.697095
11818         DPF3      14_34 0.9574312   33.35512 3.097993e-05  6.264960
10521       FAM43A      3_120 0.9569176   29.69898 2.756935e-05 -5.487179
2444       SEC23IP      10_74 0.9517809   22.38862 2.067163e-05 -4.565228
13075    LINC01629      14_36 0.9471613   31.88580 2.929757e-05 -5.695652
2138           AES       19_4 0.9403219   20.20438 1.843030e-05  4.182804
4658        POPDC3       6_70 0.9252511   25.19514 2.261449e-05 -4.758170
10290       NKX2-5      5_103 0.9228745   61.55975 5.511247e-05 -9.391892
7515       TNFSF13       17_7 0.9135188   34.26969 3.036953e-05 -5.883117
5185          GYPC       2_74 0.9068050   38.95864 3.427111e-05 -6.380531
8248         CMTM5       14_3 0.9067300   30.09803 2.647442e-05 -5.472727
10548       SCN10A       3_28 0.8867624   77.72022 6.685774e-05 -8.814286
13967 RP5-890E16.5      17_28 0.8660551   23.11200 1.941751e-05 -4.761194
712          SP100      2_135 0.8658723   18.73540 1.573719e-05 -3.671335
9012         MTSS1       8_82 0.8594108   20.87861 1.740655e-05  4.402634
8992          MURC       9_50 0.8478869   23.34736 1.920376e-05  4.911964
5223         PSMB7       9_64 0.8430652   25.30057 2.069197e-05 -4.820896
6114       STK11IP      2_130 0.8406381   18.96825 1.546845e-05 -3.868022
10416          PGP       16_2 0.8298586   28.51218 2.295329e-05  5.943820
9691           BOK      2_144 0.8255975   19.18259 1.536335e-05  3.910125
8420          MARS      12_36 0.8180336   17.81958 1.414097e-05 -3.366197
3088          GNB4      3_110 0.8097696   30.34628 2.383842e-05 -5.583333
      num_eqtl
2293         3
3275         2
4009         1
3527         1
1310         1
12836        1
9257         2
3523         1
6621         1
6914         2
9639         1
11818        3
10521        1
2444         2
13075        1
2138         3
4658         1
10290        1
7515         1
5185         1
8248         1
10548        1
13967        1
712          2
9012         2
8992         2
5223         1
6114         2
10416        1
9691         3
8420         1
3088         1

Genes with largest effect sizes

#plot PIP vs effect size
plot(ctwas_gene_res$susie_pip, ctwas_gene_res$mu2, xlab="PIP", ylab="mu^2", main="Gene PIPs vs Effect Size")

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#genes with 20 largest effect sizes
head(ctwas_gene_res[order(-ctwas_gene_res$mu2),report_cols],10)
          genename region_tag    susie_pip       mu2          PVE         z
3250         WIPF1      2_105 0.000000e+00 2852.2491 0.000000e+00  8.441558
12836 RP11-325L7.2       5_82 9.826020e-01 1993.7379 1.900449e-03 12.356322
1487         SIRT1      10_44 3.337242e-01 1299.2406 4.206179e-04 -5.053846
7413          IL6R       1_75 5.862422e-12 1273.1807 7.240649e-15 -4.978495
6444         HERC4      10_44 1.013394e-01 1220.9395 1.200281e-04 -5.359560
11106         NACA      12_35 5.127731e-03 1179.0569 5.865032e-06 -6.240809
960          BAZ2A      12_35 2.047295e-03 1162.5562 2.308898e-06 -5.942857
7986       SLC35A1       6_59 0.000000e+00  804.8763 0.000000e+00 -5.072464
2507          WNT3      17_27 3.707228e-05  797.4584 2.867925e-08 -4.389402
2292          CAV2       7_70 0.000000e+00  689.1121 0.000000e+00 14.534943
      num_eqtl
3250         1
12836        1
1487         1
7413         1
6444         2
11106        2
960          1
7986         1
2507         2
2292         2

Genes with highest PVE

#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],10)
          genename region_tag susie_pip        mu2          PVE          z
12836 RP11-325L7.2       5_82 0.9826020 1993.73789 1.900449e-03  12.356322
2293          CAV1       7_70 1.0000000  622.84815 6.042165e-04  15.567870
1487         SIRT1      10_44 0.3337242 1299.24064 4.206179e-04  -5.053846
6444         HERC4      10_44 0.1013394 1220.93950 1.200281e-04  -5.359560
3275         PRRX1       1_84 0.9998808  119.34663 1.157627e-04  14.667578
6621         AKAP6       14_8 0.9721549   76.81846 7.244551e-05  -9.197368
10548       SCN10A       3_28 0.8867624   77.72022 6.685774e-05  -8.814286
12013       ZSWIM8      10_49 0.7522384   87.25963 6.367652e-05 -11.216495
4009           DEK       6_14 0.9925129   63.47784 6.111795e-05  -9.000000
8298       SYNPO2L      10_49 0.6265676   99.72467 6.061512e-05 -11.945652
      num_eqtl
12836        1
2293         3
1487         1
6444         2
3275         2
6621         1
10548        1
12013        1
4009         1
8298         1

