diff --git a/docs/notebooks/example.ipynb b/docs/notebooks/example.ipynb index 5f0898e..c505f6b 100644 --- a/docs/notebooks/example.ipynb +++ b/docs/notebooks/example.ipynb @@ -4,18 +4,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Example FlowSOM Pipeline" + "# Example FlowSOM Pipeline\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This vignette describes a protocol for analyzing high-dimensional cytometry \n", - "data using FlowSOM, a clustering and visualization algorithm based \n", + "This vignette describes a protocol for analyzing high-dimensional cytometry\n", + "data using FlowSOM, a clustering and visualization algorithm based\n", "on a self-organizing map (SOM). FlowSOM is used to distinguish cell populations\n", "from cytometry data in an unsupervised way and can help to gain deeper insights\n", - "in fields such as immunology and oncology. " + "in fields such as immunology and oncology.\n" ] }, { @@ -23,15 +23,25 @@ "metadata": {}, "source": [ "## Loading in the data\n", + "\n", "FlowSOM handles different inputs, such as an anndata object by pytometry or a filepath. For this purpose we will make use of an anndata object. This allows\n", - "easier preprocessing. " + "easier preprocessing.\n" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "❗ You are running 3.12.2\n", + "Only python versions 3.7~3.10 are currently tested, use at your own risk.\n" + ] + } + ], "source": [ "# Import modules\n", "import flowsom as fs\n", @@ -44,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -55,7 +65,7 @@ " uns: 'meta'" ] }, - "execution_count": 37, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -70,12 +80,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can get an overview of the most important data in our anndata object by using `var`. All the metadata is stored in a dictionary at `ff.uns[\"meta]`" + "We can get an overview of the most important data in our anndata object by using `var`. All the metadata is stored in a dictionary at `ff.uns[\"meta]`\n" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -334,7 +344,7 @@ "CD3 18 PE-Cy7-A CD3 32 0,0 1.0 262144 588" ] }, - "execution_count": 38, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -345,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -354,7 +364,7 @@ "dict_keys(['__header__', '$BEGINANALYSIS', '$BEGINDATA', '$BEGINSTEXT', '$BTIM', '$BYTEORD', '$DATATYPE', '$DATE', '$ENDANALYSIS', '$ENDDATA', '$ENDSTEXT', '$ETIM', '$FIL', '$INST', '$MODE', '$NEXTDATA', '$PAR', '$SRC', '$SYS', '$TIMESTEP', '$TOT', 'APPLY COMPENSATION', 'AUTOBS', 'CREATOR', 'EXPERIMENT NAME', 'EXPORT TIME', 'EXPORT USER NAME', 'FCSversion', 'FILENAME', 'flowCore_$P10Rmax', 'flowCore_$P10Rmin', 'flowCore_$P11Rmax', 'flowCore_$P11Rmin', 'flowCore_$P12Rmax', 'flowCore_$P12Rmin', 'flowCore_$P13Rmax', 'flowCore_$P13Rmin', 'flowCore_$P14Rmax', 'flowCore_$P14Rmin', 'flowCore_$P15Rmax', 'flowCore_$P15Rmin', 'flowCore_$P16Rmax', 'flowCore_$P16Rmin', 'flowCore_$P17Rmax', 'flowCore_$P17Rmin', 'flowCore_$P18Rmax', 'flowCore_$P18Rmin', 'flowCore_$P1Rmax', 'flowCore_$P1Rmin', 'flowCore_$P2Rmax', 'flowCore_$P2Rmin', 'flowCore_$P3Rmax', 'flowCore_$P3Rmin', 'flowCore_$P4Rmax', 'flowCore_$P4Rmin', 'flowCore_$P5Rmax', 'flowCore_$P5Rmin', 'flowCore_$P6Rmax', 'flowCore_$P6Rmin', 'flowCore_$P7Rmax', 'flowCore_$P7Rmin', 'flowCore_$P8Rmax', 'flowCore_$P8Rmin', 'flowCore_$P9Rmax', 'flowCore_$P9Rmin', 'FSC ASF', 'GUID', 'ORIGINALGUID', 'P10BS', 'P10DISPLAY', 'P10MS', 'P11BS', 'P11DISPLAY', 'P11MS', 'P12BS', 'P12DISPLAY', 'P12MS', 'P13BS', 'P13DISPLAY', 'P13MS', 'P14BS', 'P14DISPLAY', 'P14MS', 'P15BS', 'P15DISPLAY', 'P15MS', 'P16BS', 'P16DISPLAY', 'P16MS', 'P17BS', 'P17DISPLAY', 'P17MS', 'P18BS', 'P18DISPLAY', 'P18MS', 'P1BS', 'P1MS', 'P2BS', 'P2DISPLAY', 'P2MS', 'P3BS', 'P3DISPLAY', 'P3MS', 'P4BS', 'P4MS', 'P5BS', 'P5DISPLAY', 'P5MS', 'P6BS', 'P6DISPLAY', 'P6MS', 'P7BS', 'P7MS', 'P8BS', 'P8DISPLAY', 'P8MS', 'P9BS', 'P9DISPLAY', 'P9MS', 'THRESHOLD', 'transformation', 'TUBE NAME', 'WINDOW EXTENSION', 'channels', 'header', 'spill'])" ] }, - "execution_count": 39, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -367,12 +377,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Additionaly we can read in a csv file as well." + "Additionaly we can read in a csv file as well.\n" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -382,7 +392,7 @@ " var: 'n', 'channel', 'marker'" ] }, - "execution_count": 40, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -396,17 +406,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `FlowSOM` function accepts an anndata object or a filepath to a fcs or csv file. The `FlowSOM` function will return a `FlowSOM` mudata object. This object contains all the information about the SOM and the clustering." + "The `FlowSOM` function accepts an anndata object or a filepath to a fcs or csv file. The `FlowSOM` function will return a `FlowSOM` mudata object. This object contains all the information about the SOM and the clustering.