{"id":3669,"date":"2024-03-26T00:42:54","date_gmt":"2024-03-25T16:42:54","guid":{"rendered":"http:\/\/www.ccwifi.cc\/blogs\/?p=3669"},"modified":"2024-03-26T00:42:54","modified_gmt":"2024-03-25T16:42:54","slug":"%e6%9c%89%e4%ba%9b%e8%bd%af%e4%bb%b6%e6%80%bb%e8%83%bd%e4%bb%a5%e6%9f%90%e7%a7%8d%e6%96%b9%e5%bc%8f%e5%8a%9d%e9%80%80%e4%bd%a0spe2234","status":"publish","type":"post","link":"https:\/\/www.ccwifi.cc\/blogs\/2024\/03\/26\/%e6%9c%89%e4%ba%9b%e8%bd%af%e4%bb%b6%e6%80%bb%e8%83%bd%e4%bb%a5%e6%9f%90%e7%a7%8d%e6%96%b9%e5%bc%8f%e5%8a%9d%e9%80%80%e4%bd%a0spe2234\/","title":{"rendered":"\u6709\u4e9b\u8f6f\u4ef6\u603b\u80fd\u4ee5\u67d0\u79cd\u65b9\u5f0f\u529d\u9000\u4f60 | SpectralTAD \u4e4b TAD calli"},"content":{"rendered":"
\n

\u5728\u7ebfwifi\u8dd1\u5305 \u91d1\u521a\u5305\u8dd1\u5305 cap\u8dd1\u5305 hccapx ewsa\u5728\u7ebf \u5c31\u6765 \u63e1\u624b\u5305\u8dd1\u5305<\/a><\/strong><\/p>\n

\u5404\u4f4d\u597d \u53c8\u89c1\u9762\u4e86 \u6211\u662f\u66f9\u64cd \u4eca\u5929\u7ed9\u5927\u5bb6\u5e26\u6765\u4e00\u7bc7\u65b0\u7684\u6559\u7a0b<\/p>\n

\u5e0c\u671b\u5404\u4f4d\u7ec6\u5fc3\u5b66\u4e60 \u4f4e\u8c03\u7528\u7f51<\/p>\n<\/div>\n

