lr_kallisto_example.ipynb

{
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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/pachterlab/LSRRSRLFKOTWMWMP_2024/blob/main/lr_kallisto_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0HemxoLMyYFy",
      "metadata": {
        "id": "0HemxoLMyYFy"
      },
      "source": [
        "###lr-kallisto Example single-cell notebook for processing ONT single-nuclei data, starting with seqspec and using splitcode for extraction"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "MxKzVloVkADW",
      "metadata": {
        "id": "MxKzVloVkADW"
      },
      "source": [
        "#Introduction: Pseudoalignment of single-cell long read RNA seq data\n",
        "\n",
        "References:\n",
        "[1] Loving, R, Sullivan, DK, Reese, F, Rebboah, E, Sakr, J, Rezaie, N, Liang, HY, Filimban, G, Kawauchi, S, Oakes, C, Trout, D, Williams, BA, MacGregor, G, Wold, BJ, Mortazavi, A, Pachter, L Long-read sequencing transcriptome quantification with lr-kallisto. bioRxiv 2024.07.19.604364 https://doi.org/10.1101/2024.07.19.604364\n",
        "\n",
        "[2] Sullivan DK, Pachter L. (2024). Flexible parsing, interpretation, and editing of technical sequences with splitcode. Bioinformatics. https://doi.org/10.1093/bioinformatics/btae331\n",
        "\n",
        "[3] Ali Sina Booeshaghi, Xi Chen, Lior Pachter, A machine-readable specification for genomics assays, Bioinformatics, Volume 40, Issue 4, April 2024, btae168.\n",
        "\n",
        "kallisto can perform long-read pseudoalignment of nucleotide sequences against a large k-mer reference while retaining single-cell (for single-cell RNA sequencing data) or sample (for bulk RNA seq data) resolution. To perform long-read pseudoalignment, first add -k 63 to kb ref and, second, add the --long flag to the kb count commands.\n",
        "\n",
        "Long-read pseudoalignment is performed by the longer k-mer length improving the quality of mapping k-mers in the higher sequencing error rates (relative to short read sequencing), making it more probable that the read originates from the transcript compatibility class it maps to. As k increases, the number of distinct k-mers also increases, but the number of contigs decreases. This implies that the number of transcripts in a transcript compatibility class decreases on average with increasing length of k. Overall, the complexity of the T-DBG decreases (Supplementary Fig. 5), increasing the probability of the read originating from the transcript compatibility class it is mapping to. Furthermore, this also increases the probability of the intersection of equivalence classes being nonempty, which increases the overall mapping rate.\n",
        "\n",
        "The workflow can be executed in a few lines of code, and computational requirements do not exceed those of a standard laptop. Building on kallisto’s versatility, the workflow is compatible with all state-of-the-art single-cell and bulk RNA sequencing methods, including but not limited to SMART-Seq [add citation]_ and SPLiT-Seq [add citation]_ (including Parse Biosciences) and performance is state-of-the-art on both PacBio and Oxford Nanopore Technologies long-read data.\n",
        "\n",
        "Note: For long-read single-cell data to be processed with lr-kallisto some preprocessing steps are required. Here we present the use of splitcode [2] and seqspec [3] to facilitate an automated processing of LR-SPLiT-Seq. seqspec is used to create a configuration file for splitcode to extract barcodes, umis, and the biological sequences from the reads [2][3]. seqspec requires as input a machine readable specification file for the sample protocol that is in the seqspec format. splitcode can then be called on the reads with the configuration file created by seqspec to extract from the reads the barcodes, umis, and biological sequences. The output of splitcode can be piped directly into lr-kallisto or output to files that are processed with lr-kallisto.\n",
        "\n",
        "The long-read pseudoalignment workflows can be used to align RNA sequencing data to any transcriptome reference"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "w1AMZTe8jUUa",
      "metadata": {
        "id": "w1AMZTe8jUUa"
      },
      "source": [
        "Downloading splitcode (v0.30.0) source code from github\n",
        "\n",
        "Installing splitcode from source"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "s9VYj8LXJh1G",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "s9VYj8LXJh1G",
        "outputId": "e7ff8224-6311-4553-9dd6-4a385892732d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'splitcode'...\n",
            "remote: Enumerating objects: 2833, done.\u001b[K\n",
            "remote: Counting objects: 100% (230/230), done.\u001b[K\n",
            "remote: Compressing objects: 100% (117/117), done.\u001b[K\n",
            "remote: Total 2833 (delta 132), reused 197 (delta 111), pack-reused 2603 (from 1)\u001b[K\n",
            "Receiving objects: 100% (2833/2833), 15.76 MiB | 10.25 MiB/s, done.\n",
            "Resolving deltas: 100% (1557/1557), done.\n",
            "\u001b[0mCMake Deprecation Warning at CMakeLists.txt:1 (cmake_minimum_required):\n",
            "  Compatibility with CMake < 3.5 will be removed from a future version of\n",
            "  CMake.\n",
            "\n",
            "  Update the VERSION argument <min> value or use a ...<max> suffix to tell\n",
            "  CMake that the project does not need compatibility with older versions.\n",
            "\n",
            "\u001b[0m\n",
            "-- The C compiler identification is GNU 11.4.0\n",
            "-- The CXX compiler identification is GNU 11.4.0\n",
            "-- Detecting C compiler ABI info\n",
            "-- Detecting C compiler ABI info - done\n",
            "-- Check for working C compiler: /usr/bin/cc - skipped\n",
            "-- Detecting C compile features\n",
            "-- Detecting C compile features - done\n",
            "-- Detecting CXX compiler ABI info\n",
            "-- Detecting CXX compiler ABI info - done\n",
            "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n",
            "-- Detecting CXX compile features\n",
            "-- Detecting CXX compile features - done\n",
            "\u001b[0mshared build\u001b[0m\n",
            "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD\n",
            "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success\n",
            "-- Found Threads: TRUE\n",
            "-- Found ZLIB: /usr/lib/x86_64-linux-gnu/libz.so (found version \"1.2.11\")\n",
            "\u001b[0musing htslib\u001b[0m\n",
            "-- Configuring done (3.1s)\n",
            "-- Generating done (0.1s)\n",
            "-- Build files have been written to: /content/splitcode/build\n",
            "[  7%] \u001b[34m\u001b[1mCreating directories for 'htslib'\u001b[0m\n",
            "[ 15%] \u001b[34m\u001b[1mNo download step for 'htslib'\u001b[0m\n",
            "[ 23%] \u001b[34m\u001b[1mNo update step for 'htslib'\u001b[0m\n",
            "[ 30%] \u001b[34m\u001b[1mNo patch step for 'htslib'\u001b[0m\n",
            "[ 38%] \u001b[34m\u001b[1mNo configure step for 'htslib'\u001b[0m\n",
            "[ 46%] \u001b[34m\u001b[1mPerforming build step for 'htslib'\u001b[0m\n",
            "\u001b[01m\u001b[Kbgzf.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbgzf_write\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kbgzf.c:733:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmisleading-indentation\u0007-Wmisleading-indentation\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  733 |     \u001b[01;35m\u001b[Kif\u001b[m\u001b[K ( !fp->is_compressed )\n",
            "      |     \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kbgzf.c:736:9:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            "  736 |         \u001b[01;36m\u001b[Kconst\u001b[m\u001b[K uint8_t *input = (const uint8_t*)data;\n",
            "      |         \u001b[01;36m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kfaidx.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfai_load\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kfaidx.c:265:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kelse\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmisleading-indentation\u0007-Wmisleading-indentation\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  265 |     \u001b[01;35m\u001b[Kelse\u001b[m\u001b[K\n",
            "      |     \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kfaidx.c:268:9:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kelse\u001b[m\u001b[K’\n",
