Figure6_fullset_trans

workflowr

• Summary
• Checks
• Past versions

Last updated: 2020-11-24

Checks: 7 0

Knit directory: Workflowr_Array_GBRS/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20201114)

The command set.seed(20201114) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: fd893f5

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version fd893f5. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Untracked files:
    Untracked:  analysis/eqtl_with_perms/
    Untracked:  analysis/qtl2_data_process_GIGAMUGA358.R
    Untracked:  analysis/qtl2_data_process_MEGAMUGA184.R
    Untracked:  analysis/qtl2_data_process_MUGA275.R
    Untracked:  analysis/workflow_proc.R
    Untracked:  analysis/workflow_proc.err
    Untracked:  analysis/workflow_proc.out
    Untracked:  analysis/workflow_proc.sh
    Untracked:  code/GBRS2MUGA_sample_mismatch.R
    Untracked:  code/GBRS2MUGA_sample_mismatch_w.R
    Untracked:  code/cis_eqtl_scan.R
    Untracked:  code/combine.sample.aftermismatch.R
    Untracked:  code/comparison.R
    Untracked:  code/replace_ids.R
    Untracked:  code/violin_plot.R
    Untracked:  data/DO_Liver_sample_sheet.tsv
    Untracked:  data/DO_Striatum_sample_sheet.tsv
    Untracked:  data/marker_grid_0.02cM_plus.txt
    Untracked:  data/qtl2/
    Untracked:  output/DO_Striatum_recomb.GBRS.genotypes.RData
    Untracked:  output/DO_liver_recomb.GBRS.genotypes.RData
    Untracked:  output/DO_striatum_recomb.GBRS.genotypes.RData
    Untracked:  output/GBRS184.recomb.RData
    Untracked:  output/GBRS275.recomb.RData
    Untracked:  output/GBRS358.recomb.RData
    Untracked:  output/gene.anno.cis.RData
    Untracked:  output/gigamuga358.recomb.RData
    Untracked:  output/megamuga184.recomb.RData
    Untracked:  output/mismatch_autochr_gigamuga358GBRS.RData
    Untracked:  output/mismatch_autochr_megamuga184GBRS.RData
    Untracked:  output/mismatch_autochr_muga275GBRS.RData
    Untracked:  output/mismatch_gigamuga358GBRS.RData
    Untracked:  output/mismatch_megamuga184GBRS.RData
    Untracked:  output/mismatch_muga275GBRS.RData
    Untracked:  output/muga275.recomb.RData

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Figure6_fullset_trans.Rmd) and HTML (docs/Figure6_fullset_trans.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
html	2cd3a58	xhyuo	2020-11-15	Build site.
Rmd	c132b32	xhyuo	2020-11-15	First publish

---

library

library(ggplot2)
library(tidyverse)

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

✔ tibble  3.0.1     ✔ dplyr   1.0.0
✔ tidyr   1.1.0     ✔ stringr 1.4.0
✔ readr   1.4.0     ✔ forcats 0.5.0
✔ purrr   0.3.4     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(dplyr)
library(data.table)

Attaching package: 'data.table'

The following objects are masked from 'package:dplyr':

    between, first, last

The following object is masked from 'package:purrr':

    transpose

library(qvalue)
library(gridExtra)

Attaching package: 'gridExtra'

The following object is masked from 'package:dplyr':

    combine

library(plotly)

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

library(reshape2)

Attaching package: 'reshape2'

The following objects are masked from 'package:data.table':

    dcast, melt

The following object is masked from 'package:tidyr':

    smiths

library(MASS)

Attaching package: 'MASS'

The following object is masked from 'package:plotly':

    select

The following object is masked from 'package:dplyr':

    select

library(ggplot2)
library(viridis)

Loading required package: viridisLite

library(cowplot)
library(ggpubr)

Attaching package: 'ggpubr'

The following object is masked from 'package:cowplot':

    get_legend

library(furrr)

Loading required package: future

library(kableExtra)

Attaching package: 'kableExtra'

The following object is masked from 'package:dplyr':

    group_rows

#3 workers
# Set a "plan" for how the code should run.
plan(multisession, workers = 3)
load("output/gene.anno.cis.RData")
source("code/comparison.R")
options(knitr.kable.NA = '')

load all trans results

##meta data
meta.data <- c("muga275", "gbrs275", "fixed.muga275",
               "combine.fixed.muga275", "combine.fixed.gbrs275", 
               "megamuga184","gbrs184","fixed.megamuga184",
               "combine.fixed.megamuga184", "combine.fixed.gbrs184", 
               "gigamuga358","gbrs358","fixed.gigamuga358",
               "combine.fixed.gigamuga358", "combine.fixed.gbrs358")