\"\u8dd1\u5305\u8f6f\u4ef6\u65e0\u6cd5\u8fd0\u884c\"<\/p>\n

\u5f15\u8a00
\n\u6709\u4e9b\u8f6f\u4ef6\u603b\u80fd\u4ee5\u67d0\u79cd\u5f62\u5f0f\u529d\u9000\u4f60\uff0c\u8ba9\u4f60\u6beb\u4e0d\u72b9\u8c6b\u5730\u6295\u5165\u5176\u4ed6\u8f6f\u4ef6\u7684\u6000\u62b1\uff0c\u5916\u52a0\u4ea7\u751f\u4e00\u79cd\u4e0d\u5410\u4e0d\u5feb\u7684\u60c5\u612b\uff0c\u6fc0\u53d1\u4e00\u79cd\u552f\u6050\u4ed6\u4eba\u4e5f\u4f1a\u201c\u906d\u6b64\u6bd2\u624b\u201d\u7684\u6b63\u4e49\u611f\u3002\u8f6f\u4ef6\u4e4b\u6240\u4ee5\u88ab\u79f0\u4e3a\u8f6f\u4ef6\uff0c\u5927\u62b5\u662f\u56e0\u4e3a\u7ecf\u8fc7\u5305\u88c5\u4e0e\u6d4b\u8bd5\uff0c\u6bd4\u8f83\u6210\u719f\u4e14\u6709\u5f88\u5927\u7684\u517c\u5bb9\u6027\uff0c\u81f3\u5c11\u6309\u7167\u6587\u6863\u6765\u8fd0\u884c\u5e94\u8be5\u4e0d\u4f1a\u6709\u95ee\u9898\uff0c\u5982\u679c\u52a8\u4e0d\u52a8\u5c31\u51fa\u9519\u90a3\u4e0e\u811a\u672c\u4f55\u5f02\uff1f\u751f\u4fe1\u5206\u6790\u8f6f\u4ef6\u5f88\u5927\u4e00\u90e8\u5206\u90fd\u5c5e\u4e8e\u5c0f\u4f17\u8f6f\u4ef6\uff0c\u6709\u4e9b\u8f6f\u4ef6\u4e5f\u8bb8\u672c\u8eab\u7684\u51fa\u53d1\u70b9\u5c31\u662f\u79d1\u7814\u4e0e\u6587\u7ae0\uff0c\u800c\u597d\u4e0d\u597d\u7528\u80fd\u4e0d\u80fd\u8ba9\u66f4\u591a\u7684\u4eba\u4f7f\u7528\u5e76\u4e0d\u91cd\u8981\u3002\u9047\u5230\u8fd9\u6837\u7684\u8f6f\u4ef6\uff0c\u90a3\u5c31\u542f\u7528\u6211\u4eec\u7684\u4e07\u80fd\u8eba\u5e73\u62db\u5f0f\uff1a\u5206\u6790\u8f6f\u4ef6\u5343\u5343\u4e07\uff0c\u4e0d\u884c\u54b1\u5c31\u6362\u4e00\u6362\u3002\u540c\u65f6\uff0c\u5bf9\u4e8e\u8fd9\u7c7b\u8f6f\u4ef6\u5e94\u8be5\u4fdd\u6301\u656c\u754f\u4e4b\u5fc3\uff0c\u90a3\u53e5\u8bdd\u600e\u4e48\u8bf4\u6765\u7740\uff1a\u660e\u77e5\u5c71\u6709\u864e\uff0c\u731b\u6572\u9000\u5802\u9f13\u3002\u4e0d\u7136\u6d6a\u8d39\u7684\u53ef\u662f\u81ea\u5df1\u5b9d\u8d35\u7684\u65f6\u95f4\uff0c\u4e0b\u9762\u6211\u4eec\u4e00\u8d77\u6765\u770b\u770b\u4eca\u5929\u7684\u4e3b\u89d2\u3002<\/p>\n

\"\u8dd1\u5305\u8f6f\u4ef6\u65e0\u6cd5\u8fd0\u884c\"<\/p>\n

\u7f18\u8d77
\n\u4e3a\u4ec0\u4e48\u4f1a\u7528\u5230SpectralTAD\u5462\uff1f\u8fd9\u90fd\u6e90\u81ea\u4e8e\u505aTAD\u5dee\u5f02\u5206\u6790\u65f6\u4f7f\u7528\u7684TADCompare\u8f6f\u4ef6\uff0c\u5173\u4e8e\u8be5\u8f6f\u4ef6\u4e5f\u5199\u8fc7\u4e24\u7bc7\u5e16\u5b50\uff1a[]\uff0c[]\u3002TADCompare\u5206\u6790\u65f6\u53ef\u4ee5\u63a5\u53d7\u9884\u5b9a\u7684TAD\u6587\u4ef6\uff0c\u800c\u8f6f\u4ef6\u6587\u6863\u4e2d\u8c03\u7528TAD\u4f7f\u7528\u7684\u5c31\u662fSpectralTAD\uff0c\u8fd9\u4fbf\u6709\u4e86\u4e0b\u9762\u7684\u4e8b\u60c5\u3002SpectralTAD\u662f\u4e00\u4e2a\u4e13\u95e8\u7528\u4e8e\u4eceHiC\u63a5\u89e6\u77e9\u9635\u4e2d\u8fdb\u884cTAD calling\u7684R\u5305\uff0c\u5176\u4f7f\u7528\u4e00\u79cd\u57fa\u4e8e\u6ed1\u52a8\u7a97\u53e3\u7684\u6539\u8fdb\u7248\u5149\u8c31\u805a\u7c7b\u65b9\u6cd5\u6765\u5feb\u901f\u68c0\u6d4bTAD\u3002SpectralTAD\u5c06\u63a5\u89e6\u77e9\u9635\u4f5c\u4e3a\u8f93\u5165\uff0c\u5e76\u8f93\u51faBED\u683c\u5f0f\u7684\u6570\u636e\u6846\uff0c\u5176\u4e2d\u5305\u542bTAD\u76f8\u5bf9\u5e94\u7684\u5750\u6807\u4f4d\u7f6e\u3002\u8be5\u5de5\u5177\u5305\u542bSpectralTAD\u3001SpectralTAD_Par\u4e24\u4e2a\u51fd\u6570\uff0c\u5206\u522b\u4e3a\u5355CPU\u548c\u591aCPU\u6a21\u5f0f\u3002\u53e6\u5916\uff0c\u8be5\u5305\u63a5\u53d7\u591a\u79cd\u683c\u5f0f\uff0c\u5982n \u00d7 n\u3001n \u00d7 (n + 3)\u30013\u5217\u7b49\u3002\u81f3\u6b64\uff0c\u611f\u89c9SpectralTAD\u4e00\u5207\u8fd8\u633a\u6b63\u5e38\uff0c\u8ddf\u7740\u6587\u6863\u4ee3\u7801\u8d70\u4e00\u906d\u624d\u77e5\u9053\u8be5\u8f6f\u4ef6\u975e\u5e38\u96be\u4ee5\u9a7e\u9a6d\uff0c\u4e00\u8d77\u6765\u770b\u770b\u600e\u4e48\u56de\u4e8b\uff1a<\/p>\n