            "  268 |         \u001b[01;36m\u001b[Kif\u001b[m\u001b[K (fp == 0) {\n",
            "      |         \u001b[01;36m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Khts.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Khts_idx_init\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Khts.c:522:63:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Koverflow in conversion from ‘\u001b[01m\u001b[Kuint32_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’} to ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ changes value from ‘\u001b[01m\u001b[Kidx->z.last_bin = 4294967295\u001b[m\u001b[K’ to ‘\u001b[01m\u001b[K-1\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Woverflow\u0007-Woverflow\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  522 |         idx->z.save_bin = idx->z.save_tid = idx->z.last_tid = \u001b[01;35m\u001b[Kidx\u001b[m\u001b[K->z.last_bin = 0xffffffffu;\n",
            "      |                                                               \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/include/string.h:535\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Ksam.c:3\u001b[m\u001b[K:\n",
            "In function ‘\u001b[01m\u001b[Kstrncpy\u001b[m\u001b[K’,\n",
            "    inlined from ‘\u001b[01m\u001b[Kbam_hdr_write\u001b[m\u001b[K’ at \u001b[01m\u001b[Ksam.c:130:2\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/string_fortified.h:95:10:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin_strncpy\u001b[m\u001b[K’ output truncated before terminating nul copying 4 bytes from a string of the same length [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wstringop-truncation\u0007-Wstringop-truncation\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "   95 |   return \u001b[01;35m\u001b[K__builtin___strncpy_chk (__dest, __src, __len,\u001b[m\u001b[K\n",
            "      |          \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "   96 | \u001b[01;35m\u001b[K                                  __glibc_objsize (__dest))\u001b[m\u001b[K;\n",
            "      |                                   \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ktbx.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktbx_seqnames\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Ktbx.c:285:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmisleading-indentation\u0007-Wmisleading-indentation\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  285 |     \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K (tid=0; tid<m; tid++)\n",
            "      |     \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ktbx.c:287:9:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n",
            "  287 |         \u001b[01;36m\u001b[K*\u001b[m\u001b[Kn = m;\n",
            "      |         \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kvcf.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvcf_format\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kvcf.c:1909:25:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmisleading-indentation\u0007-Wmisleading-indentation\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            " 1909 |                         \u001b[01;35m\u001b[Kif\u001b[m\u001b[K ( !first ) kputc(';', s); first = 0;\n",
            "      |                         \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kvcf.c:1909:54:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            " 1909 |                         if ( !first ) kputc(';', s); \u001b[01;36m\u001b[Kfirst\u001b[m\u001b[K = 0;\n",
            "      |                                                      \u001b[01;36m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kvcf.c:1948:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmisleading-indentation\u0007-Wmisleading-indentation\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            " 1948 |                     \u001b[01;35m\u001b[Kif\u001b[m\u001b[K (!first) kputc(':', s); first = 0;\n",
            "      |                     \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kvcf.c:1948:48:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            " 1948 |                     if (!first) kputc(':', s); \u001b[01;36m\u001b[Kfirst\u001b[m\u001b[K = 0;\n",
            "      |                                                \u001b[01;36m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[Khtslib/vcf.h:12\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Khtslib/vcfutils.h:8\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kvcfutils.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[Kvcfutils.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbcf_remove_alleles\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kvcfutils.c:291:25:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around assignment used as truth value [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wparentheses\u0007-Wparentheses\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  291 |                 assert( \u001b[01;35m\u001b[Kn\u001b[m\u001b[K=nG_ori );\n",
            "      |                         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcram/cram_io.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcram_populate_ref\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kcram/cram_io.c:1529:34:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K.tmp_\u001b[m\u001b[K’ directive writing 5 bytes into a region of size between 1 and 4096 [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat-overflow=\u0007-Wformat-overflow=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            " 1529 |             sprintf(path_tmp, \"%s\u001b[01;35m\u001b[K.tmp_\u001b[m\u001b[K%d\", path, /*getpid(),*/ i);\n",
            "      |                                  \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcram/cram_io.c:1529:31:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kdirective argument in the range [0, 2147483647]\n",
            " 1529 |             sprintf(path_tmp, \u001b[01;36m\u001b[K\"%s.tmp_%d\"\u001b[m\u001b[K, path, /*getpid(),*/ i);\n",
            "      |                               \u001b[01;36m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/include/stdio.h:894\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kcram/cram_io.c:51\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/stdio2.h:38:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin___sprintf_chk\u001b[m\u001b[K’ output between 7 and 4111 bytes into a destination of size 4096\n",
            "   38 |   return \u001b[01;36m\u001b[K__builtin___sprintf_chk (__s, __USE_FORTIFY_LEVEL - 1,\u001b[m\u001b[K\n",
            "      |          \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "   39 | \u001b[01;36m\u001b[K                                  __glibc_objsize (__s), __fmt,\u001b[m\u001b[K\n",
            "      |                                   \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "   40 | \u001b[01;36m\u001b[K                                  __va_arg_pack ())\u001b[m\u001b[K;\n",
            "      |                                   \u001b[01;36m\u001b[K~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/include/string.h:535\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kcram/cram_io.c:55\u001b[m\u001b[K:\n",
            "In function ‘\u001b[01m\u001b[Kstrncpy\u001b[m\u001b[K’,\n",
            "    inlined from ‘\u001b[01m\u001b[Kfull_path\u001b[m\u001b[K’ at \u001b[01m\u001b[Kcram/cram_io.c:2917:6\u001b[m\u001b[K,\n",
            "    inlined from ‘\u001b[01m\u001b[Kcram_write_SAM_hdr\u001b[m\u001b[K’ at \u001b[01m\u001b[Kcram/cram_io.c:2987:3\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/string_fortified.h:95:10:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin_strncpy\u001b[m\u001b[K’ specified bound 4096 equals destination size [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wstringop-truncation\u0007-Wstringop-truncation\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "   95 |   return \u001b[01;35m\u001b[K__builtin___strncpy_chk (__dest, __src, __len,\u001b[m\u001b[K\n",
            "      |          \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "   96 | \u001b[01;35m\u001b[K                                  __glibc_objsize (__dest))\u001b[m\u001b[K;\n",
            "      |                                   \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "In function ‘\u001b[01m\u001b[Kstrncpy\u001b[m\u001b[K’,\n",
            "    inlined from ‘\u001b[01m\u001b[Kfull_path\u001b[m\u001b[K’ at \u001b[01m\u001b[Kcram/cram_io.c:2909:2\u001b[m\u001b[K,\n",