##path
path <- c("muga275/", "gbrs275/", "fixed.muga275/",
          "combine.fixed/combine.fixed.muga275/", "combine.fixed/combine.fixed.gbrs275/", 
          "megamuga184/","gbrs184/","fixed.megamuga184/",
          "combine.fixed/combine.fixed.megamuga184/", "combine.fixed/combine.fixed.gbrs184/", 
          "gigamuga358/","gbrs358/","fixed.gigamuga358/",
          "combine.fixed/combine.fixed.gigamuga358/", "combine.fixed/combine.fixed.gbrs358/")
names(path) <- meta.data

#cutoff data
cutoff <- list()
pairs  <- list()
for (i in meta.data){
  print(i)
  b <- get(load(paste0("analysis/eqtl_with_perms/", path[[i]], i,"_pvalue.RData")))
  b <- setDT(b)[, .SD[which.max(lod)], by=genes]
  b$marker <- paste0(b$chr,"_",as.integer(b$pos))
  b$max_qvalue <- qvalue(p = b$pval)$qvalues
  b$eqtl_marker <- paste0(b$chr, "_", as.integer(b$pos))
  b <- merge(b, gene.anno, by.x=c("genes"), by.y=c("gene"), all.x = TRUE)
  b$cis_trans <- ifelse((b$chr.y == b$chr.x) & (abs(b$pos - b$start) <= 5e+06),"cis", "trans")
  b <- b[b$max_qvalue <= 0.05 & b$cis_trans == "trans",]
  cutoff[[i]] <- as.numeric(b[which.max(b$max_qvalue),"lod"])
  print(cutoff[[i]])
  pairs[[i]] <- b[,c("genes","eqtl_marker")] %>% mutate(across(c(genes, eqtl_marker),as.character)) %>% distinct()
}

[1] "muga275"
[1] 7.00561
[1] "gbrs275"
[1] 6.637
[1] "fixed.muga275"
[1] 6.642903
[1] "combine.fixed.muga275"
[1] 6.966156
[1] "combine.fixed.gbrs275"
[1] 6.448847
[1] "megamuga184"
[1] 7.877257
[1] "gbrs184"
[1] 6.733226
[1] "fixed.megamuga184"
[1] 6.937301
[1] "combine.fixed.megamuga184"
[1] 7.652161
[1] "combine.fixed.gbrs184"
[1] 6.672418
[1] "gigamuga358"
[1] 7.477519
[1] "gbrs358"
[1] 7.081477
[1] "fixed.gigamuga358"
[1] 7.045437
[1] "combine.fixed.gigamuga358"
[1] 7.268445
[1] "combine.fixed.gbrs358"
[1] 7.11942

Figure6_beforefixedarray_vs_GBRS

#parameters
xi      <- c("gbrs275", "gbrs184", "gbrs358")
yi      <- c("muga275", "megamuga184",  "gigamuga358")

x         <- pairs[xi]
name_xi   <- list("GBRS","GBRS", "GBRS")
cutoff_x  <- cutoff[xi]
y         <- pairs[yi]
name_yi   <- list("MUGA","MEGAMUGA", "GIGAMUGA")
cutoff_y  <- cutoff[yi]
color     <- list("#bdd7e7", "#6baed6", "#2171b5")

#args
args <- list(xi       = xi,
             x        = x,
             name_xi  = name_xi,
             cutoff_x = cutoff_x,
             yi       = yi,
             y        = y,
             name_yi  = name_yi,
             cutoff_y = cutoff_y,
             color    = color)

results1 <- args %>% future_pmap(comparison_trans)
names(results1) <- paste0(name_yi, "_VS_", name_xi)
print(results1)

$MUGA_VS_GBRS
$MUGA_VS_GBRS$info
[1] "x:GBRS VS y:MUGA"

$MUGA_VS_GBRS$z

Both    x    y 
1509 2010  636 

$MUGA_VS_GBRS$down_up

Down   Up 
3158  997 

$MUGA_VS_GBRS$d1_mean
# A tibble: 2 x 4
  down_up mean_lodx mean_lody  diff
  <chr>       <dbl>     <dbl> <dbl>
1 Down        13.9       9.40  4.49
2 Up           6.47      8.34 -1.87