<\/span>devtools::install_github(\"dozmorovlab\/SpectralTAD\"<\/span>)
library(SpectralTAD)

data(\"rao_chr20_25_rep\"<\/span>)
tads = SpectralTAD(rao_chr20_25_rep, chr = \"chr20\"<\/span>, levels = 2, qual_filter = FALSE)
Converting to n x n matrix
Matrix dimensions: 2517x2517
Error in<\/span> `vec_init()`:
! `x` must be a vector, not `NULL`.
Run `rlang::last_trace()` to see where<\/span> the error occurred.
Warning messages:
1: Unknown or uninitialised column: `Group`.
2: Unknown or uninitialised column: `Group`.
<\/code><\/pre>\n
<\/span>else<\/span> {
    gap_order = order(-point_dist)
    sil_score = c()
    for<\/span> (cluster in<\/span> clusters) {
        k = 1
        partition_found = 0
        first_run = TRUE
        cutpoints = c()
        while<\/span> (partition_found == 0) {
          new_gap = gap_order[k]
          cutpoints = c(cutpoints, new_gap)
          diff_points = which<\/span>(abs(new_gap - cutpoints[-length(cutpoints)]) <=
            min_size)
          if<\/span> (length(diff_points) > 0) {
            cutpoints = cutpoints[-length(cutpoints)]
          }
          if<\/span> (length(cutpoints) == cluster) {
            partition_found = 1
          }
          else<\/span> {
            k = k + 1
          }
        }
        if<\/span> (any(is.na(cutpoints))) {
          next
        }
        cutpoints = cutpoints[order(cutpoints)]
        cutpoints = c(1, cutpoints, length(non_gaps_within) +
          1)
        group_size = diff(cutpoints)
        memberships = c()
        for<\/span> (i in<\/span> seq_len(length(group_size))) {
          memberships = c(memberships, rep(i, times<\/span> = group_size[i]))
        }
        sil = summary(cluster::silhouette(memberships,
          dist_sub))
        sil_score = c(sil_score, sil$si<\/span>.summary[4])
        Group_mem[[cluster]] = memberships
    }
    end_group = Group_mem[[which<\/span>(diff(sil_score) < 0)[1]]]
    if<\/span> (length(end_group) == 0) {
        end_group = dplyr::bind_rows()
    }
    else<\/span> {
        end_group = data.frame(ID = as.numeric(colnames(sub_filt)),
          Group = end_group)
        end_group = end_group %>% dplyr::mutate(group_place = Group) %>%
          dplyr::group_by(group_place) %>% dplyr::mutate(Group = max(ID)) %>%
          ungroup() %>% dplyr::select(ID, Group)
    }
}
if<\/span> (end == nrow(cont_mat)) {
    Group_over = dplyr::bind_rows(Group_over, end_group)
    end_loop = 1
}
else<\/span> {
    end_IDs = which<\/span>(end_group$Group<\/span> == last(end_group$Group<\/span>))
    start = end - length(end_IDs) + 1
    if<\/span> (length(start) == 0) {
        start = end
    }
    end = start + window_size
    end_group = end_group[-end_IDs, ]
    Group_over = dplyr::bind_rows(Group_over, end_group)
    if<\/span> ((end + (2e+06\/resolution)) > nrow(cont_mat)) {
        end = nrow(cont_mat)
    }
}
<\/code><\/pre>\n
<\/span>tads = SpectralTAD(rao_chr20_25_rep, chr = \"chr20\"<\/span>, levels = 2, qual_filter = FALSE, min_size=4)
Error in<\/span> `vec_init()`:
! `x` must be a vector, not `NULL`.
Run `rlang::last_trace()` to see where<\/span> the error occurred.
Warning messages:
1: Unknown or uninitialised column: `Group`.
2: Unknown or uninitialised column: `Group`.