            "    inlined from ‘\u001b[01m\u001b[Kcram_write_SAM_hdr\u001b[m\u001b[K’ at \u001b[01m\u001b[Kcram/cram_io.c:2987:3\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/string_fortified.h:95:10:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin_strncpy\u001b[m\u001b[K’ specified bound 4096 equals destination size [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wstringop-truncation\u0007-Wstringop-truncation\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "   95 |   return \u001b[01;35m\u001b[K__builtin___strncpy_chk (__dest, __src, __len,\u001b[m\u001b[K\n",
            "      |          \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "   96 | \u001b[01;35m\u001b[K                                  __glibc_objsize (__dest))\u001b[m\u001b[K;\n",
            "      |                                   \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "In function ‘\u001b[01m\u001b[Kstrncpy\u001b[m\u001b[K’,\n",
            "    inlined from ‘\u001b[01m\u001b[Kcram_dopen\u001b[m\u001b[K’ at \u001b[01m\u001b[Kcram/cram_io.c:3291:2\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/string_fortified.h:95:10:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin_strncpy\u001b[m\u001b[K’ specified bound 20 equals destination size [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wstringop-truncation\u0007-Wstringop-truncation\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "   95 |   return \u001b[01;35m\u001b[K__builtin___strncpy_chk (__dest, __src, __len,\u001b[m\u001b[K\n",
            "      |          \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "   96 | \u001b[01;35m\u001b[K                                  __glibc_objsize (__dest))\u001b[m\u001b[K;\n",
            "      |                                   \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[Kcram/sam_header.c:36\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[Kcram/sam_header.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ksam_hdr_add_lines\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kcram/sam_header.c:328:20:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around assignment used as truth value [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wparentheses\u0007-Wparentheses\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  328 |             assert(\u001b[01;35m\u001b[Kp\u001b[m\u001b[K->next = t);\n",
            "      |                    \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcram/sam_header.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ksam_hdr_vadd\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kcram/sam_header.c:449:16:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around assignment used as truth value [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wparentheses\u0007-Wparentheses\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  449 |         assert(\u001b[01;35m\u001b[Kp\u001b[m\u001b[K->next = t);\n",
            "      |                \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/include/string.h:535\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kcram/string_alloc.c:39\u001b[m\u001b[K:\n",
            "In function ‘\u001b[01m\u001b[Kstrncpy\u001b[m\u001b[K’,\n",
            "    inlined from ‘\u001b[01m\u001b[Kstring_ndup\u001b[m\u001b[K’ at \u001b[01m\u001b[Kcram/string_alloc.c:149:5\u001b[m\u001b[K,\n",
            "    inlined from ‘\u001b[01m\u001b[Kstring_dup\u001b[m\u001b[K’ at \u001b[01m\u001b[Kcram/string_alloc.c:141:12\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/include/x86_64-linux-gnu/bits/string_fortified.h:95:10:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[K__builtin_strncpy\u001b[m\u001b[K’ output truncated before terminating nul copying as many bytes from a string as its length [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wstringop-truncation\u0007-Wstringop-truncation\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "   95 |   return \u001b[01;35m\u001b[K__builtin___strncpy_chk (__dest, __src, __len,\u001b[m\u001b[K\n",
            "      |          \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "   96 | \u001b[01;35m\u001b[K                                  __glibc_objsize (__dest))\u001b[m\u001b[K;\n",
            "      |                                   \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcram/string_alloc.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kstring_dup\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kcram/string_alloc.c:141:12:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Klength computed here\n",
            "  141 |     return \u001b[01;36m\u001b[Kstring_ndup(a_str, instr, strlen(instr))\u001b[m\u001b[K;\n",
            "      |            \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcram/vlen.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvflen\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kcram/vlen.c:121:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Ki\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-but-set-variable\u0007-Wunused-but-set-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  121 |     int \u001b[01;35m\u001b[Ki\u001b[m\u001b[K;\n",
            "      |         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "[ 53%] \u001b[34m\u001b[1mNo install step for 'htslib'\u001b[0m\n",
            "[ 61%] \u001b[34m\u001b[1mCompleted 'htslib'\u001b[0m\n",
            "[ 61%] Built target htslib\n",
            "[ 69%] \u001b[32mBuilding CXX object src/CMakeFiles/splitcode_core.dir/ProcessReads.cpp.o\u001b[0m\n",
            "[ 76%] \u001b[32mBuilding CXX object src/CMakeFiles/splitcode_core.dir/main.cpp.o\u001b[0m\n",
            "[ 84%] \u001b[32m\u001b[1mLinking CXX static library libsplitcode_core.a\u001b[0m\n",
            "[ 84%] Built target splitcode_core\n",
            "[ 92%] \u001b[32mBuilding CXX object src/CMakeFiles/splitcode.dir/main.cpp.o\u001b[0m\n",
            "[100%] \u001b[32m\u001b[1mLinking CXX executable splitcode\u001b[0m\n",
            "[100%] Built target splitcode\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/pachterlab/splitcode\n",
        "!cd splitcode; mkdir build; cd build; cmake ..; make"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7tenQqndjnEx",
      "metadata": {
        "id": "7tenQqndjnEx"
      },
      "source": [
        "Show splitcode version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "rI53BIjdviWP",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rI53BIjdviWP",
        "outputId": "a781aa20-4769-4948-ce08-c6afb2793973"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "splitcode, version 0.30.0\n"
          ]
        }
      ],
      "source": [
        "#show splitcode version\n",
        "!splitcode/build/src/splitcode --version"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "l9jrl0blki51",
      "metadata": {
        "id": "l9jrl0blki51"
      },
      "source": [
        "#For this notebook we use a subset of 2 million reads of the exome capture cortex data discussed in Fig1 of [1]. We now download the example data and seqspec for the data with supporting files, such as on-lists for correcting barcodes and configuration files to use with splitcode to do SPLiT-seq specific correction and merging of barcodes with different priming strategies for the same cell."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3-G508bPXNGE",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "3-G508bPXNGE",
        "outputId": "b60db350-e2dd-4dfd-9e4b-fbe4e9bdbdbc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2024-12-02 03:45:23--  https://zenodo.org/records/14146317/files/lr-kallisto_example.tar.gz\n",
            "Resolving zenodo.org (zenodo.org)... 188.185.45.92, 188.185.48.194, 188.185.43.25, ...\n",
            "Connecting to zenodo.org (zenodo.org)|188.185.45.92|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 970627147 (926M) [application/octet-stream]\n",
            "Saving to: ‘lr-kallisto_example.tar.gz’\n",
            "\n",
            "lr-kallisto_example 100%[===================>] 925.66M  28.2MB/s    in 36s     \n",
            "\n",
            "2024-12-02 03:46:00 (25.8 MB/s) - ‘lr-kallisto_example.tar.gz’ saved [970627147/970627147]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#Get example data and seqspec with supporting on-list files and configs for barcode correction and merging that is SPLiT-Seq specific\n",
        "!wget https://zenodo.org/records/14146317/files/lr-kallisto_example.tar.gz"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "voq3gb5Ok4qV",
      "metadata": {
        "id": "voq3gb5Ok4qV"
      },
      "source": [
        "Next we uncompress the example data folder to access the contents and copy the files to the working directory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "CEj2Bkm-XO32",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "CEj2Bkm-XO32",