Genes with largest z scores

#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],10)
          genename region_tag    susie_pip        mu2          PVE         z
2293          CAV1       7_70 1.000000e+00  622.84815 6.042165e-04  15.56787
3275         PRRX1       1_84 9.998808e-01  119.34663 1.157627e-04  14.66758
2292          CAV2       7_70 0.000000e+00  689.11215 0.000000e+00  14.53494
12836 RP11-325L7.2       5_82 9.826020e-01 1993.73789 1.900449e-03  12.35632
7729          PMVK       1_76 9.693335e-05  161.14638 1.515319e-08 -12.10294
8298       SYNPO2L      10_49 6.265676e-01   99.72467 6.061512e-05 -11.94565
7730        PBXIP1       1_76 9.335873e-05  156.14141 1.414111e-08 -11.86765
9805         MYOZ1      10_49 3.497772e-03   88.98387 3.019349e-07  11.75935
9257         AGAP5      10_49 9.779202e-01   48.91516 4.640420e-05  11.51859
12013       ZSWIM8      10_49 7.522384e-01   87.25963 6.367652e-05 -11.21649
      num_eqtl
2293         3
3275         2
2292         2
12836        1
7729         1
8298         1
7730         1
9805         2
9257         2
12013        1

Comparing z scores and PIPs

#set nominal signifiance threshold for z scores
alpha <- 0.05

#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)

#Q-Q plot for z scores
obs_z <- ctwas_gene_res$z[order(ctwas_gene_res$z)]
exp_z <- qnorm((1:nrow(ctwas_gene_res))/nrow(ctwas_gene_res))

plot(exp_z, obs_z, xlab="Expected z", ylab="Observed z", main="Gene z score Q-Q plot")
abline(a=0,b=1)

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#plot z score vs PIP
plot(abs(ctwas_gene_res$z), ctwas_gene_res$susie_pip, xlab="abs(z)", ylab="PIP")
abline(v=sig_thresh, col="red", lty=2)

Version Author Date
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#proportion of significant z scores
mean(abs(ctwas_gene_res$z) > sig_thresh)
[1] 0.008654023
#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20)
          genename region_tag    susie_pip        mu2          PVE          z
2293          CAV1       7_70 1.000000e+00  622.84815 6.042165e-04  15.567870
3275         PRRX1       1_84 9.998808e-01  119.34663 1.157627e-04  14.667578
2292          CAV2       7_70 0.000000e+00  689.11215 0.000000e+00  14.534943
12836 RP11-325L7.2       5_82 9.826020e-01 1993.73789 1.900449e-03  12.356322
7729          PMVK       1_76 9.693335e-05  161.14638 1.515319e-08 -12.102941
8298       SYNPO2L      10_49 6.265676e-01   99.72467 6.061512e-05 -11.945652
7730        PBXIP1       1_76 9.335873e-05  156.14141 1.414111e-08 -11.867647
9805         MYOZ1      10_49 3.497772e-03   88.98387 3.019349e-07  11.759348
9257         AGAP5      10_49 9.779202e-01   48.91516 4.640420e-05  11.518590
12013       ZSWIM8      10_49 7.522384e-01   87.25963 6.367652e-05 -11.216495
6395        C9orf3       9_48 4.919856e-02   94.74228 4.521751e-06  10.671642
7407        ZBTB7B       1_76 9.636563e-05  122.09475 1.141378e-08  10.638889
13556   RP1-79C4.4       1_84 8.254209e-03   58.95140 4.720414e-07   9.586404
10290       NKX2-5      5_103 9.228745e-01   61.55975 5.511247e-05  -9.391892
9719        SEC24C      10_49 9.597200e-04   46.26092 4.306945e-08   9.271945
6621         AKAP6       14_8 9.721549e-01   76.81846 7.244551e-05  -9.197368
4009           DEK       6_14 9.925129e-01   63.47784 6.111795e-05  -9.000000
10548       SCN10A       3_28 8.867624e-01   77.72022 6.685774e-05  -8.814286
3665         KCNJ5      11_80 5.037532e-01   67.73277 3.309993e-05  -8.748092
7733         DCST2       1_76 2.613168e-04   89.76014 2.275418e-08  -8.718310
      num_eqtl
2293         3
3275         2
2292         2
12836        1
7729         1
8298         1
7730         1
9805         2
9257         2
12013        1
6395         1
7407         1
13556        2
10290        1
9719         2
6621         1
4009         1
10548        1
3665         1
7733         1
library(tibble)
library(tidyverse)
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✔ tidyr   1.1.4     ✔ dplyr   1.0.7
✔ readr   2.1.1     ✔ stringr 1.4.0
✔ purrr   0.3.4     ✔ forcats 0.5.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
full.gene.pip.summary <- data.frame(gene_name = ctwas_gene_res$genename, 
                                    gene_pip = ctwas_gene_res$susie_pip, 
                                    gene_id = ctwas_gene_res$id, 
                                    chr = as.integer(ctwas_gene_res$chrom),
                                    start = ctwas_gene_res$pos / 1e3,
                                    is_highlight = F, stringsAsFactors = F) %>% as_tibble()
full.gene.pip.summary$is_highlight <- full.gene.pip.summary$gene_pip > 0.80

don <- full.gene.pip.summary %>% 
  
  # Compute chromosome size
  group_by(chr) %>% 
  summarise(chr_len=max(start)) %>% 
  
  # Calculate cumulative position of each chromosome
  mutate(tot=cumsum(chr_len)-chr_len) %>%
  dplyr::select(-chr_len) %>%
  
