Question about time course analyse

[X02cinnamondirty]

I’ve seen the code and manual about time course data like dynamic_ocrs <- getTopSpecifcPeaks(spm_data = spm, norm_data = quant_data, top_N = 1000, save_path = "F:/cisDynet/example/", file_prefix = "Top1000_raw_data") and getTimeATAC(norm_data = dynamic_ocrs),but I cant find the data associated with time. Can you tell me the standard format of data to describe time series in like quant_data or spm_data?I've seen the all quant data you provided but Ithink it may cant fix my trouble?

[tzhu-bio]

I apologise for the delay in replying. The quant_data here is what you get after quantification earlier (each row is a peak, each column is a sample or time point). If your data is in time order, then you need to arrange the data from left to right according to the time order. spm_data is just filtering some difference peaks on the basis of quant_data.

BTW, what difficulties are you experiencing now?

[X02cinnamondirty]

Sorry for bordering.I am reading the origin code and thinking about running the code from the midway point.And now I am confused with the function addAnnotation(),and I found it's a necessary part,but I dont know what is the gene_bed,and I have seen the code of snakemake too,I cant find the code about gene_bed and gtf,I see snakemake ask to offer GFF3,maybe I miss that part about gene_bed ,can you tell me what the code is ? By the way,I try to use the normalized count(peakset) from DiffBind(dba.count and dba.normalize) to as the quant_data,it works,and I think time course analyse is only acoording to the RANK?,so whether its CPM\RPKM or what,the numeric size will not influence the result, am I right?

------------------ 原始邮件 ------------------ 发件人: "tzhu-bio/cisDynet" @.>; 发送时间: 2024年7月19日(星期五) 上午9:30 @.>; @.**@.>; 主题: Re: [tzhu-bio/cisDynet] Question about time course analyse (Issue #12)

I apologise for the delay in replying. The quant_data here is what you get after quantification earlier (each row is a peak, each column is a sample or time point). If your data is in time order, then you need to arrange the data from left to right according to the time order. spm_data is just filtering some difference peaks on the basis of quant_data.

BTW, what difficulties are you experiencing now?

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[tzhu-bio]

You can follow these files and tutorial to prepare your own annotation file.

In principle, the results obtained using DiffBind do not affect the timing analysis. However, I strongly recommend using cisDynet pipeline, as it incorporates peak2gene information necessary for calculating fitted ATAC and RNA.

[X02cinnamondirty]

Thank you.

------------------ 原始邮件 ------------------ 发件人: "tzhu-bio/cisDynet" @.>; 发送时间: 2024年7月19日(星期五) 上午10:27 @.>; @.**@.>; 主题: Re: [tzhu-bio/cisDynet] Question about time course analyse (Issue #12)

You can follow these files and tutorial to prepare your own annotation file.

In principle, the results obtained using DiffBind do not affect the timing analysis. However, I strongly recommend using cisDynet pipeline, as it incorporates peak2gene information necessary for calculating fitted ATAC and RNA.

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[X02cinnamondirty]

Sorry for bordering again. I wonder that if I use the normalized-width-peakset [like use dba.counts(summits=100),it normalize all peak in the same width]in annoMergedPeaks and getPeak2Gene,will it influence the correctness of calculations (especially the correlation coefficient of peak2gene)?

------------------ 原始邮件 ------------------ 发件人: "tzhu-bio/cisDynet" @.>; 发送时间: 2024年7月19日(星期五) 上午10:27 @.>; @.**@.>; 主题: Re: [tzhu-bio/cisDynet] Question about time course analyse (Issue #12)

You can follow these files and tutorial to prepare your own annotation file.

In principle, the results obtained using DiffBind do not affect the timing analysis. However, I strongly recommend using cisDynet pipeline, as it incorporates peak2gene information necessary for calculating fitted ATAC and RNA.

— Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you authored the thread.Message ID: @.***>

[X02cinnamondirty]

Hey,I am facing a new problem now and sorry for bordering again,when I use the function getTimeRNA,I notice that the start of time point is associated with the Z-score>1.9,[column_idx <- which(df[1, ] > 1.9)[1]],but in my dataframe(the first gene),all the score is smaller than 1.9,what's that mean? and i got an error of the column_idx to be NA.Here is a part of my df, is it right?should I choose the column 135 to be the first timepoint?

head(df) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] chr11:115494588-115494788 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr4:156077450-156077650 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr10:13745391-13745591 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr15:79400406-79400606 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr1:192924697-192924897 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr2:32737312-32737512 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] chr11:115494588-115494788 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr4:156077450-156077650 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr10:13745391-13745591 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr15:79400406-79400606 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr1:192924697-192924897 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr2:32737312-32737512 -2 -2 -2 -2 -2 -2 -2 -2 -2 [,20] [,21] [,22] [,23] [,24] [,25] [,26] [,27] [,28] chr11:115494588-115494788 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr4:156077450-156077650 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr10:13745391-13745591 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr15:79400406-79400606 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr1:192924697-192924897 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr2:32737312-32737512 -2 -2 -2 -2 -2 -2 -2 -2 -2 [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] chr11:115494588-115494788 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr4:156077450-156077650 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr10:13745391-13745591 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr15:79400406-79400606 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr1:192924697-192924897 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr2:32737312-32737512 -2 -2 -2 -2 -2 -2 -2 -2 -2 [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] chr11:115494588-115494788 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr4:156077450-156077650 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr10:13745391-13745591 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr15:79400406-79400606 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr1:192924697-192924897 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr2:32737312-32737512 -2 -2 -2 -2 -2 -2 -2 -2 -2 [,47] [,48] [,49] [,50] [,51] [,52] [,53] [,54] [,55] chr11:115494588-115494788 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr4:156077450-156077650 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr10:13745391-13745591 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr15:79400406-79400606 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr1:192924697-192924897 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr2:32737312-32737512 -2 -2 -2 -2 -2 -2 -2 -2 -2 [,56] [,57] [,58] [,59] [,60] [,61] [,62] [,63] [,64] chr11:115494588-115494788 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr4:156077450-156077650 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr10:13745391-13745591 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr15:79400406-79400606 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr1:192924697-192924897 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr2:32737312-32737512 -2 -2 -2 -2 -2 -2 -2 -2 -2 [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] chr11:115494588-115494788 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr4:156077450-156077650 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr10:13745391-13745591 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr15:79400406-79400606 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr1:192924697-192924897 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr2:32737312-32737512 -2 -2 -2 -2 -2 -2 -2 -2 -2 [,74] [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] chr11:115494588-115494788 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr4:156077450-156077650 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr10:13745391-13745591 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr15:79400406-79400606 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr1:192924697-192924897 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr2:32737312-32737512 -2 -2 -2 -2 -2 -2 -2 -2 -2 [,83] [,84] [,85] [,86] [,87] [,88] [,89] [,90] [,91] chr11:115494588-115494788 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr4:156077450-156077650 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr10:13745391-13745591 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr15:79400406-79400606 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr1:192924697-192924897 -2 -2 -2 -2 -2 -2 -2 -2 -2 chr2:32737312-32737512 -2 -2 -2 -2 -2 -2 -2 -2 -2 [,92] [,93] [,94] [,95] [,96] [,97] [,98] [,99] chr11:115494588-115494788 -2 -2 -2 -2 -2 -2 -2 -2 chr4:156077450-156077650 -2 -2 -2 -2 -2 -2 -2 -2 chr10:13745391-13745591 -2 -2 -2 -2 -2 -2 -2 -2 chr15:79400406-79400606 -2 -2 -2 -2 -2 -2 -2 -2 chr1:192924697-192924897 -2 -2 -2 -2 -2 -2 -2 -2 chr2:32737312-32737512 -2 -2 -2 -2 -2 -2 -2 -2 [,100] [,101] [,102] [,103] [,104] [,105] [,106] chr11:115494588-115494788 -2 -2 -2 -2 -2 -2 -2 chr4:156077450-156077650 -2 -2 -2 -2 -2 -2 -2 chr10:13745391-13745591 -2 -2 -2 -2 -2 -2 -2 chr15:79400406-79400606 -2 -2 -2 -2 -2 -2 -2 chr1:192924697-192924897 -2 -2 -2 -2 -2 -2 -2 chr2:32737312-32737512 -2 -2 -2 -2 -2 -2 -2 [,107] [,108] [,109] [,110] [,111] [,112] [,113] chr11:115494588-115494788 -2 -2 -2 -2 -2 -2 -2 chr4:156077450-156077650 -2 -2 -2 -2 -2 -2 -2 chr10:13745391-13745591 -2 -2 -2 -2 -2 -2 -2 chr15:79400406-79400606 -2 -2 -2 -2 -2 -2 -2 chr1:192924697-192924897 -2 -2 -2 -2 -2 -2 -2 chr2:32737312-32737512 -2 -2 -2 -2 -2 -2 -2 [,114] [,115] [,116] [,117] [,118] [,119] [,120] chr11:115494588-115494788 -2 -2 -2 -2 -2 -2 -2 chr4:156077450-156077650 -2 -2 -2 -2 -2 -2 -2 chr10:13745391-13745591 -2 -2 -2 -2 -2 -2 -2 chr15:79400406-79400606 -2 -2 -2 -2 -2 -2 -2 chr1:192924697-192924897 -2 -2 -2 -2 -2 -2 -2 chr2:32737312-32737512 -2 -2 -2 -2 -2 -2 -2 [,121] [,122] [,123] [,124] [,125] [,126] [,127] chr11:115494588-115494788 -2 -2 -2 -2 -2 -2 -2 chr4:156077450-156077650 -2 -2 -2 -2 -2 -2 -2 chr10:13745391-13745591 -2 -2 -2 -2 -2 -2 -2 chr15:79400406-79400606 -2 -2 -2 -2 -2 -2 -2 chr1:192924697-192924897 -2 -2 -2 -2 -2 -2 -2 chr2:32737312-32737512 -2 -2 -2 -2 -2 -2 -2 [,128] [,129] [,130] [,131] [,132] [,133] [,134] chr11:115494588-115494788 -2 -2 -2 -2 -2 -2 -2 chr4:156077450-156077650 -2 -2 -2 -2 -2 -2 -2 chr10:13745391-13745591 -2 -2 -2 -2 -2 -2 -2 chr15:79400406-79400606 -2 -2 -2 -2 -2 -2 -2 chr1:192924697-192924897 -2 -2 -2 -2 -2 -2 -2 chr2:32737312-32737512 -2 -2 -2 -2 -2 -2 -2 [,135] [,136] [,137] [,138] chr11:115494588-115494788 0.90240820 0.90140381 0.90039946 0.89939513 chr4:156077450-156077650 0.56758941 0.56634535 0.56510263 0.56386124 chr10:13745391-13745591 0.72592477 0.72819672 0.73046245 0.73272197 chr15:79400406-79400606 -0.02327534 -0.01991515 -0.01656218 -0.01321643 chr1:192924697-192924897 0.71092805 0.71462510 0.71831301 0.72199175 chr2:32737312-32737512 1.41660355 1.41429264 1.41198353 1.40967619 [,139] [,140] [,141] [,142] chr11:115494588-115494788 0.898390843 0.897386580 0.896382345 8.953781e-01 chr4:156077450-156077650 0.562621178 0.561382447 0.560145042 5.589090e-01 chr10:13745391-13745591 0.734975294 0.737222424 0.739463371 7.416981e-01 chr15:79400406-79400606 -0.009877907 -0.006546613 -0.003222551 9.427624e-05 chr1:192924697-192924897 0.725661310 0.729321672 0.732972824 7.366147e-01 chr2:32737312-32737512 1.407370633 1.405066834 1.402764788 1.400464e+00 [,143] [,144] [,145] [,146] chr11:115494588-115494788 0.894373946 0.893369779 0.89236563 0.89136150 chr4:156077450-156077650 0.557674196 0.556440750 0.55520862 0.55397780 chr10:13745391-13745591 0.743926742 0.746149182 0.74836547 0.75057561 chr15:79400406-79400606 0.003403866 0.006706214 0.01000132 0.01328918 chr1:192924697-192924897 0.740247433 0.743870861 0.74748502 0.75108989 chr2:32737312-32737512 1.398165910 1.395869058 1.39357392 1.39128048

[tzhu-bio]

You can try normalized-width-peakset and the difference shouldn't be too much.