        "outputId": "06a7fbc3-c6c9-4ded-b3f3-358dee51832b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "lr-kallisto_example/\n",
            "lr-kallisto_example/spec.yaml\n",
            "lr-kallisto_example/13G-gc_5W.fastq.gz\n",
            "lr-kallisto_example/config.mergeRT\n",
            "lr-kallisto_example/r2_3.txt\n",
            "lr-kallisto_example/config_RT_randO.txt\n",
            "lr-kallisto_example/config-correct.txt\n",
            "lr-kallisto_example/config_RT_polyT.txt\n",
            "lr-kallisto_example/r1_R.txt\n",
            "lr-kallisto_example/r1_T.txt\n",
            "lr-kallisto_example/13G_2M.fastq.gz\n"
          ]
        }
      ],
      "source": [
        "#uncompress folder\n",
        "!tar -xvf lr-kallisto_example.tar.gz\n",
        "!cp lr-kallisto_example/r*txt ."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6dQxQNrFlFbs",
      "metadata": {
        "id": "6dQxQNrFlFbs"
      },
      "source": [
        "We now install seqspec using pip to install seqspec from github and use `seqspec index` command to create the splitcode config for demultiplexing the reads with splitcode."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "IrYJjuM9Qg_a",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "IrYJjuM9Qg_a",
        "outputId": "2a24005d-343c-49dc-c99a-dd367923d10a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/pachterlab/seqspec\n",
            "  Cloning https://github.com/pachterlab/seqspec to /tmp/pip-req-build-y5u1g6so\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/pachterlab/seqspec /tmp/pip-req-build-y5u1g6so\n",
            "  Resolved https://github.com/pachterlab/seqspec to commit 1475ff00eac673af900d9be4fb36f23716058da4\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pyyaml>=6.0 in /usr/local/lib/python3.10/dist-packages (from seqspec==0.3.1) (6.0.2)\n",
            "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from seqspec==0.3.1) (4.23.0)\n",
            "Collecting newick (from seqspec==0.3.1)\n",
            "  Downloading newick-1.9.0-py2.py3-none-any.whl.metadata (8.4 kB)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from seqspec==0.3.1) (2.32.3)\n",
            "Collecting biopython (from seqspec==0.3.1)\n",
            "  Downloading biopython-1.84-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from seqspec==0.3.1) (24.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from biopython->seqspec==0.3.1) (1.26.4)\n",
            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->seqspec==0.3.1) (24.2.0)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema->seqspec==0.3.1) (2024.10.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema->seqspec==0.3.1) (0.35.1)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema->seqspec==0.3.1) (0.21.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->seqspec==0.3.1) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->seqspec==0.3.1) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->seqspec==0.3.1) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->seqspec==0.3.1) (2024.8.30)\n",
            "Downloading biopython-1.84-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.2 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m29.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading newick-1.9.0-py2.py3-none-any.whl (15 kB)\n",
            "Building wheels for collected packages: seqspec\n",
            "  Building wheel for seqspec (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqspec: filename=seqspec-0.3.1-py3-none-any.whl size=47739 sha256=72dbaac76ab3efc29fb99c6a9ffd12bd3d5edaaf5b7fe8d5c4854d83ebb9dfbd\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ufx9wed9/wheels/a3/4d/76/1e56215ad56c936bb0c24c4be01c5e8f7aeb0af528d415423c\n",
            "Successfully built seqspec\n",
            "Installing collected packages: newick, biopython, seqspec\n",
            "Successfully installed biopython-1.84 newick-1.9.0 seqspec-0.3.1\n"
          ]
        }
      ],
      "source": [
        "#Install seqspec\n",
        "!pip install git+https://github.com/pachterlab/seqspec\n",
        "\n",
        "!seqspec index -m rna -s file -t splitcode lr-kallisto_example/spec.yaml > ONT.config"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fFqfS2e6lelJ",
      "metadata": {
        "id": "fFqfS2e6lelJ"
      },
      "source": [
        "Next we use sed to change the error tolerance to only allow 1 error for runtime considerations in Google Colab; in practice, we have found the tolerance of 3 errors to be a good choice for SPLiT-Seq ONT R10.4 data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "JBhyO5nvgyHE",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "JBhyO5nvgyHE"
      },
      "outputs": [],
      "source": [
        "#Make config less error tolerant, allowing one error instead of 3 errors\n",
        "!sed -i 's/3:3:3/1:1:1/g' ONT.config;"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "IzoVPRD6lxps",
      "metadata": {
        "id": "IzoVPRD6lxps"
      },
      "source": [
        "We can now use splitcode to extract barcodes, cDNAs, and umis for the single-nuclei ONT data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "-O1c6eTywLj_",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "-O1c6eTywLj_",
        "outputId": "412f1d05-4270-4404-ebf2-6daceddfec3d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "* Forcing --gzip because all output file names end in .gz\n",
            "* Using a list of 8 tags (vector size: 8; map size: 6,286; num elements in map: 6,344)\n",
            "* will process sample 1: lr-kallisto_example/13G_2M.fastq.gz\n",
            "* processing the reads ...\n",
            "done \n",
            "* processed 1,000,000 reads\n"
          ]
        }
      ],
      "source": [
        "#extract barcodes, cDNAs, and umis\n",
        "!splitcode/build/src/splitcode -c ONT.config -t 2 lr-kallisto_example/13G_2M.fastq.gz -o igvfb01_13G_scmodified/igvfb01_13G_5W.fastq.gz"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ZSRdPjNUnF9V",
      "metadata": {
        "id": "ZSRdPjNUnF9V"
      },
      "source": [
        "We now reorient the reads as they are extracted in forward, complement, reverse, and reverse complement orientations and merge the results.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "sBlcYQkezoUc",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "sBlcYQkezoUc",
        "outputId": "c0482f25-baf1-40a4-d7bb-896ddb731f42"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cat f_barcode.fastq c_barcode.fastq r_barcode.fastq rc_barcode.fastq > _barcode.fastq\n",
            "cat f_cDNA.fastq c_cDNA.fastq r_cDNA.fastq rc_cDNA.fastq > _cDNA.fastq\n",
            "cat f_umi.fastq c_umi.fastq r_umi.fastq rc_umi.fastq > _umi.fastq\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "samples = [''] #,'13G']\n",
        "\n",
        "\n",
        "for sample in samples:\n",
        "    files_f = [sample+'f', sample+'c']\n",
        "    files_r = [sample+'r', sample+'rc']\n",
        "\n",
        "    for f in files_r+files_f:\n",
        "        for fastq in [\"barcode\", \"cDNA\", \"umi\"]:\n",
        "            os.system(\"gunzip -f \"+f+\"_\"+fastq+\".fastq.gz\")\n",
        "\n",
        "    c = 0\n",
        "    writelines = \"\"\n",
        "    fastqc=open(sample+'c_barcode.fastq', 'r')\n",
        "    ofastqc=open(sample+'c_barcode.tmp.fastq', 'w+')\n",
        "    for l in fastqc.readlines():\n",
        "        c+=1\n",
        "        if l.startswith(\"@\") or l.startswith(\"+\"):\n",
        "            writelines+=l\n",
        "        elif l != \"\\n\":\n",
        "            writelines+=l[0:8][::-1]+l[8:16][::-1]+l[16:24][::-1]+\"\\n\"\n",
        "        else:\n",
        "            writelines+=l\n",
        "        if c == 4:\n",
        "            ofastqc.write(writelines)\n",
        "            c = 0\n",
        "            writelines = \"\"\n",
        "    fastqc.close()\n",
        "    ofastqc.close()\n",
        "    os.system(\"mv \"+sample+'c_barcode.tmp.fastq '+sample+'c_barcode.fastq')\n",
        "\n",
        "    fastqc=open(sample+'r_barcode.fastq', 'r')\n",
        "    ofastqc=open(sample+'r_barcode.tmp.fastq', 'w+')\n",
        "    for l in fastqc.readlines():\n",
        "        c+=1\n",
        "        if l.startswith(\"@\") or l.startswith(\"+\"):\n",
        "            writelines+=l\n",
        "        elif l != \"\\n\":\n",
        "            writelines+=l[::-1]+\"\\n\"\n",
        "        else:\n",
        "            writelines+=l\n",