  # Add this info to the initial dataset
  left_join(full.gene.pip.summary, ., by=c("chr"="chr")) %>%
  
  # Add a cumulative position of each SNP
  arrange(chr, start) %>%
  mutate( BPcum=start+tot)

axisdf <- don %>% group_by(chr) %>% summarize(center=( max(BPcum) + min(BPcum) ) / 2 )

x_axis_labels <- axisdf$chr
x_axis_labels[seq(1,21,2)] <- ""

ggplot(don, aes(x=BPcum, y=gene_pip)) +
  
  # Show all points
  ggrastr::geom_point_rast(aes(color=as.factor(chr)), size=2) +
  scale_color_manual(values = rep(c("grey", "skyblue"), 22 )) +
  
  # custom X axis:
  # scale_x_continuous(label = axisdf$chr, 
  #                    breaks= axisdf$center,
  #                    guide = guide_axis(n.dodge = 2)) +
  scale_x_continuous(label = x_axis_labels,
                     breaks = axisdf$center) +
  
  scale_y_continuous(expand = c(0, 0), limits = c(0,1.25), breaks=(1:5)*0.2, minor_breaks=(1:10)*0.1) + # remove space between plot area and x axis
  
  # Add highlighted points
  ggrastr::geom_point_rast(data=subset(don, is_highlight==T), color="orange", size=2) +
  
  # Add label using ggrepel to avoid overlapping
  ggrepel::geom_label_repel(data=subset(don, is_highlight==T), 
                            aes(label=gene_name), 
                            size=4,
                            min.segment.length = 0, 
                            label.size = NA,
                            fill = alpha(c("white"),0)) +
  
  # Custom the theme:
  theme_bw() +
  theme( 
    text = element_text(size = 14),
    legend.position="none",
    panel.border = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank()
  ) +
  xlab("Chromosome") + 
  ylab("cTWAS PIP")

Version Author Date
e080e7b sq-96 2021-12-20
3052d49 sq-96 2021-12-20

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] forcats_0.5.1   stringr_1.4.0   dplyr_1.0.7     purrr_0.3.4    
 [5] readr_2.1.1     tidyr_1.1.4     tidyverse_1.3.1 tibble_3.1.6   
 [9] cowplot_1.0.0   ggplot2_3.3.5   workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2        bit64_4.0.5       jsonlite_1.7.2    modelr_0.1.8     
 [5] assertthat_0.2.1  highr_0.9         blob_1.2.2        vipor_0.4.5      
 [9] cellranger_1.1.0  yaml_2.2.1        ggrepel_0.8.1     pillar_1.6.4     
[13] RSQLite_2.2.8     backports_1.4.1   glue_1.5.1        digest_0.6.29    
[17] promises_1.0.1    rvest_1.0.2       colorspace_2.0-2  htmltools_0.5.2  
[21] httpuv_1.5.1      pkgconfig_2.0.3   broom_0.7.10      haven_2.4.3      
[25] scales_1.1.1      whisker_0.3-2     ggrastr_1.0.1     later_0.8.0      
[29] tzdb_0.2.0        git2r_0.26.1      generics_0.1.1    farver_2.1.0     
[33] ellipsis_0.3.2    cachem_1.0.6      withr_2.4.3       cli_3.1.0        
[37] magrittr_2.0.1    crayon_1.4.2      readxl_1.3.1      memoise_2.0.1    
[41] evaluate_0.14     fs_1.5.2          fansi_0.5.0       xml2_1.3.3       
[45] beeswarm_0.2.3    Cairo_1.5-12.2    tools_3.6.1       data.table_1.14.2
[49] hms_1.1.1         lifecycle_1.0.1   munsell_0.5.0     reprex_2.0.1     
[53] compiler_3.6.1    jquerylib_0.1.4   rlang_0.4.12      grid_3.6.1       
[57] rstudioapi_0.13   labeling_0.4.2    rmarkdown_2.11    gtable_0.3.0     
[61] DBI_1.1.1         R6_2.5.1          lubridate_1.8.0   knitr_1.36       
[65] fastmap_1.1.0     bit_4.0.4         utf8_1.2.2        rprojroot_2.0.2  
[69] stringi_1.7.6     ggbeeswarm_0.6.0  Rcpp_1.0.7        vctrs_0.3.8      
[73] dbplyr_2.1.1      tidyselect_1.1.1  xfun_0.29