Showing posts with label network aging. Show all posts

Sunday, June 19, 2022

operator theory

https://en.wikipedia.org/wiki/Operator_theory

Friday, June 17, 2022

aging methylation data, single cell, Transcription factor

Inference of age-associated transcription factor regulatory activity changes in single cells

https://www.nature.com/articles/s43587-022-00233-9#data-availability

re we present and validate a TF activity estimation method for single cells from the hematopoietic system that is based on TF regulons, and apply it to a mouse single-cell RNA-sequencing atlas, to infer age-associated differentiation activity changes in the immune cells of different organs

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56046

Series GSE56046

Query DataSets for GSE56046

Status

Public on Nov 24, 2014

Title

Transcriptomics and methylomics of human monocytes [methylome]

Organism

Homo sapiens

Experiment type

Methylation profiling by genome tiling array

Summary

The MESA Epigenomics and Transcriptomics Study has been launched to investigate potential gene expression regulatory methylation sites in humans by examining the association between CpG methylation and gene expression in purified human monocytes from a large study population (community-dwelling participants in the Multi-Ethnic Study of Atherosclerosis (MESA)).
The MESA Epigenomics and Transcriptomics Study was funded by a National Heart, Lung and Blood Institute grant (R01HL101250) through the NIH Roadmap Epigenomics Program in 2009.

Sunday, May 30, 2021

weight in controllability theory

Xdot = AX + Bu

So, weight in matrix A are coefficients of the linear systems, which will influence the co-dependency but not the degree of freedom. So, it seems that weight will influence the which gene would be the driver nodes, but not the minimum number of driver node in theory. However, because aging biological network have noises, to weight would influence the sensitivity and robustness of gene network to aging noises.

Friday, September 27, 2019

single cell genomics pseduotime

https://github.com/hongqin/single-cell-pseudotime

https://www.sciencedirect.com/science/article/pii/S0959437X17300084

Monday, August 26, 2019

yeast PIN nD

controllability for yPIN with Dang's data

```{r eigen-spectrum}
e=eig$values;
summary(e)
digits = c(1:30)
zeros = digits
debug = 0
for ( i in 1:length(digits ) ) {
tmp = sort(table(round(e, roundings[i])), decreasing = T)
if (debug > 0) { print(tmp[1:3]) }
zeros[i] = tmp[1]
}
cbind( digits , zeros)
```

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-59.919  -1.696   0.000   0.000   1.384  97.758 
      digits zeros
 [1,]      1   228
 [2,]      2   158
 [3,]      3   140
 [4,]      4   136
 [5,]      5   135
 [6,]      6   135
 [7,]      7   135
 [8,]      8   135
 [9,]      9   135
[10,]     10   135
[11,]     11   135
[12,]     12   135
[13,]     13   135
[14,]     14   135
[15,]     15   112
[16,]     16    45
[17,]     17     9
[18,]     18     3
[19,]     19     2
[20,]     20     1
[21,]     21     1
[22,]     22     1
[23,]     23     1
[24,]     24     1
[25,]     25     1
[26,]     26     1
[27,]     27     1
[28,]     28     1
[29,]     29     1
[30,]     30     1

Conclusion: frequency of zero eigen values stabilize between 5 and 14 at 135.

Wednesday, August 21, 2019

phage PIN and aging simulation

first principle simulation of phage PIN: Can I regenerate the exponetial survival curves?

2013 Dec;87(23):12745-55. doi: 10.1128/JVI.02495-13. Epub 2013 Sep 18.

The protein interaction network of bacteriophage lambda with its host, Escherichia coli.

Blasche S1, Wuchty S, Rajagopala SV, Uetz P.

https://www.ncbi.nlm.nih.gov/pubmed/24049175

2011 Sep 26;11:213. doi: 10.1186/1471-2180-11-213.

The protein interaction map of bacteriophage lambda.