        "        if c == 4:\n",
        "            ofastqc.write(writelines)\n",
        "            c = 0\n",
        "            writelines = \"\"\n",
        "    fastqc.close()\n",
        "    ofastqc.close()\n",
        "    os.system(\"mv \"+sample+'r_barcode.tmp.fastq '+sample+'r_barcode.fastq')\n",
        "\n",
        "    c = 0\n",
        "    writelines = \"\"\n",
        "    fastqc=open(sample+'rc_barcode.fastq', 'r')\n",
        "    ofastqc=open(sample+'rc_barcode.tmp.fastq', 'w+')\n",
        "    for l in fastqc.readlines():\n",
        "        c+=1\n",
        "        if l.startswith(\"@\") or l.startswith(\"+\"):\n",
        "            writelines+=l\n",
        "        elif l != \"\\n\":\n",
        "            writelines+=l[16:24]+l[8:16]+l[0:8]+\"\\n\"\n",
        "        else:\n",
        "            writelines+=l\n",
        "        if c == 4:\n",
        "            ofastqc.write(writelines)\n",
        "            c = 0\n",
        "            writelines = \"\"\n",
        "    fastqc.close()\n",
        "    ofastqc.close()\n",
        "    os.system(\"mv \"+sample+'rc_barcode.tmp.fastq '+sample+'rc_barcode.fastq')\n",
        "\n",
        "\n",
        "    for fastq in [\"barcode\", \"cDNA\", \"umi\"]:\n",
        "        cmd = \"cat \"\n",
        "        for f in files_f+files_r:\n",
        "            cmd += f+'_'+fastq+'.fastq '\n",
        "        cmd += '> '+sample+'_'+fastq+'.fastq'\n",
        "        print(cmd)\n",
        "        os.system(cmd)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "RVzxPsVunyZ-",
      "metadata": {
        "id": "RVzxPsVunyZ-"
      },
      "source": [
        "We now correct barcodes, and merge rand oligo primed and random hexamer primed together"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "BhHDW63l2i7d",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "BhHDW63l2i7d",
        "outputId": "53a09999-0482-4111-ae1c-eead83ad4674"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "* Using a list of 384 tags (vector size: 384; map size: 32,059; num elements in map: 42,792)\n",
            "* will process sample 1: _cDNA.fastq\n",
            "                         _barcode.fastq\n",
            "3M reads processed         \n",
            "done \n",
            "* processed 4,000,000 reads\n",
            "* Using a list of 384 tags (vector size: 384; map size: 32,059; num elements in map: 42,792)\n",
            "* will process sample 1: _umi.fastq\n",
            "                         _barcode.fastq\n",
            "3M reads processed         \n",
            "done \n",
            "* processed 4,000,000 reads\n",
            "* Using a list of 96 tags (vector size: 96; map size: 2,989; num elements in map: 3,168)\n",
            "* will process sample 1: 13G_barcode.fastq.gz\n",
            "* processing the reads ...\n",
            "done \n",
            "* processed 378,887 reads\n"
          ]
        }
      ],
      "source": [
        "#correct barcodes\n",
        "!splitcode/build/src/splitcode -c lr-kallisto_example/config-correct.txt --nFastqs=2 --select=0 --gzip -o 13G_cDNA.fastq.gz _cDNA.fastq _barcode.fastq -t 2;\n",
        "\n",
        "!splitcode/build/src/splitcode -c lr-kallisto_example/config-correct.txt --nFastqs=2 --select=0 --gzip -o 13G_umi.fastq.gz _umi.fastq _barcode.fastq -t 2;\n",
        "\n",
        "#merge randO and polyT\n",
        "!splitcode/build/src/splitcode -c lr-kallisto_example/config.mergeRT -o 13G_barcode.fastq 13G_barcode.fastq.gz -t 2;"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "N4h99HXkoA8L",
      "metadata": {
        "id": "N4h99HXkoA8L"
      },
      "source": [
        "We also correct and extract barcodes that are polyT primed only"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cFvWnIEO4IF4",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "cFvWnIEO4IF4",
        "outputId": "567d34e8-d195-4ea4-c462-89843afb9f74"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "* Using a list of 288 tags (vector size: 288; map size: 22,690; num elements in map: 31,560)\n",
            "* will process sample 1: 13G_cDNA.fastq.gz\n",
            "                         13G_barcode.fastq.gz\n",
            "* processing the reads ...\n",
            "done \n",
            "* processed 378,887 reads\n",
            "* Using a list of 288 tags (vector size: 288; map size: 22,690; num elements in map: 31,560)\n",
            "* will process sample 1: 13G_umi.fastq.gz\n",
            "                         13G_barcode.fastq.gz\n",
            "* processing the reads ...\n",
            "done \n",
            "* processed 378,887 reads\n"
          ]
        }
      ],
      "source": [
        "#correct barcodes and extract only polyT\n",
        "!splitcode/build/src/splitcode -c lr-kallisto_example/config_RT_polyT.txt --nFastqs=2 --select=0 --gzip -o 13G_cDNA_polyT.fastq.gz 13G_cDNA.fastq.gz 13G_barcode.fastq.gz -t 2;\n",
        "\n",
        "!splitcode/build/src/splitcode -c lr-kallisto_example/config_RT_polyT.txt --nFastqs=2 --select=0 --gzip -o 13G_umi_polyT.fastq.gz 13G_umi.fastq.gz 13G_barcode.fastq.gz -t 2;\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ub8fsLHZoHcv",
      "metadata": {
        "id": "ub8fsLHZoHcv"
      },
      "source": [
        "Likewise, we correct and extract barcodes that are random oligo primed only"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Q4aHG0924JMG",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Q4aHG0924JMG",
        "outputId": "903547a5-798e-45b5-a8ec-1da64e475795"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "* Using a list of 288 tags (vector size: 288; map size: 22,913; num elements in map: 31,647)\n",
            "* will process sample 1: 13G_cDNA.fastq.gz\n",
            "                         13G_barcode.fastq.gz\n",
            "* processing the reads ...\n",
            "done \n",
            "* processed 378,887 reads\n",
            "* Using a list of 288 tags (vector size: 288; map size: 22,913; num elements in map: 31,647)\n",
            "* will process sample 1: 13G_umi.fastq.gz\n",
            "                         13G_barcode.fastq.gz\n",
            "* processing the reads ...\n",
            "done \n",
            "* processed 378,887 reads\n",
            "* Using a list of 96 tags (vector size: 96; map size: 2,989; num elements in map: 3,168)\n",
            "* will process sample 1: 13G_barcode_randO.fastq.gz\n",
            "* processing the reads ...\n",
            "done \n",
            "* processed 226,788 reads\n"
          ]
        }
      ],
      "source": [
        "#correct barcodes and extract only randO\n",
        "!splitcode/build/src/splitcode -c lr-kallisto_example/config_RT_randO.txt --nFastqs=2 --select=0 --gzip -o 13G_cDNA_randO.fastq.gz 13G_cDNA.fastq.gz 13G_barcode.fastq.gz -t 2;\n",
        "\n",
        "!splitcode/build/src/splitcode -c lr-kallisto_example/config_RT_randO.txt --nFastqs=2 --select=0 --gzip -o 13G_umi_randO.fastq.gz 13G_umi.fastq.gz 13G_barcode.fastq.gz -t 2;\n",
        "\n",
        "#switch randO and polyT\n",
        "!splitcode/build/src/splitcode -c lr-kallisto_example/config.mergeRT -o 13G_barcode_randO.fastq 13G_barcode_randO.fastq.gz -t 2;"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e9kO9y_uoMWG",
      "metadata": {
        "id": "e9kO9y_uoMWG"
      },
      "source": [
        "We then install kb and gget using pip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "raj15jWY0alU",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "raj15jWY0alU",
        "outputId": "1dfaf4bb-8531-4cf3-bb18-463f7979ff48"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.8/4.8 MB\u001b[0m \u001b[31m41.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m36.5/36.5 MB\u001b[0m \u001b[31m45.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.1/43.1 MB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m141.9/141.9 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m34.4/34.4 MB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.9/50.9 MB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m21.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.1/50.1 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m39.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.9/56.9 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m22.0/22.0 MB\u001b[0m \u001b[31m48.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.8/88.8 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.6/40.6 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m83.6/83.6 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for loompy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for session-info (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "#install gget and kb\n",
