Publications by My Nguyen
28a_Round1 Filter
Load data TopDom df Window size = 3L Change Algorithm - findTAD match_dis_TAD function Function to find TAD from a starting point by matching distance between starting point and local minimums ## Identify TAD by matching distance between starting point and local minimums match_dis_TAD <- function(smooth_line, probability_table, starting_right_TA...
693 sym Python (9269 sym/15 pcs) 7 img
26_Update_preciseTAD.findTAD_algorithm
Load data ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 5.00 10.00 16.50 18.28 20.75 61.00 TopDom df Window size = 3L ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 0.00 11.31 14.78 13.96 16.67 28.67 1 Update algorithm preciseTAD - findTAD update heatmap function Test updated Algorithm ...
184 sym 4 img
script 22 - GM12878 200bp
GM12878 on bp - After removing telemore and centremore, Study about string of 0 ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.0 17.0 50.0 168.8 116.0 134569.0 Arm 1 - 200bp resolution Load data Study string of 0 ## [1] 18145 summary(zero_regions$bp200_bin_size) ## Min. 1st Qu. Median Mean 3rd Qu. M...
900 sym 83 img
Instruction for BOOOM package
Optimization problem Install package library(devtools) install_github("hamy12398/ballsdMN") ### Load package library("BOOOM") library(Rcpp) library(testthat) library(RcppArmadillo) library(pracma) Function augment BOOOM_opt(Mat, func, desired_min = -10^(60), s_init = 1, no_runs = 1000, max_iter = 10000, rho = 2, phi = 10^(-20), ...
1254 sym R (5699 sym/14 pcs) 1 img
18a_preciseTAD_strongRegions
Load data w = 5000 + h = 1500. PreciseTAD with sliding window of 5000 (5KB resolution) and smoothing h = 1500 e = 0.1 and t = 0.1 ## Summary of size is: ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 55000 320000 5450000 6788238 11620000 24145000 Arm1 - Report error of closest IS boundary and preciseTAD ## Summary of distanc...
325 sym Python (588 sym/7 pcs) 18 img
20_IS_apply_preciseTAD
Load data Create reflection of IS Apply preciseTAD on IS curve to find TAD Window = 5000 Full Window window_resolution <- 43458 IS_bound.preciseTAD_fullW <- unique(c(all.tads.arm2.bp$left_TAD, all.tads.arm2.bp$right_TAD)) overlap_bound = intersect(boundaryIS_ch1$Start, IS_bound.preciseTAD_fullW) overlap_bound ## [1] 1920000 3900000 67...
453 sym Python (3201 sym/21 pcs) 6 img
preciseTAD_script18
Load data TAD from Insulation Score IS - TAD size ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 65000 185000 265000 374547 400000 24490000 w = 500 + h = 1100. PreciseTAD with sliding window of 500 (5KB resolution) and smoothing h = 1100 e = 0.1 and t = 0.1 Arm1 ## left_TAD right_TAD center_TAD averageProb_diff error...
1187 sym Python (7891 sym/48 pcs) 26 img
preciseTAD_script17c
Load data ## [1] 245955000 PreciseTAD Use optimal harmonic = 1100 (found in scipt 17) Arm1 ## # A tibble: 6 × 4 ## # Groups: left_TAD, center_TAD [6] ## left_TAD center_TAD right_TAD Freq ## <int> <int> <int> <int> ## 1 181 345 512 1 ## 2 483 497 512 1 ## 3 512 600 ...
1040 sym Python (12748 sym/44 pcs) 30 img 4 tbl
Document
Load data TAD from Insulation Score IS - TAD size ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 65000 185000 265000 374547 400000 24490000 500. PreciseTAD with sliding window of 500 (5KB resolution) and smoothing h = 1100 e = 0.1 and t = 0.1 Arm1 ## left_TAD right_TAD center_TAD averageProb_diff error_symetric overl...
572 sym Python (6912 sym/29 pcs) 14 img