Rajagopala SV¹, Casjens S, Uetz P.

https://www.ncbi.nlm.nih.gov/pubmed/21943085

Monday, July 23, 2018

Dynamic networks reveal key players in aging

Faisal and Melenkovic 2014 bioinforamtics
https://academic.oup.com/bioinformatics/article/30/12/1721/2748134#supplementary-data

Todo: apply this in yeast. network controllability analysis, -> reliability analysis

Thursday, May 10, 2018

generate github repo for publication support, network aging manuscript

applejack:network_aging_ms_draft hqin$ pwd

/Users/hqin/github/network_aging_ms_draft

After porting old repo to the new one, I fixed some variable names, clean up unnecessary lines from the codes. "diff' show that old publication file and newly generated publication file are the same (except for one column name). So, the porting repo repeated old results for natural isolate RLS fitting.

applejack:github hqin$ ls network_aging_ms_draft/0.nat.rls.fitting/sandbox/Bootstrap_summary_for_publication.csv

network_aging_ms_draft/0.nat.rls.fitting/sandbox/Bootstrap_summary_for_publication.csv

applejack:github hqin$ ls bmc_netwk_aging_manuscript/R1/0.nat.rls.fitting/sandbox/Bootstrap_summary_for_publication.csv

bmc_netwk_aging_manuscript/R1/0.nat.rls.fitting/sandbox/Bootstrap_summary_for_publication.csv

applejack:github hqin$ diff bmc_netwk_aging_manuscript/R1/0.nat.rls.fitting/sandbox/Bootstrap_summary_for_publication.csv network_aging_ms_draft/0.nat.rls.fitting/sandbox/Bootstrap_summary_for_publication.csv

1c1

< "BootstrapMean...c..my.strains...","RwithStd","t0withStd","nwithStd","GwithStd","avgLSwithStd"

---

> "BootstrapMean...c..strains...","RwithStd","t0withStd","nwithStd","GwithStd","avgLSwithStd"

20180531. trying to find old script for histogram overlay from Jan 13 2018 commit.
https://github.com/hongqin/bmc_netwk_aging_manuscript/commit/91ebd58ba7ad71635dbdbf9e324f883ceb0baf6d
https://github.com/hongqin/bmc_netwk_aging_manuscript/blob/91ebd58ba7ad71635dbdbf9e324f883ceb0baf6d/R1/1.kaeberlein04plos/0.fit.kaeberlein04.pdf

Thursday, November 30, 2017

RLS pairwise difference, P-value < 1/10K using ms02 permutation

rm(list=ls())
#setwd("~/github/0.network.aging.ms02/1.Fraser02")
setwd("/home/hqin/github/network.aging.configuration/1.Fraser02")
source("../network.r")
set.seed(2017)
debug = 1; 
start_time = Sys.time();

list.files(path="../data/")

## [1] "ken-RLS-byORF.csv"                             
## [2] "SummaryRegressionHetHomFactorized2015Oct13.csv"
## [3] "unique_biogrid_ScePPI.csv"

rls = read.csv("../data/ken-RLS-byORF.csv");
biogrid = read.csv("../data/unique_biogrid_ScePPI.csv");
fit = read.csv("../data/SummaryRegressionHetHomFactorized2015Oct13.csv")

ppi = biogrid[, c("Systematic.Name.Interactor.A","Systematic.Name.Interactor.B")];
names(ppi) = c("ORF1", "ORF2" )

#First, define a function to calculate V difference in pairs of proteins
 diff.RLS = function( inpairs ) {
   inpairs$rls1 = rls$avgLS[match( inpairs$ORF1, rls$ORF ) ];
   inpairs$rls2 = rls$avgLS[match( inpairs$ORF2, rls$ORF ) ];
   
   inpairs$essen1 = fit$essenflag[match(inpairs$ORF1, fit$orf)];
   inpairs$essen2 = fit$essenflag[match(inpairs$ORF2, fit$orf)];
   
   inpairs$rls1 = ifelse( inpairs$essen1=='essential', 0, inpairs$rls1);
   inpairs$rls2 = ifelse( inpairs$essen2=='essential', 0, inpairs$rls2);
   
   ret = mean( abs( inpairs$rls1 - inpairs$rls2 ), na.rm=T );
 }

 # calculate the observed difference in RLS
 diff.RLS.obs = diff.RLS ( ppi );
 paste( "Observed deltaRLS = ", diff.RLS.obs);