        "!pip install -q kb_python gget"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Gs_QihVdoRcx",
      "metadata": {
        "id": "Gs_QihVdoRcx"
      },
      "source": [
        "We now can either download references using `kb ref` or use `kb ref` and `gget ref` to download genome and annotation for a given species and build index and t2g from scratch."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7N6Wu_sV0eK-",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "7N6Wu_sV0eK-",
        "outputId": "bdfd379b-ba8a-4c9c-d8ea-a1f69df2606c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2024-12-04 18:08:15,925]    INFO [download] Downloading files for mouse (standard workflow) from https://github.com/pachterlab/kallisto-transcriptome-indices/releases/download/v1/mouse_index_standard.tar.xz to tmp/mouse_index_standard.tar.xz\n",
            "100% 82.8M/82.8M [00:01<00:00, 70.9MB/s]\n",
            "[2024-12-04 18:08:17,151]    INFO [download] Extracting files from tmp/mouse_index_standard.tar.xz\n",
            "[2024-12-04 18:08:45,549]    INFO [download] Downloading files for mouse (standard workflow) from https://github.com/pachterlab/kallisto-transcriptome-indices/releases/download/v1/mouse_index_standard_long.tar.xz to tmp/mouse_index_standard_long.tar.xz\n",
            "100% 74.8M/74.8M [00:01<00:00, 51.6MB/s]\n",
            "[2024-12-04 18:08:47,072]    INFO [download] Extracting files from tmp/mouse_index_standard_long.tar.xz\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\n!kb ref -k 63    -i mouse_k-63.idx     -g t2g.txt     -f1 fasta.fa     $(gget ref --ftp -w dna,gtf mouse)\\n'"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#download reference for mouse with k-mer size 31 or 63\n",
        "!kb ref -k 31 -d mouse -i mouse_k-31.idx -g mouse.t2g\n",
        "!kb ref -k 63 -d mouse -i mouse_k-63.idx -g mouse.t2g\n",
        "#alternatively build your own\n",
        "'''\n",
        "!kb ref -k 63\\\n",
        "    -i mouse_k-63.idx \\\n",
        "    -g t2g.txt \\\n",
        "    -f1 fasta.fa \\\n",
        "    $(gget ref --ftp -w dna,gtf mouse)\n",
        "'''"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "F5lL1VV3oilW",
      "metadata": {
        "id": "F5lL1VV3oilW"
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      "source": [
        "We can now use `kb count` to perform the psuedoalignment with lr-kallisto and the counting with bustools to produce cell x genes count matrices. Alternatively, you can use `--tcc --matrix-to-directories` to with `kb count` to also produce transcript-level matrices. We do this for both the merged barcodes as well as the only polyT primed barcodes and only random oligo primed barcodes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "sm2ETLfW1Saj",
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        },
        "id": "sm2ETLfW1Saj",
        "outputId": "99650968-8a43-4337-a864-867e7487d16d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2024-12-04 18:09:15,056]    INFO [count] Using index mouse_k-63.idx to generate BUS file to output from\n",
            "[2024-12-04 18:09:15,056]    INFO [count]         13G_cDNA.fastq.gz\n",
            "[2024-12-04 18:09:15,056]    INFO [count]         13G_umi.fastq.gz\n",
            "[2024-12-04 18:09:15,057]    INFO [count]         13G_barcode.fastq\n",
            "[2024-12-04 18:13:49,857]    INFO [count] Sorting BUS file output/output.bus to output/tmp/output.s.bus\n",
            "[2024-12-04 18:13:54,466]    INFO [count] On-list not provided\n",
            "[2024-12-04 18:13:54,466]    INFO [count] Generating on-list output/whitelist.txt from BUS file output/tmp/output.s.bus\n",
            "[2024-12-04 18:13:55,569]    INFO [count] Inspecting BUS file output/tmp/output.s.bus\n",
            "[2024-12-04 18:13:56,671]    INFO [count] Correcting BUS records in output/tmp/output.s.bus to output/tmp/output.s.c.bus with on-list output/whitelist.txt\n",
            "[2024-12-04 18:13:59,582]    INFO [count] Sorting BUS file output/tmp/output.s.c.bus to output/output.unfiltered.bus\n",
            "[2024-12-04 18:14:02,795]    INFO [count] Generating count matrix output/counts_unfiltered/cells_x_genes from BUS file output/output.unfiltered.bus\n",
            "[2024-12-04 18:14:04,850]    INFO [count] Writing gene names to file output/counts_unfiltered/cells_x_genes.genes.names.txt\n",
            "[2024-12-04 18:14:05,308] WARNING [count] 1599 gene IDs do not have corresponding valid gene names. These genes will use their gene IDs instead.\n",
            "[2024-12-04 18:14:16,524]    INFO [count] Using index mouse_k-63.idx to generate BUS file to output_randO from\n",
            "[2024-12-04 18:14:16,524]    INFO [count]         13G_cDNA_randO.fastq.gz\n",
            "[2024-12-04 18:14:16,525]    INFO [count]         13G_umi_randO.fastq.gz\n",
            "[2024-12-04 18:14:16,525]    INFO [count]         13G_barcode_randO.fastq\n",
            "[2024-12-04 18:17:00,084]    INFO [count] Sorting BUS file output_randO/output.bus to output_randO/tmp/output.s.bus\n",
            "[2024-12-04 18:17:03,092]    INFO [count] On-list not provided\n",
            "[2024-12-04 18:17:03,092]    INFO [count] Generating on-list output_randO/whitelist.txt from BUS file output_randO/tmp/output.s.bus\n",
            "[2024-12-04 18:17:04,203]    INFO [count] Inspecting BUS file output_randO/tmp/output.s.bus\n",
            "[2024-12-04 18:17:05,312]    INFO [count] Correcting BUS records in output_randO/tmp/output.s.bus to output_randO/tmp/output.s.c.bus with on-list output_randO/whitelist.txt\n",
            "[2024-12-04 18:17:08,225]    INFO [count] Sorting BUS file output_randO/tmp/output.s.c.bus to output_randO/output.unfiltered.bus\n",
            "[2024-12-04 18:17:11,436]    INFO [count] Generating count matrix output_randO/counts_unfiltered/cells_x_genes from BUS file output_randO/output.unfiltered.bus\n",
            "[2024-12-04 18:17:13,364]    INFO [count] Writing gene names to file output_randO/counts_unfiltered/cells_x_genes.genes.names.txt\n",
            "[2024-12-04 18:17:13,825] WARNING [count] 1599 gene IDs do not have corresponding valid gene names. These genes will use their gene IDs instead.\n",
            "[2024-12-04 18:17:23,450]    INFO [count] Using index mouse_k-63.idx to generate BUS file to output_polyT from\n",
            "[2024-12-04 18:17:23,450]    INFO [count]         13G_cDNA_polyT.fastq.gz\n",
            "[2024-12-04 18:17:23,450]    INFO [count]         13G_umi_polyT.fastq.gz\n",
            "[2024-12-04 18:17:23,450]    INFO [count]         13G_barcode_polyT.fastq.gz\n",
            "[2024-12-04 18:20:28,574]    INFO [count] Sorting BUS file output_polyT/output.bus to output_polyT/tmp/output.s.bus\n",
            "[2024-12-04 18:20:31,181]    INFO [count] On-list not provided\n",
            "[2024-12-04 18:20:31,181]    INFO [count] Generating on-list output_polyT/whitelist.txt from BUS file output_polyT/tmp/output.s.bus\n",
            "[2024-12-04 18:20:32,284]    INFO [count] Inspecting BUS file output_polyT/tmp/output.s.bus\n",
            "[2024-12-04 18:20:33,385]    INFO [count] Correcting BUS records in output_polyT/tmp/output.s.bus to output_polyT/tmp/output.s.c.bus with on-list output_polyT/whitelist.txt\n",
            "[2024-12-04 18:20:35,591]    INFO [count] Sorting BUS file output_polyT/tmp/output.s.c.bus to output_polyT/output.unfiltered.bus\n",
            "[2024-12-04 18:20:38,699]    INFO [count] Generating count matrix output_polyT/counts_unfiltered/cells_x_genes from BUS file output_polyT/output.unfiltered.bus\n",
            "[2024-12-04 18:20:40,515]    INFO [count] Writing gene names to file output_polyT/counts_unfiltered/cells_x_genes.genes.names.txt\n",
            "[2024-12-04 18:20:40,965] WARNING [count] 1599 gene IDs do not have corresponding valid gene names. These genes will use their gene IDs instead.\n"
          ]
        }
      ],
      "source": [
        "#Use kb count to pseudoalign and produce count matrices\n",
        "!kb count -k 63 --long --threshold 0.8 -i mouse_k-63.idx -g mouse.t2g -o output -x '2,0,24:1,0,10:0,0,0' 13G_cDNA.fastq.gz 13G_umi.fastq.gz 13G_barcode.fastq\n",
        "!kb count -k 63 --long --threshold 0.8 -i mouse_k-63.idx -g mouse.t2g -o output_randO -x '2,0,24:1,0,10:0,0,0' 13G_cDNA_randO.fastq.gz 13G_umi_randO.fastq.gz 13G_barcode_randO.fastq.gz\n",
        "!kb count -k 63 --long --threshold 0.8 -i mouse_k-63.idx -g mouse.t2g -o output_polyT -x '2,0,24:1,0,10:0,0,0' 13G_cDNA_polyT.fastq.gz 13G_umi_polyT.fastq.gz 13G_barcode_polyT.fastq.gz"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "da5aFgnyo7sW",