tads = SpectralTAD(rao_chr20_25_rep, chr = \"chr20\"<\/span>, levels = 2, qual_filter = FALSE, min_size=3)
Error in<\/span> `vec_init()`:
! `x` must be a vector, not `NULL`.
Run `rlang::last_trace()` to see where<\/span> the error occurred.
Warning messages:
1: Unknown or uninitialised column: `Group`.
2: Unknown or uninitialised column: `Group`.

tads = SpectralTAD(rao_chr20_25_rep, chr = \"chr20\"<\/span>, levels = 2, qual_filter = FALSE, min_size=2)
 head(tads$Level_1<\/span>)
# A tibble: 6 \u00d7 4<\/span>
  chr     start     end Level
         
1 chr20   50000  325000     1
2 chr20  325000  525000     1
3 chr20  525000  775000     1
4 chr20  775000 1200000     1
5 chr20 1200000 1450000     1
6 chr20 1450000 1775000     1
<\/code><\/pre>\n
\u81ea\u5df1\u6d4b\u8bd5\uff1a
\n\u8fd9\u4e2a\u9519\u8bef\u6765\u5f97\u7a81\u7136\uff0c\u4e5f\u8ba9\u4eba\u6709\u70b9\u6478\u4e0d\u5230\u5934\u8111\u3002\u600e\u4e48\u56de\u4e8b\uff0c\u8fd9\u8f6f\u4ef6\u8fd8\u6311\u4eba\u4e0d\u6210\uff1f\u5f53\u7136\uff0c\u8f6f\u4ef6\u4e0d\u53ef\u80fd\u56e0\u4eba\u800c\u5f02\uff0c\u4f46\u4f1a\u56e0\u73af\u5883\u800c\u5f02\u3002\u7ecf\u8fc7\u4e00\u756a\u5c1d\u8bd5\uff0c\u53d1\u73b0\u4e0b\u9762\u8fd9\u4e00\u6bb5\u4ee3\u7801\uff1a
\n\u8fd0\u884c\u6587\u6863\u4e2d\u7684\u4ee3\u7801\u4e4b\u6240\u4ee5\u51fa\u9519\u662f\u56e0\u4e3aendgroup\u6ca1\u6709\u7ed3\u679c\uff0cdplyr::bind<\/em>rows(Groupover, end<\/em>group)\u5408\u5e76\u6570\u636e\u65f6\u5f15\u53d1\u4e86\u9519\u8bef\uff0c\u4e5f\u7531\u6b64\u627e\u5230\u4e86\u76f8\u5173\u8054\u7684\u53c2\u6570min_size\uff1a<\/p>\n
<\/span>tads = SpectralTAD(rao_chr20_25_rep, chr = \"chr20\"<\/span>, levels = 2, qual_filter = FALSE, min_size=2, out_format='juicebox'<\/span>)
New names:
\u2022 `chr` -> `chr...1`
\u2022 `start` -> `start...2`
\u2022 `end` -> `end...3`
\u2022 `chr` -> `chr...4`
\u2022 `start` -> `start...5`
\u2022 `end` -> `end...6`
Error in<\/span> `mutate()`:
\u2139 In argument: `start = format(start, scientific = FALSE)`.
Caused by error:
! `start` must be size 1709 or 1, not 14.
<\/code><\/pre>\n