You can refer to this issue.

BTW, could you provide the full dataframe so I can see what exactly is wrong?

[X02cinnamondirty]

OK，Here is the RDS、TPM and error df table I got in the attachment.

------------------ 原始邮件 ------------------ 发件人: "tzhu-bio/cisDynet" @.>; 发送时间: 2024年7月20日(星期六) 上午8:51 @.>; @.**@.>; 主题: Re: [tzhu-bio/cisDynet] Question about time course analyse (Issue #12)

You can try normalized-width-peakset and the difference shouldn't be too much.

You can refer to this issue.

BTW, could you provide the full dataframe so I can see what exactly is wrong?

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从QQ邮箱发来的超大附件

error_df.txt (46.88M, 无限期)进入下载页面：http://mail.qq.com/cgi-bin/ftnExs_download?t=exs_ftn_download&k=78626161459e25c9f284195b1564061b411603075257030214500550504955015f534c0752025119580607500756010d08500359336834514b100e136c00521a4d1a15610e&code=9baa3d44

Peak2Gene_All_Links.rds (5.66M, 无限期)进入下载页面：http://mail.qq.com/cgi-bin/ftnExs_download?t=exs_ftn_download&k=79366131ed4996c9fed0190b133301194d42530005070453180257010c1e510703054c015105001b0605580306515152050356543524336650570a0372565d536a770d5d6a7f5a585e454f435140330b&code=56a15336

H2D_RNA_TPM.tsv (3.63M, 无限期)进入下载页面：http://mail.qq.com/cgi-bin/ftnExs_download?t=exs_ftn_download&k=73393239f8eba69efcdf4a031134514e4f4d565c075657541a5c515b5619530456001f0900005a4c550f005b04505a040201040a373b6329057d6d6b79753c3567741c4d4442635c&code=792974ca

[tzhu-bio]

Please send me the dynamic ocrs file.

[X02cinnamondirty]

ok,here is the dynamic_ocrs.By the way,I use getSpecificPeak but not getTopSpecificPeak.

------------------ 原始邮件 ------------------ 发件人: "tzhu-bio/cisDynet" @.>; 发送时间: 2024年7月20日(星期六) 中午11:18 @.>; @.**@.>; 主题: Re: [tzhu-bio/cisDynet] Question about time course analyse (Issue #12)

Please send me the dynamic ocrs file.

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dynamic_ocrs.txt (2.08M, 无限期)进入下载页面：https://mail.qq.com/cgi-bin/ftnExs_download?k=72336231b7f3bc9dfcd51a0b1133504d4f475250550550511a520301521e060602504f520e51564f5356575454000055530b5653372362064e5d035c5e503d0d5441111f434b16620a&t=exs_ftn_download&code=73b173bb

[tzhu-bio]

I have fixed the error. Please install the latest version.

[X02cinnamondirty]

Thank you！

------------------ 原始邮件 ------------------ 发件人: "tzhu-bio/cisDynet" @.>; 发送时间: 2024年7月20日(星期六) 下午3:34 @.>; @.**@.>; 主题: Re: [tzhu-bio/cisDynet] Question about time course analyse (Issue #12)

I have fixed the error. Please install the latest version.

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[X02cinnamondirty]

I have fixed the error. Please install the latest version.

And the code in plotTimeAll sould be fixed in the same way maybe？

[tzhu-bio]

Sorry, please install again.

[X02cinnamondirty]

Sorry, please install again. ＯＫ(ゝω・´★)