      "metadata": {
        "id": "da5aFgnyo7sW"
      },
      "source": [
        "#We can now visualize our results reading them into an Anndata and using scanpy to perform QC and then visualize the the results between the polyT priming and the merged barcodes. We first read in the count matrices to Anndata"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "96769eb5-b695-45da-952a-ee862dc8d542",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "96769eb5-b695-45da-952a-ee862dc8d542"
      },
      "outputs": [],
      "source": [
        "# Import packages and read in count matrix from polyT\n",
        "import scipy\n",
        "import anndata\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import scanpy as sc\n",
        "from sklearn.decomposition import TruncatedSVD\n",
        "from scipy import sparse, io\n",
        "import seaborn as sns\n",
        "\n",
        "matplotlib.rcParams.update({'font.size': 12})\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "# import data\n",
        "dir = 'output_polyT/counts_unfiltered' #'b01_nanopore_13G_single_cell'#'b01_nanopore_13G__sc_single_cell'\n",
        "adata = sc.read_mtx(dir+\"/cells_x_genes.mtx\")\n",
        "adata.obs.index = pd.read_csv(dir+\"/cells_x_genes.barcodes.txt\", header=None)[0].values\n",
        "adata.var.index = pd.read_csv(dir+\"/cells_x_genes.genes.names.txt\", header=None)[0].values\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b1feb36f-e8bc-4bb6-92a1-ec435d93a151",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "b1feb36f-e8bc-4bb6-92a1-ec435d93a151"
      },
      "outputs": [],
      "source": [
        "#Read in merged polyT and randO matrix\n",
        "dir = 'output_randO/counts_unfiltered' #'b01_nanopore_13G_single_cell'#'b01_nanopore_13G__sc_single_cell'\n",
        "Iadata = sc.read_mtx(dir+\"/cells_x_genes.mtx\")\n",
        "Iadata.obs.index = pd.read_csv(dir+\"/cells_x_genes.barcodes.txt\", header=None)[0].values\n",
        "Iadata.var.index = pd.read_csv(dir+\"/cells_x_genes.genes.names.txt\", header=None)[0].values\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "qqUnutuSpizX",
      "metadata": {
        "id": "qqUnutuSpizX"
      },
      "source": [
        "Below we show that the var of the Anndatas are the genes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "63837eed-c34c-435a-a4ee-49cca6ec8508",
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        "outputId": "79fc8904-2430-4b37-e0ef-abfeea740bd0"
      },
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              "\n",
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          },
          "execution_count": 18,
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      ],
      "source": [
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    {
      "cell_type": "markdown",
      "id": "6cIpxSD-pnf5",
      "metadata": {
        "id": "6cIpxSD-pnf5"
      },
      "source": [
        "We then show that the obs of the Anndata are the nuclei barcodes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7852d7ff-c5c9-4e85-9fea-0aa8172e7e4b",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-dd493a07-2196-4a15-8177-092ee6aef6af\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dd493a07-2196-4a15-8177-092ee6aef6af')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dd493a07-2196-4a15-8177-092ee6aef6af button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: []\n",
              "Index: [AAACATCGACGTATCAACATTTAC, AAACATCGAGTGGTCAGTTACGTA, AAACATCGATTGAGGAACTTAGCT, AAACATCGCCTCCTGACACTTTCA, AAACATCGCGAACTTAATGCTGCT, AAACATCGCTCAATGACTCTGTGC, AAACATCGGCTCGGTATTACCTGC, AAACATCGGTACGCAACTTCATCA, AAACATCGTGGAACAAACTCCTGC, AACAACCAAACAACCAACTTAGCT, AACAACCAAACGCTTACCTTTGCA, AACAACCAAACTCACCCTGACTTC, AACAACCAAAGAGATCACATTCAT, AACAACCAAAGAGATCCTTCATCA, AACAACCAACAGCAGACCAATTCT, AACAACCAACATTGGCCCCTTATA, AACAACCAACCACTGTATTTGGCA, AACAACCAACGCTCGAATTTGGCA, AACAACCAACGTATCATCATGCTC, AACAACCAAGCCATGCCTATTTCA, AACAACCAAGTACAAGCACTTTCA, AACAACCAAGTGGTCACATTTACA, AACAACCAATCCTGTACCTTTGCA, AACAACCAATTGAGGAACTGCTCT, AACAACCACAAGACTACATTTACA, AACAACCACCTAATCCATTTGGCA, AACAACCACCTCTATCACATTTAC, AACAACCACGCATACACTATTTCA, AACAACCACTGTAGCCACTTTACC, AACAACCAGAACAGGCACATTTGG, AACAACCAGCCACATAACTGCTCT, AACAACCAGGAGAACATCATGCTC, AACAACCAGTCGTAGAACTGCTCT, AACAACCATATCAGCACCTTTGCA, AACAACCATCTTCACAACATTTGG, AACAACCATCTTCACACTCTGTGC, AACAACCATGGCTTCACCCTTATA, AACAACCATGGTGGTAACTTTACC, AACCGAGAAACTCACCGTTACGTA, AACCGAGAAAGAGATCCTTCATCA, AACCGAGAACACAGAATAAATATC, AACCGAGAACACGACCACTGCTCT, AACCGAGAACAGATTCCTTCTAAC, AACCGAGAAGATGTACTTACCTGC, AACCGAGAAGTACAAGACTCCTGC, AACCGAGAATCATTCCCATTTACA, AACCGAGAATCATTCCCCCTTATA, AACCGAGAATCCTGTAATCCTTAC, AACCGAGACATCAAGTGCCTATCT, AACCGAGACGCATACAACTGCTCT, AACCGAGACGCATACACATTTACA, AACCGAGAGAATCTGAACTGCTCT, AACCGAGAGAATCTGACTGACTTC, AACCGAGAGAGCTGAACTTCTAAC, AACCGAGAGCCACATACTGACTTC, AACCGAGAGCGAGTAAACTGCTCT, AACCGAGAGCTAACGACTATTTCA, AACCGAGATTCACGCACTTCATCA, AACGCTTAAAGACGGACCAATTCT, AACGCTTAAAGAGATCCCAATTCT, AACGCTTAACATTGGCATGCTGCT, AACGCTTAACATTGGCCATTTACA, AACGCTTAACCTCCAAGTTACGTA, AACGCTTACATACCAAACTCCTGC, AACGCTTACATACCAACTATTTCA, AACGCTTACATACCAACTTCATCA, AACGCTTACCGACAACACTGCTCT, AACGCTTAGACAGTGCTTACCTGC, AACGCTTAGAGCTGAAATGCTGCT, AACGCTTAGAGCTGAACATTTACA, AACGCTTAGCTAACGAGCTCTACT, AACGCTTAGTACGCAACTCTAATC, AACGCTTAGTACGCAATCTAGGCT, AACGCTTAGTACGCAATTACCTGC, AACGCTTATGAAGAGAATTTGGCA, AACGCTTATGAAGAGATGCTTGGG, AACGCTTATGGCTTCACCAATTCT, AACGTGATAACGCTTAACCCTTGC, AACGTGATAACGCTTATCATGCTC, AACGTGATAGATGTACATTTGGCA, AACGTGATAGCAGGAATCATTGCA, AACGTGATAGCCATGCATTTGGCA, AACGTGATATCCTGTATCATTGCA, AACGTGATCATCAAGTCCAATTCT, AACGTGATCCAGTTCACCTTTGCA, AACGTGATCCAGTTCACTATTTCA, AACGTGATCCGAAGTAGTTACGTA, AACGTGATCGACTGGACTGACTTC, AACGTGATCGCTGATCCATTTACA, AACGTGATCTAAGGTCACTCGTAA, AACGTGATCTAAGGTCCCAATTCT, AACGTGATGACTAGTAACTTAGCT, AACGTGATGACTAGTAGTTACGTA, AACGTGATGGTGCGAACCAATTCT, AACGTGATGTACGCAACTTCATCA, AACGTGATGTCTGTCACCTATATC, AACGTGATTATCAGCAACTTTACC, AACGTGATTGGCTTCACCCTTATA, AACGTGATTGGTGGTACTATTTCA, AACTCACCAACAACCAATCCTTAC, ...]\n",
              "\n",
              "[1755 rows x 0 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Iadata.obs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "758b48ed-71f8-485d-af95-38700844e07d",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "758b48ed-71f8-485d-af95-38700844e07d",
        "outputId": "a8bbee5a-a97b-456f-c6ef-ee9a3724cbf8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"adata\",\n  \"rows\": 1281,\n  \"fields\": []\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-19d52857-bf0c-4ef7-8a59-d09dd61f3f8e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AAACATCGACGTATCAACATTTAC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AAACATCGACGTATCAGCTTCATA</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AAACATCGAGTCACTAACTGCTCT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AAACATCGATCCTGTACACCTTTA</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AAACATCGCCTCCTGACTCTGTGC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TTCACGCACACTTCGACTATCATC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TTCACGCACGAACTTAACTGCTCT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TTCACGCACGCTGATCCTATCATC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TTCACGCACTCAATGAATGCTGCT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TTCACGCAGAATCTGATCATTGCA</th>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1281 rows × 0 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-19d52857-bf0c-4ef7-8a59-d09dd61f3f8e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-19d52857-bf0c-4ef7-8a59-d09dd61f3f8e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-19d52857-bf0c-4ef7-8a59-d09dd61f3f8e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e4fdd853-81f3-4cdd-836c-bb916d56a066\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e4fdd853-81f3-4cdd-836c-bb916d56a066')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e4fdd853-81f3-4cdd-836c-bb916d56a066 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: []\n",