\u5f53\u4ee5\u4e3a\u53ef\u4ee5\u6b63\u5e38\u8fd0\u884c\u65f6\uff0c\u8f6f\u4ef6\u53c8\u544a\u8bc9\u4f60\u6ca1\u90a3\u4e48\u7b80\u5355\u3002\u5e2e\u52a9\u4fe1\u606f\u4e2d\u867d\u7136\u63d0\u793a\u53ef\u4ee5\u901a\u8fc7out_format\u53c2\u6570\u4fee\u6539\u8f93\u51fa\u683c\u5f0f\uff1ajuicebox\u3001bedpe\u3001hicexplorer\u3001bed\uff0c\u4f46\u662f\u4e00\u8fd0\u884c\u53c8\u6389\u5165\u53e6\u5916\u4e00\u4e2a\u5751\uff1a<\/p>\n
\"\u8dd1\u5305\u8f6f\u4ef6\u65e0\u6cd5\u8fd0\u884c\"<\/p>\n
\u7b97\u4e86\uff0c\u771f\u5fc3\u7d2f\u4e86\uff0c\u4e0d\u518d\u6298\u817e\u4e86\uff0c\u8fd9\u4e2a\u65f6\u5019\u53ea\u60f3\u5bf9\u8f6f\u4ef6\u8bf4\u4e00\u53e5\uff1a\u5c31\u6b64\u522b\u8fc7\uff0c\u540e\u4f1a\u65e0\u671f\uff01<\/p>\n
\u7ed3\u8bed
\n\u5206\u6790\u65f6\u8fd8\u662f\u9009\u62e9\u77e5\u540d\u5ea6\u597d\u3001\u51fa\u573a\u7387\u9ad8\u7684\u8f6f\u4ef6\u5427\uff0c\u6bd5\u7adf\u8fd9\u6837\u7684\u8f6f\u4ef6\u66f4\u52a0\u6210\u719f\uff0c\u5373\u4f7f\u51fa\u4e86\u95ee\u9898\u4e5f\u80fd\u5feb\u901f\u627e\u5230\u76f8\u5e94\u7684\u89e3\u51b3\u529e\u6cd5\uff0c\u7528\u4e0d\u7740\u82b1\u8d39\u5927\u91cf\u7684\u65f6\u95f4\u5728\u8f6f\u4ef6\u4e0a\u3002\u9664\u975e\u6ca1\u6709\u66f4\u597d\u7684\u9009\u62e9\uff0c\u5426\u5219\u8fd8\u662f\u4e0d\u8981\u5543\u786c\u9aa8\u5934\u7684\u597d\uff0c\u4e0d\u7136\u65f6\u95f4\u6d6a\u8d39\u4e0d\u8bf4\u8fd8\u53ef\u80fd\u6d88\u5316\u4e0d\u826f\uff0c\u4e0d\u5982\u4e00\u5f00\u59cb\u5c31\u907f\u800c\u8fdc\u4e4b\u3002<\/p>\n
\u5f53\u7136\uff0c\u4e0d\u53ef\u5426\u8ba4\uff0c\u9020\u6210\u8f6f\u4ef6\u65e0\u6cd5\u6b63\u5e38\u8fd0\u884c\u7684\u539f\u56e0\u5f88\u53ef\u80fd\u662f\u4e0e\u5f00\u53d1\u8005\u7684\u73af\u5883\u4e0d\u540c\u3002\u6240\u4ee5\uff0c\u5206\u6790\u65f6\u4f7f\u7528\u8f6f\u4ef6\u66f4\u591a\u7684\u65f6\u5019\u90fd\u662f\u5728\u4e0e\u8f6f\u4ef6\u7684\u73af\u5883\u548c\u53c2\u6570\u4f5c\u6597\u4e89\uff0c\u8eba\u4e0d\u5e73\u7684\u65f6\u5019\u8fd8\u662f\u8981\u652f\u68f1\u8d77\u6765\uff1a\u751f\u4fe1\u8def\u4e0d\u5e73\uff0c\u8fd8\u9700\u8981\u7f1d\u7f1d\u8865\u8865\u3002<\/p>\n
\u5f80\u671f\u56de\u987e<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
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