$MUGA_VS_GBRS$slope
(Intercept)       lod.x 
  2.0760329   0.5835706 

$MUGA_VS_GBRS$plot

Past versions of Figure6_beforefixedarray_vs_GBRS-1.png

Version	Author	Date
2cd3a58	xhyuo	2020-11-15

$MEGAMUGA_VS_GBRS
$MEGAMUGA_VS_GBRS$info
[1] "x:GBRS VS y:MEGAMUGA"

$MEGAMUGA_VS_GBRS$z

Both    x    y 
1044 3040  357 

$MEGAMUGA_VS_GBRS$down_up

Down   Up 
3605  836 

$MEGAMUGA_VS_GBRS$d1_mean
# A tibble: 2 x 4
  down_up mean_lodx mean_lody  diff
  <chr>       <dbl>     <dbl> <dbl>
1 Down        10.3       7.36  2.90
2 Up           6.89      8.73 -1.85

$MEGAMUGA_VS_GBRS$slope
(Intercept)       lod.x 
  1.5618024   0.6290887 

$MEGAMUGA_VS_GBRS$plot

Past versions of Figure6_beforefixedarray_vs_GBRS-2.png

Version	Author	Date
2cd3a58	xhyuo	2020-11-15

$GIGAMUGA_VS_GBRS
$GIGAMUGA_VS_GBRS$info
[1] "x:GBRS VS y:GIGAMUGA"

$GIGAMUGA_VS_GBRS$z

Both    x    y 
1383 2450  763 

$GIGAMUGA_VS_GBRS$down_up

Down   Up 
3581 1015 

$GIGAMUGA_VS_GBRS$d1_mean
# A tibble: 2 x 4
  down_up mean_lodx mean_lody  diff
  <chr>       <dbl>     <dbl> <dbl>
1 Down        15.5      10.4   5.02
2 Up           6.48      9.45 -2.97

$GIGAMUGA_VS_GBRS$slope
(Intercept)       lod.x 
  1.7869691   0.6254573 

$GIGAMUGA_VS_GBRS$plot

Past versions of Figure6_beforefixedarray_vs_GBRS-3.png

Version	Author	Date
2cd3a58	xhyuo	2020-11-15

#table
df1 <- results_sum(results1)
df1 %>%
  kbl(caption = "Summary on beforefixedarray_vs_GBRS", digits = 3, align = "l") %>%
  kable_styling()

Summary on beforefixedarray_vs_GBRS

Infor	Var1	Freq	Down_Up	Freq	mean_lodx	mean_lody	diff	Intercept	Slope
x:GBRS VS y:MUGA	Both	1509						2.076	0.584
	x	2010	Down	3158	13.889	9.396	4.493
	y	636	Up	997	6.465	8.335	-1.870
x:GBRS VS y:MEGAMUGA	Both	1044						1.562	0.629
	x	3040	Down	3605	10.257	7.356	2.901
	y	357	Up	836	6.885	8.731	-1.846
x:GBRS VS y:GIGAMUGA	Both	1383						1.787	0.625
	x	2450	Down	3581	15.455	10.431	5.024
	y	763	Up	1015	6.478	9.447	-2.969

Figure6_fixedarray_vs_GBRS

#parameters
xi      <- c("gbrs275", "gbrs184", "gbrs358")
yi      <- c("fixed.muga275", "fixed.megamuga184",  "fixed.gigamuga358")

x         <- pairs[xi]
name_xi   <- list("GBRS","GBRS", "GBRS")
cutoff_x  <- cutoff[xi]
y         <- pairs[yi]
name_yi   <- list("FIXED.MUGA","FIXED.MEGAMUGA", "FIXED.GIGAMUGA")
cutoff_y  <- cutoff[yi]
color     <- list("#bdd7e7", "#6baed6", "#2171b5")

#args
args <- list(xi       = xi,
             x        = x,
             name_xi  = name_xi,
             cutoff_x = cutoff_x,
             yi       = yi,
             y        = y,
             name_yi  = name_yi,
             cutoff_y = cutoff_y,
             color    = color)

results2 <- args %>% future_pmap(comparison_trans)
names(results2) <- paste0(name_yi, "_VS_", name_xi)
print(results2)

$FIXED.MUGA_VS_GBRS
$FIXED.MUGA_VS_GBRS$info
[1] "x:GBRS VS y:FIXED.MUGA"