              "Index: [AAACATCGACGTATCAACATTTAC, AAACATCGACGTATCAGCTTCATA, AAACATCGAGTCACTAACTGCTCT, AAACATCGATCCTGTACACCTTTA, AAACATCGCCTCCTGACTCTGTGC, AAACATCGGAATCTGAAATTTCTC, AAACATCGTGGAACAACTGACTTC, AACAACCAAACAACCAACATTTAC, AACAACCAAACAACCAACTTAGCT, AACAACCAACGCTCGAATTTGGCA, AACAACCAAGCCATGCATGCTGCT, AACAACCAAGTACAAGCTCTGTGC, AACAACCAAGTGGTCAATGCTGCT, AACAACCACAAGACTAATGCTGCT, AACAACCACCTCTATCACATTTAC, AACAACCACCTCTATCGCTTCATA, AACAACCAGAACAGGCCTCTGTGC, AACAACCAGCTAACGAATTCATGG, AACAACCATCTTCACACTCTGTGC, AACAACCATGGTGGTAACTGCTCT, AACCGAGAAAGAGATCCTTCATCA, AACCGAGAACGTATCAATGCTGCT, AACCGAGAAGTACAAGACTCCTGC, AACCGAGAAGTACAAGCTGACTTC, AACCGAGAATCATTCCATGCTGCT, AACCGAGAATCCTGTAACTGCTCT, AACCGAGACATCAAGTAATTTCTC, AACCGAGACGCATACAATGCTGCT, AACCGAGACGCATACACCTATATC, AACCGAGAGCCACATACTGACTTC, AACGCTTAACAAGCTACATACTTC, AACGCTTAACAGATTCACCCTTGC, AACGCTTAACATTGGCATGCTGCT, AACGCTTACATACCAAACTCCTGC, AACGCTTACATACCAACTGACTTC, AACGCTTAGAGCTGAAATGCTGCT, AACGCTTAGCTAACGAGCTCTACT, AACGCTTAGTACGCAACTCTAATC, AACGCTTAGTCTGTCAGCTCTACT, AACGCTTATGAAGAGAATTTGGCA, AACGCTTATGGCTTCACCAATTCT, AACGTGATAACGCTTAACCCTTGC, AACGTGATAACGCTTACACCTTTA, AACGTGATAACGCTTATCATGCTC, AACGTGATAACGTGATACTGCTCT, AACGTGATAAGGACACACCCTTGC, AACGTGATACACAGAATTCGCTAC, AACGTGATCCAGTTCACCTTTGCA, AACGTGATCCATCCTCCCGTTCTA, AACGTGATCGACTGGACTGACTTC, AACGTGATCGCTGATCATGCTGCT, AACGTGATGAATCTGAAATTTCTC, AACGTGATGACTAGTAACTTAGCT, AACGTGATGTCTGTCACCTATATC, AACTCACCAACAACCAACTGCTCT, AACTCACCAACAACCAATCCTTAC, AACTCACCAAGAGATCATGCTGCT, AACTCACCAAGGACACTCATTGCA, AACTCACCAGAGTCAATCATGCTC, AACTCACCCATACCAAACTGCTCT, AACTCACCCATCAAGTAATTTCTC, AACTCACCCATCAAGTGCCTATCT, AACTCACCCCTAATCCCCGTTCTA, AACTCACCCTGGCATACACTTTCA, AACTCACCGTGTTCTACTCTGTGC, AAGACGGAAACGCTTACTGACTTC, AAGACGGAACATTGGCCTCTGTGC, AAGACGGAACTATGCAAATTTCTC, AAGACGGACACTTCGAGCTCTACT, AAGACGGACAGATCTGCTGACTTC, AAGACGGACAGCGTTATCTCATGC, AAGACGGACTAAGGTCACTGCTCT, AAGACGGAGAATCTGAATGCTGCT, AAGACGGAGCCACATACTCTAATC, AAGAGATCAAACATCGCTCTGTGC, AAGAGATCAAGAGATCCTCTGTGC, AAGAGATCACAGATTCTCATTGCA, AAGAGATCAGTCACTAGCTTCATA, AAGAGATCCACTTCGAATTTGGCA, AAGAGATCCAGCGTTAAATTTCTC, AAGAGATCCTGGCATAATGCTGCT, AAGAGATCGCCACATACTGACTTC, AAGAGATCTCCGTCTACCGTTCTA, AAGAGATCTCTTCACAACTTTACC, AAGAGATCTCTTCACACTATCATC, AAGAGATCTGAAGAGACATGTCTC, AAGAGATCTGAAGAGACTATCATC, AAGAGATCTTCACGCACCTTTGCA, AAGGACACAAGGACACCTGACTTC, AAGGACACACACAGAACATTTGGA, AAGGACACACCACTGTACTGCTCT, AAGGACACACGCTCGAACTCCTGC, AAGGACACACGCTCGACTGACTTC, AAGGACACATCCTGTAACTTAGCT, AAGGACACATCCTGTAATGCTGCT, AAGGACACCAATGGAACTGACTTC, AAGGACACGACTAGTACTGACTTC, AAGGACACTATCAGCAATCTTAGG, AAGGTACAAGAGTCAAATGCTGCT, AAGGTACAATCATTCCCTATCATC, ...]\n",
              "\n",
              "[1281 rows x 0 columns]"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "adata.obs"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Nq3AHuPYpsCG",
      "metadata": {
        "id": "Nq3AHuPYpsCG"
      },
      "source": [
        "We now calculate simple QC metrics and plot the overlap of the barcodes between the polyT and the merged barcodes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "g5Z0kp7xsxns",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "g5Z0kp7xsxns"
      },
      "outputs": [],
      "source": [
        "sc.pp.calculate_qc_metrics(adata, inplace=True)\n",
        "sc.pp.calculate_qc_metrics(Iadata, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Ij6AmtrXtEHl",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Ij6AmtrXtEHl"
      },
      "outputs": [],
      "source": [
        "sc.pp.normalize_per_cell(adata, counts_per_cell_after=1e4)\n",
        "sc.pp.log1p(adata)\n",
        "\n",
        "sc.pp.normalize_per_cell(Iadata, counts_per_cell_after=1e4)\n",
        "sc.pp.log1p(Iadata)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ad18d2d8-7a35-4ae4-9f93-d99ecff0cd18",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ad18d2d8-7a35-4ae4-9f93-d99ecff0cd18",
        "outputId": "b451462b-6ca9-4524-d055-0c377d7d5171"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib_venn._common.VennDiagram at 0x781bed530d90>"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 377,
              "width": 515
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#!pip install matplotlib-venn\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib_venn import venn2\n",
        "\n",
        "aadata = [t for t in adata.obs.index]\n",
        "iadata = [t for t in Iadata.obs.index]\n",
        "\n",
        "venn2((set(aadata), set(iadata)), ('polyT', 'merged'))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "iYdgGC9Rp510",
      "metadata": {
        "id": "iYdgGC9Rp510"
      },
      "source": [
        "Finally, we plot the Spearman gene-level correlations between the matched nuclei in"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "vBt__LPztVw_",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "vBt__LPztVw_"
      },
      "outputs": [],
      "source": [
        "intersect_g = [gene for gene in adata.var.index if gene in Iadata.var.index]\n",
        "intersectsc = [t for t in adata.obs.index if t in Iadata.obs.index]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "iQfgC-XEsSiD",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "iQfgC-XEsSiD",
        "outputId": "5523aefc-9aeb-457f-d19e-1c8aa40a7df4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 444,
              "width": 579
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "adata[intersectsc, intersect_g]\n",
        "spearman_gene_ONT_vs_Ill = []\n",
        "for c in intersectsc:\n",
        "    if np.squeeze(np.asarray(adata[c, intersect_g].X.todense())).all() != 1:\n",
        "        spearman=scipy.stats.spearmanr(np.squeeze(np.asarray(adata[c, intersect_g].X.todense())), np.squeeze(np.asarray(Iadata[c, intersect_g].X.todense())))\n",
        "        spearman_gene_ONT_vs_Ill.append(spearman[0])\n",
        "\n",
        "#plt.scatter(adata.obs['n_counts'], spearman_gene_ONT_vs_Ill)\n",
        "plt.hexbin(adata[intersectsc, intersect_g].obs['n_counts'], spearman_gene_ONT_vs_Ill, gridsize=50, cmap='jet', bins='log')\n",
        "plt.ylabel(\"Spearman genelevel sn expression polyT vs merged\")\n",
        "plt.xlabel(\"Number of merged UMI/nucleus\")\n",
        "plt.ylim((0,1))\n",
        "plt.colorbar(label='counts')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "YTqI_hBjqayI",
      "metadata": {
        "id": "YTqI_hBjqayI"
      },
      "source": [
        "Finally, we show how to install lr-kallisto from source"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "NRe7601zsFSk",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "NRe7601zsFSk",
        "outputId": "67df485d-66ab-433f-9bc0-eabf55804ecb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'kallisto'...\n",
            "remote: Enumerating objects: 8710, done.\u001b[K\n",
            "remote: Counting objects: 100% (1792/1792), done.\u001b[K\n",
            "remote: Compressing objects: 100% (524/524), done.\u001b[K\n",
            "remote: Total 8710 (delta 1337), reused 1578 (delta 1266), pack-reused 6918 (from 1)\u001b[K\n",
            "Receiving objects: 100% (8710/8710), 9.37 MiB | 12.13 MiB/s, done.\n",
            "Resolving deltas: 100% (5763/5763), done.\n",
            "astyle.txt\text\t    gen_release.sh  INSTALL.md\t README.md  test\n",
            "CMakeLists.txt\tfunc_tests  gulpfile.js     license.txt  src\t    unit_tests\n",
            "astyle.txt  CMakeLists.txt  func_tests\t    gulpfile.js  license.txt  src   unit_tests\n",
            "build\t    ext\t\t    gen_release.sh  INSTALL.md\t README.md    test\n",
            "bcf_sr_sort.c  hfile_gcs.c\t htslib.pc.in\t plugin.c\t      tbx.c\n",
            "bcf_sr_sort.h  hfile_internal.h  htslib_vars.mk  probaln.c\t      test\n",
            "bgzf.c\t       hfile_libcurl.c\t INSTALL\t README\t\t      textutils.c\n",
            "bgzip.c        hfile_net.c\t kfunc.c\t README.md\t      thread_pool.c\n",
            "config.mk.in   hfile_s3.c\t knetfile.c\t realn.c\t      thread_pool_internal.h\n",
            "configure.ac   hts.c\t\t kstring.c\t regidx.c\t      vcf.5\n",
            "cram\t       htsfile.1\t LICENSE\t sam.5\t\t      vcf.c\n",
            "errmod.c       htsfile.c\t Makefile\t sam.c\t\t      vcf_sweep.c\n",
            "faidx.5        hts_internal.h\t md5.c\t\t synced_bcf_reader.c  vcfutils.c\n",
            "faidx.c        htslib\t\t multipart.c\t tabix.1\n",
            "hfile.c        htslib.mk\t NEWS\t\t tabix.c\n",
            "\u001b[0mCMake Error: Unknown argument -\u001b[0m\n",
            "\u001b[0mCMake Error: Run 'cmake --help' for all supported options.\u001b[0m\n",
            "make: *** No targets specified and no makefile found.  Stop.\n",
            "CPU times: user 73.1 ms, sys: 12.2 ms, total: 85.3 ms\n",
            "Wall time: 4.37 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "!git clone https://github.com/pachterlab/kallisto.git\n",
        "!ls kallisto\n",
        "!mkdir kallisto/build\n",
        "!ls kallisto\n",
        "!ls kallisto/ext/htslib/\n",
        "!cd kallisto/ext/htslib; autoheader; autoconf\n",
        "!cd kallisto/build; cmake .. -DMAX_KMER_SIZE=64 -; make"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.8"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}