## [1] "Observed deltaRLS =  12.759632586095"

#permutation of pairs, and their difference in Ka
 Nsims = 10000; #number of permutations
 permutated.diff.RLS = numeric( Nsims ); #empty vector to store calculations

library(foreach)
library(doMC)

## Loading required package: iterators

## Loading required package: parallel

registerDoMC(cores=8) #Intel i7 has 6 cores, Xeon E5-2603 @ridgeside has 8 cores

permutated.diff.RLS = foreach(i=1:Nsims) %dopar% {
   new.pairs = ms02_singlerun(ppi ) #generate a new MS02 random network
   new.pairs = new.pairs[,1:2] #reformating into two-columns
   names(new.pairs) = c("ORF1", "ORF2")
   diff.RLS( new.pairs ); 
  }

p-value

permutated.diff.RLS = unlist(permutated.diff.RLS)

summary(permutated.diff.RLS)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   14.03   14.20   14.22   14.22   14.25   14.37

sub = permutated.diff.RLS[ permutated.diff.RLS < diff.RLS.obs ]
paste("pvalue = ", length(sub)/Nsims)

## [1] "pvalue =  0"

hist(permutated.diff.RLS)

stop_time = Sys.time()
start_time;

## [1] "2017-11-29 10:01:30 EST"

stop_time;

## [1] "2017-11-29 13:04:53 EST"

paste( "running time = ", stop_time - start_time)

## [1] "running time =  3.05638415979015"

Wednesday, November 29, 2017

p=0.001 ridgeside, rls pairwise difference in yeast biogrid PPI

rm(list=ls())
#setwd("~/github/0.network.aging.ms02/1.Fraser02")
setwd("/home/hqin/github/network.aging.configuration/1.Fraser02")
source("../network.r")
set.seed(2017)
debug = 1; 
start_time = Sys.time();

list.files(path="../data/")

## [1] "ken-RLS-byORF.csv"                             
## [2] "SummaryRegressionHetHomFactorized2015Oct13.csv"
## [3] "unique_biogrid_ScePPI.csv"

rls = read.csv("../data/ken-RLS-byORF.csv");
biogrid = read.csv("../data/unique_biogrid_ScePPI.csv");
fit = read.csv("../data/SummaryRegressionHetHomFactorized2015Oct13.csv")

ppi = biogrid[, c("Systematic.Name.Interactor.A","Systematic.Name.Interactor.B")];
names(ppi) = c("ORF1", "ORF2" )

#First, define a function to calculate V difference in pairs of proteins
 diff.RLS = function( inpairs ) {
   inpairs$rls1 = rls$avgLS[match( inpairs$ORF1, rls$ORF ) ];
   inpairs$rls2 = rls$avgLS[match( inpairs$ORF2, rls$ORF ) ];
   
   inpairs$essen1 = fit$essenflag[match(inpairs$ORF1, fit$orf)];
   inpairs$essen2 = fit$essenflag[match(inpairs$ORF2, fit$orf)];
   
   inpairs$rls1 = ifelse( inpairs$essen1=='essential', 0, inpairs$rls1);
   inpairs$rls2 = ifelse( inpairs$essen2=='essential', 0, inpairs$rls2);
   
   ret = mean( abs( inpairs$rls1 - inpairs$rls2 ), na.rm=T );
 }

 # calculate the observed difference in RLS
 diff.RLS.obs = diff.RLS ( ppi );
 paste( "Observed deltaRLS = ", diff.RLS.obs);

## [1] "Observed deltaRLS =  12.759632586095"

#permutation of pairs, and their difference in Ka
 Nsims = 1000; #number of permutations
 permutated.diff.RLS = numeric( Nsims ); #empty vector to store calculations

library(foreach)
library(doMC)

## Loading required package: iterators

## Loading required package: parallel

registerDoMC(cores=8) #Intel i7 has 6 cores, Xeon E5-2603 @ridgeside has 8 cores

permutated.diff.RLS = foreach(i=1:Nsims) %dopar% {
   new.pairs = ms02_singlerun(ppi ) #generate a new MS02 random network
   new.pairs = new.pairs[,1:2] #reformating into two-columns
   names(new.pairs) = c("ORF1", "ORF2")
   diff.RLS( new.pairs ); 
  }