$FIXED.MUGA_VS_GBRS$z

Both    x    y 
2319 1495 1181 

$FIXED.MUGA_VS_GBRS$down_up

Down   Up 
2843 2152 

$FIXED.MUGA_VS_GBRS$d1_mean
# A tibble: 2 x 4
  down_up mean_lodx mean_lody  diff
  <chr>       <dbl>     <dbl> <dbl>
1 Down        13.4      10.7   2.73
2 Up           8.10      9.46 -1.36

$FIXED.MUGA_VS_GBRS$slope
(Intercept)       lod.x 
  1.2117747   0.8038834 

$FIXED.MUGA_VS_GBRS$plot

Past versions of Figure6_fixedarray_vs_GBRS-1.png

Version	Author	Date
2cd3a58	xhyuo	2020-11-15

$FIXED.MEGAMUGA_VS_GBRS
$FIXED.MEGAMUGA_VS_GBRS$info
[1] "x:GBRS VS y:FIXED.MEGAMUGA"

$FIXED.MEGAMUGA_VS_GBRS$z

Both    x    y 
2886 2069 1821 

$FIXED.MEGAMUGA_VS_GBRS$down_up

Down   Up 
3341 3435 

$FIXED.MEGAMUGA_VS_GBRS$d1_mean
# A tibble: 2 x 4
  down_up mean_lodx mean_lody  diff
  <chr>       <dbl>     <dbl> <dbl>
1 Down         9.79      8.26  1.52
2 Up           7.17      8.65 -1.49

$FIXED.MEGAMUGA_VS_GBRS$slope
(Intercept)       lod.x 
  1.0907960   0.8712699 

$FIXED.MEGAMUGA_VS_GBRS$plot

Past versions of Figure6_fixedarray_vs_GBRS-2.png

Version	Author	Date
2cd3a58	xhyuo	2020-11-15

$FIXED.GIGAMUGA_VS_GBRS
$FIXED.GIGAMUGA_VS_GBRS$info
[1] "x:GBRS VS y:FIXED.GIGAMUGA"

$FIXED.GIGAMUGA_VS_GBRS$z

Both    x    y 
2395 1995 1422 

$FIXED.GIGAMUGA_VS_GBRS$down_up

Down   Up 
3796 2016 

$FIXED.GIGAMUGA_VS_GBRS$d1_mean
# A tibble: 2 x 4
  down_up mean_lodx mean_lody  diff
  <chr>       <dbl>     <dbl> <dbl>
1 Down        14.3       11.5  2.76
2 Up           7.75      10.6 -2.81

$FIXED.GIGAMUGA_VS_GBRS$slope
(Intercept)       lod.x 
  1.2334781   0.8284717 

$FIXED.GIGAMUGA_VS_GBRS$plot

Past versions of Figure6_fixedarray_vs_GBRS-3.png

Version	Author	Date
2cd3a58	xhyuo	2020-11-15

#table
df2 <- results_sum(results2)
df2 %>%
  kbl(caption = "Summary on fixedarray_vs_GBRS", digits = 3, align = "l") %>%
  kable_styling()

Summary on fixedarray_vs_GBRS

Infor	Var1	Freq	Down_Up	Freq	mean_lodx	mean_lody	diff	Intercept	Slope
x:GBRS VS y:FIXED.MUGA	Both	2319						1.212	0.804
	x	1495	Down	2843	13.409	10.677	2.732
	y	1181	Up	2152	8.099	9.459	-1.360
x:GBRS VS y:FIXED.MEGAMUGA	Both	2886						1.091	0.871
	x	2069	Down	3341	9.785	8.261	1.524
	y	1821	Up	3435	7.166	8.652	-1.486
x:GBRS VS y:FIXED.GIGAMUGA	Both	2395						1.233	0.828
	x	1995	Down	3796	14.277	11.517	2.760
	y	1422	Up	2016	7.749	10.562	-2.813

Figure6_combine_vs_GBRS

#parameters
xi      <- c("combine.fixed.gbrs275", "combine.fixed.gbrs184",      "combine.fixed.gbrs358")
yi      <- c("combine.fixed.muga275", "combine.fixed.megamuga184",  "combine.fixed.gigamuga358")

x         <- pairs[xi]
name_xi   <- list("GBRS","GBRS", "GBRS")
cutoff_x  <- cutoff[xi]
y         <- pairs[yi]
name_yi   <- list("COMBINE.MUGA","COMBINE.MEGAMUGA", "COMBINE.GIGAMUGA")
cutoff_y  <- cutoff[yi]
color     <- list("#bdd7e7", "#6baed6", "#2171b5")

#args
args <- list(xi       = xi,
             x        = x,
             name_xi  = name_xi,
             cutoff_x = cutoff_x,
             yi       = yi,
             y        = y,
             name_yi  = name_yi,
             cutoff_y = cutoff_y,
             color    = color)

results3 <- args %>% future_pmap(comparison_trans)
names(results3) <- paste0(name_yi, "_VS_", name_xi)
print(results3)

$COMBINE.MUGA_VS_GBRS
$COMBINE.MUGA_VS_GBRS$info
[1] "x:GBRS VS y:COMBINE.MUGA"

$COMBINE.MUGA_VS_GBRS$z

Both    x    y 
2337 2125  779 

$COMBINE.MUGA_VS_GBRS$down_up

Down   Up 
3164 2077 

$COMBINE.MUGA_VS_GBRS$d1_mean
# A tibble: 2 x 4
  down_up mean_lodx mean_lody  diff
  <chr>       <dbl>     <dbl> <dbl>
1 Down        12.1       9.92  2.21
2 Up           8.85     10.1  -1.21

$COMBINE.MUGA_VS_GBRS$slope
(Intercept)       lod.x 
  0.7887601   0.8484628 

$COMBINE.MUGA_VS_GBRS$plot

Past versions of Figure6_combine_vs_GBRS-1.png

Version	Author	Date
2cd3a58	xhyuo	2020-11-15

$COMBINE.MEGAMUGA_VS_GBRS
$COMBINE.MEGAMUGA_VS_GBRS$info
[1] "x:GBRS VS y:COMBINE.MEGAMUGA"

$COMBINE.MEGAMUGA_VS_GBRS$z

Both    x    y 
1937 2736  364 

$COMBINE.MEGAMUGA_VS_GBRS$down_up

Down   Up 
3258 1779 

$COMBINE.MEGAMUGA_VS_GBRS$d1_mean
# A tibble: 2 x 4
  down_up mean_lodx mean_lody  diff
  <chr>       <dbl>     <dbl> <dbl>
1 Down         9.48      8.18  1.30
2 Up           8.95     10.0  -1.07

$COMBINE.MEGAMUGA_VS_GBRS$slope
(Intercept)       lod.x 
  0.3510211   0.9122205 

$COMBINE.MEGAMUGA_VS_GBRS$plot

Past versions of Figure6_combine_vs_GBRS-2.png

Version	Author	Date
2cd3a58	xhyuo	2020-11-15

$COMBINE.GIGAMUGA_VS_GBRS
$COMBINE.GIGAMUGA_VS_GBRS$info
[1] "x:GBRS VS y:COMBINE.GIGAMUGA"

$COMBINE.GIGAMUGA_VS_GBRS$z

Both    x    y 
3506 1367 1280 

$COMBINE.GIGAMUGA_VS_GBRS$down_up

Down   Up 
3259 2894 

$COMBINE.GIGAMUGA_VS_GBRS$d1_mean
# A tibble: 2 x 4
  down_up mean_lodx mean_lody  diff
  <chr>       <dbl>     <dbl> <dbl>
1 Down        14.2       12.3  1.94
2 Up           9.03      10.3 -1.30

$COMBINE.GIGAMUGA_VS_GBRS$slope
(Intercept)       lod.x 
  1.2222301   0.8610328 

$COMBINE.GIGAMUGA_VS_GBRS$plot

Past versions of Figure6_combine_vs_GBRS-3.png

Version	Author	Date
2cd3a58	xhyuo	2020-11-15

#table
df3 <- results_sum(results3)
df3 %>%
  kbl(caption = "Summary on combine_vs_GBRS", digits = 3, align = "l") %>%
  kable_styling()

Summary on combine_vs_GBRS

Infor	Var1	Freq	Down_Up	Freq	mean_lodx	mean_lody	diff	Intercept	Slope
x:GBRS VS y:COMBINE.MUGA	Both	2337						0.789	0.848
	x	2125	Down	3164	12.130	9.921	2.209
	y	779	Up	2077	8.855	10.068	-1.214
x:GBRS VS y:COMBINE.MEGAMUGA	Both	1937						0.351	0.912
	x	2736	Down	3258	9.480	8.178	1.302
	y	364	Up	1779	8.946	10.016	-1.070
x:GBRS VS y:COMBINE.GIGAMUGA	Both	3506						1.222	0.861
	x	1367	Down	3259	14.243	12.304	1.938
	y	1280	Up	2894	9.026	10.325	-1.298

Figure6_beforefixedarray_vs_combined

#parameters
xi      <- c("combine.fixed.muga275", "combine.fixed.megamuga184",  "combine.fixed.gigamuga358")
yi      <- c("muga275",               "megamuga184",                "gigamuga358")

x         <- pairs[xi]
name_xi   <- list("COMBINE","COMBINE", "COMBINE")
cutoff_x  <- cutoff[xi]
y         <- pairs[yi]
name_yi   <- list("MUGA","MEGAMUGA", "GIGAMUGA")
cutoff_y  <- cutoff[yi]
color     <- list("#bdd7e7", "#6baed6", "#2171b5")

#args
args <- list(xi       = xi,
             x        = x,
             name_xi  = name_xi,
             cutoff_x = cutoff_x,
             yi       = yi,
             y        = y,
             name_yi  = name_yi,
             cutoff_y = cutoff_y,
             color    = color)

results4 <- args %>% future_pmap(comparison_trans)
names(results4) <- paste0(name_yi, "_VS_", name_xi)
print(results4)

$MUGA_VS_COMBINE
$MUGA_VS_COMBINE$info
[1] "x:COMBINE VS y:MUGA"

$MUGA_VS_COMBINE$z

Both    x    y 
1631 1314  601 

$MUGA_VS_COMBINE$down_up

Down   Up 
2576  970 

$MUGA_VS_COMBINE$d1_mean
# A tibble: 2 x 4
  down_up mean_lodx mean_lody  diff
  <chr>       <dbl>     <dbl> <dbl>
1 Down        14.3      11.0   3.28
2 Up           7.26      8.68 -1.41

$MUGA_VS_COMBINE$slope
(Intercept)       lod.x 
  1.5656899   0.7124451 

$MUGA_VS_COMBINE$plot

Past versions of Figure6_beforefixedarray_vs_combined-1.png

Version	Author	Date
2cd3a58	xhyuo	2020-11-15

$MEGAMUGA_VS_COMBINE
$MEGAMUGA_VS_COMBINE$info
[1] "x:COMBINE VS y:MEGAMUGA"

$MEGAMUGA_VS_COMBINE$z

Both    x    y 
1008 1020  408 

$MEGAMUGA_VS_COMBINE$down_up

Down   Up 
1739  697 

$MEGAMUGA_VS_COMBINE$d1_mean
# A tibble: 2 x 4
  down_up mean_lodx mean_lody  diff
  <chr>       <dbl>     <dbl> <dbl>
1 Down        12.9       9.82  3.03
2 Up           7.74      9.40 -1.66

$MEGAMUGA_VS_COMBINE$slope
(Intercept)       lod.x 
  2.0429012   0.6721594 

$MEGAMUGA_VS_COMBINE$plot

Past versions of Figure6_beforefixedarray_vs_combined-2.png

Version	Author	Date
2cd3a58	xhyuo	2020-11-15

$GIGAMUGA_VS_COMBINE
$GIGAMUGA_VS_COMBINE$info
[1] "x:COMBINE VS y:GIGAMUGA"

$GIGAMUGA_VS_COMBINE$z

Both    x    y 
1463 2603  741 

$GIGAMUGA_VS_COMBINE$down_up

Down   Up 
3729 1078 

$GIGAMUGA_VS_COMBINE$d1_mean
# A tibble: 2 x 4
  down_up mean_lodx mean_lody  diff
  <chr>       <dbl>     <dbl> <dbl>
1 Down        13.9       10.2  3.74
2 Up           8.10      10.1 -2.03

$GIGAMUGA_VS_COMBINE$slope
(Intercept)       lod.x 
  0.7712244   0.7449011 

$GIGAMUGA_VS_COMBINE$plot

Past versions of Figure6_beforefixedarray_vs_combined-3.png

Version	Author	Date
2cd3a58	xhyuo	2020-11-15

#table
df4 <- results_sum(results4)
df4 %>%
  kbl(caption = "Summary on beforefixedarray_vs_combined", digits = 3, align = "l") %>%
  kable_styling()

Summary on beforefixedarray_vs_combined

Infor	Var1	Freq	Down_Up	Freq	mean_lodx	mean_lody	diff	Intercept	Slope
x:COMBINE VS y:MUGA	Both	1631						1.566	0.712
	x	1314	Down	2576	14.318	11.038	3.280
	y	601	Up	970	7.264	8.675	-1.411
x:COMBINE VS y:MEGAMUGA	Both	1008						2.043	0.672
	x	1020	Down	1739	12.856	9.822	3.034
	y	408	Up	697	7.740	9.396	-1.656
x:COMBINE VS y:GIGAMUGA	Both	1463						0.771	0.745
	x	2603	Down	3729	13.926	10.184	3.743
	y	741	Up	1078	8.096	10.128	-2.031

Figure6_fixedarray_vs_combined

#parameters
xi      <- c("combine.fixed.muga275", "combine.fixed.megamuga184",  "combine.fixed.gigamuga358")
yi      <- c("fixed.muga275",         "fixed.megamuga184",          "fixed.gigamuga358")

x         <- pairs[xi]
name_xi   <- list("COMBINE","COMBINE", "COMBINE")
cutoff_x  <- cutoff[xi]
y         <- pairs[yi]
name_yi   <- list("FIXED.MUGA","FIXED.MEGAMUGA", "FIXED.GIGAMUGA")
cutoff_y  <- cutoff[yi]
color     <- list("#bdd7e7", "#6baed6", "#2171b5")

#args
args <- list(xi       = xi,
             x        = x,
             name_xi  = name_xi,
             cutoff_x = cutoff_x,
             yi       = yi,
             y        = y,
             name_yi  = name_yi,
             cutoff_y = cutoff_y,
             color    = color)

results5 <- args %>% future_pmap(comparison_trans)
names(results5) <- paste0(name_yi, "_VS_", name_xi)
print(results5)

$FIXED.MUGA_VS_COMBINE
$FIXED.MUGA_VS_COMBINE$info
[1] "x:COMBINE VS y:FIXED.MUGA"

$FIXED.MUGA_VS_COMBINE$z

Both    x    y 
2475  717 1151 

$FIXED.MUGA_VS_COMBINE$down_up

Down   Up 
2196 2147 

$FIXED.MUGA_VS_COMBINE$d1_mean
# A tibble: 2 x 4
  down_up mean_lodx mean_lody  diff
  <chr>       <dbl>     <dbl> <dbl>
1 Down         11.8      10.7  1.14
2 Up           10.8      11.9 -1.09

$FIXED.MUGA_VS_COMBINE$slope
(Intercept)       lod.x 
 0.01717572  0.99555914 

$FIXED.MUGA_VS_COMBINE$plot

Past versions of Figure6_fixedarray_vs_combined-1.png

Version	Author	Date
2cd3a58	xhyuo	2020-11-15

$FIXED.MEGAMUGA_VS_COMBINE
$FIXED.MEGAMUGA_VS_COMBINE$info
[1] "x:COMBINE VS y:FIXED.MEGAMUGA"

$FIXED.MEGAMUGA_VS_COMBINE$z

Both    x    y 
2098  293 2318 

$FIXED.MEGAMUGA_VS_COMBINE$down_up

Down   Up 
1652 3057 

$FIXED.MEGAMUGA_VS_COMBINE$d1_mean
# A tibble: 2 x 4
  down_up mean_lodx mean_lody  diff
  <chr>       <dbl>     <dbl> <dbl>
1 Down        11.0       9.98  1.01
2 Up           8.16      9.38 -1.22

$FIXED.MEGAMUGA_VS_COMBINE$slope
(Intercept)       lod.x 
  0.6585100   0.9755993 

$FIXED.MEGAMUGA_VS_COMBINE$plot

Past versions of Figure6_fixedarray_vs_combined-2.png

Version	Author	Date
2cd3a58	xhyuo	2020-11-15

$FIXED.GIGAMUGA_VS_COMBINE
$FIXED.GIGAMUGA_VS_COMBINE$info
[1] "x:COMBINE VS y:FIXED.GIGAMUGA"

$FIXED.GIGAMUGA_VS_COMBINE$z

Both    x    y 
2463 1972 1373 

$FIXED.GIGAMUGA_VS_COMBINE$down_up

Down   Up 
3562 2246 

$FIXED.GIGAMUGA_VS_COMBINE$d1_mean
# A tibble: 2 x 4
  down_up mean_lodx mean_lody  diff
  <chr>       <dbl>     <dbl> <dbl>
1 Down         11.8      9.98  1.85
2 Up           11.3     13.3  -1.97

$FIXED.GIGAMUGA_VS_COMBINE$slope
(Intercept)       lod.x 
 -0.2991519   0.9936949 

$FIXED.GIGAMUGA_VS_COMBINE$plot

Past versions of Figure6_fixedarray_vs_combined-3.png

Version	Author	Date
2cd3a58	xhyuo	2020-11-15

#table
df5 <- results_sum(results5)
df5 %>%
  kbl(caption = "Summary on fixedarray_vs_combined", digits = 3, align = "l") %>%
  kable_styling()

Summary on fixedarray_vs_combined

Infor	Var1	Freq	Down_Up	Freq	mean_lodx	mean_lody	diff	Intercept	Slope
x:COMBINE VS y:FIXED.MUGA	Both	2475						0.017	0.996
	x	717	Down	2196	11.798	10.662	1.136
	y	1151	Up	2147	10.783	11.878	-1.095
x:COMBINE VS y:FIXED.MEGAMUGA	Both	2098						0.659	0.976
	x	293	Down	1652	10.987	9.977	1.010
	y	2318	Up	3057	8.161	9.377	-1.216
x:COMBINE VS y:FIXED.GIGAMUGA	Both	2463						-0.299	0.994
	x	1972	Down	3562	11.827	9.978	1.849
	y	1373	Up	2246	11.336	13.305	-1.969

Session information

sessionInfo()

R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.4 LTS

Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.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=C             
 [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] kableExtra_1.3.1  furrr_0.2.1       future_1.20.1     ggpubr_0.4.0     
 [5] cowplot_1.1.0     viridis_0.5.1     viridisLite_0.3.0 MASS_7.3-51.6    
 [9] reshape2_1.4.4    plotly_4.9.2.1    gridExtra_2.3     qvalue_2.20.0    
[13] data.table_1.12.8 forcats_0.5.0     stringr_1.4.0     dplyr_1.0.0      
[17] purrr_0.3.4       readr_1.4.0       tidyr_1.1.0       tibble_3.0.1     
[21] tidyverse_1.3.0   ggplot2_3.3.2     workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] fs_1.4.1          lubridate_1.7.9   webshot_0.5.2     httr_1.4.1       
 [5] rprojroot_1.3-2   tools_4.0.0       backports_1.1.6   utf8_1.1.4       
 [9] R6_2.4.1          DBI_1.1.0         lazyeval_0.2.2    colorspace_1.4-1 
[13] withr_2.2.0       tidyselect_1.1.0  curl_4.3          compiler_4.0.0   
[17] git2r_0.27.1      cli_2.0.2         rvest_0.3.6       xml2_1.3.2       
[21] labeling_0.4.2    scales_1.1.1      digest_0.6.25     foreign_0.8-79   
[25] rmarkdown_2.5     rio_0.5.16        pkgconfig_2.0.3   htmltools_0.4.0  
[29] parallelly_1.21.0 highr_0.8         dbplyr_2.0.0      htmlwidgets_1.5.1
[33] rlang_0.4.6       readxl_1.3.1      rstudioapi_0.11   farver_2.0.3     
[37] generics_0.0.2    jsonlite_1.6.1    zip_2.1.1         car_3.0-10       
[41] magrittr_1.5      Rcpp_1.0.4.6      munsell_0.5.0     fansi_0.4.1      
[45] abind_1.4-5       lifecycle_0.2.0   stringi_1.4.6     whisker_0.4      
[49] yaml_2.2.1        carData_3.0-4     plyr_1.8.6        grid_4.0.0       
[53] parallel_4.0.0    listenv_0.8.0     promises_1.1.0    crayon_1.3.4     
[57] haven_2.3.1       splines_4.0.0     hms_0.5.3         knitr_1.28       
[61] pillar_1.4.4      ggsignif_0.6.0    codetools_0.2-16  reprex_0.3.0     
[65] glue_1.4.0        evaluate_0.14     modelr_0.1.8      vctrs_0.3.1      
[69] httpuv_1.5.4      cellranger_1.1.0  gtable_0.3.0      assertthat_0.2.1 
[73] xfun_0.13         openxlsx_4.2.3    broom_0.7.2       rstatix_0.6.0    
[77] later_1.0.0       globals_0.13.1    ellipsis_0.3.0