\n" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -425,16 +435,17 @@ "metadata": {}, "source": [ "## Preprocessing\n", - "We start with compensating the data and then we transform \n", - "the data with the logicle function. For CyTOF data an arcsinh transformation is \n", - "preferred which is also found in the pytometry package. Besides \n", + "\n", + "We start with compensating the data and then we transform\n", + "the data with the logicle function. For CyTOF data an arcsinh transformation is\n", + "preferred which is also found in the pytometry package. Besides\n", "compensation and transformation, we also recommend cleaning the data by removing\n", - "margin events and by using cleaning algorithms." + "margin events and by using cleaning algorithms.\n" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -448,12 +459,12 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -465,6 +476,8 @@ "source": [ "# Visualize data\n", "ax = plt.hist2d(ff_t[:, \"CD4\"].X.flatten(), ff_t[:, \"CD8\"].X.flatten(), bins=200, cmin=1, cmap=\"jet\")\n", + "plt.xlabel(\"CD4\")\n", + "plt.ylabel(\"CD8\")\n", "plt.show()" ] }, @@ -473,110 +486,627 @@ "metadata": {}, "source": [ "# FlowSOM\n", - "The easiest way to use this package is using the wrapper function \n", - "FlowSOM. It has less options than using the separate functions, \n", + "\n", + "The easiest way to use this package is using the wrapper function\n", + "FlowSOM. It has less options than using the separate functions,\n", "but in general it has enough power. It returns a mudata object, of which the first\n", "modality is the cell data and the second modality is the cluster data. We will\n", - "cluster the data with a 10 x 10 SOM grid and 10 metaclusters. Notice that due to the \n", - "just-in-time compilation of numba, the first run of FlowSOM can take a while and \n", - "the subsequent runs will be much faster." + "cluster the data with a 10 x 10 SOM grid and 10 metaclusters. Notice that due to the\n", + "just-in-time compilation of numba, the first run of FlowSOM can take a while and\n", + "the subsequent runs will be much faster.\n" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-03-26 23:15:22.630\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mflowsom.main\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m75\u001b[0m - \u001b[34m\u001b[1mReading input.\u001b[0m\n", + "\u001b[32m2024-03-26 23:15:22.632\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mflowsom.main\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m77\u001b[0m - \u001b[34m\u001b[1mFitting model: clustering and metaclustering.\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-03-26 23:15:23.910\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mflowsom.main\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m79\u001b[0m - \u001b[34m\u001b[1mUpdating derived values.\u001b[0m\n" + ] + } + ], + "source": [ + "fsom = fs.FlowSOM(ff_t.copy(), cols_to_use=cols_to_use, n_clusters=10, xdim=10, ydim=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can inspect the underlying model and can see it's like any other scikit-learn model. The FlowSOM estimator first overclusters using a `cluster_model` (Self-Organizing Map). Then it uses a `metacluster_model` (Consensus Agglomerative Clustering) to merge the clusters into metaclusters." + ] + }, + { + "cell_type": "code", + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
MuData object with n_obs × n_vars = 19225 × 18\n",
-       "  2 modalities\n",
-       "    cell_data:\t19225 x 18\n",
-       "      obs:\t'clustering', 'distance_to_bmu', 'metaclustering'\n",
-       "      var:\t'pretty_colnames', 'markers', 'channels', 'cols_used'\n",
-       "      uns:\t'n_nodes', 'n_metaclusters'\n",
-       "    cluster_data:\t100 x 18\n",
-       "      obs:\t'percentages', 'metaclustering'\n",
-       "      uns:\t'xdim', 'ydim', 'outliers', 'graph', 'metacluster_MFIs'\n",
-       "      obsm:\t'cv_values', 'sd_values', 'mad_values', 'codes', 'grid', 'layout'
" + "
FlowSOMEstimator(cluster_kwargs={'alpha': (0.05, 0.01), 'mst': 1, 'rlen': 10,\n",
+       "                                 'xdim': 10, 'ydim': 10},\n",
+       "                 cluster_model=SOMEstimator(codes=array([[0.17293864, 0.38055497, 0.15488249, 0.27882084, 0.18838793,\n",
+       "        0.77889943, 0.25986448],\n",
+       "       [0.16635749, 0.2384101 , 0.15171953, 0.29685625, 0.16434987,\n",
+       "        0.7477991 , 0.18665981],\n",
+       "       [0.15941879, 0.19481242, 0.15016532, 0.172723  , 0.20582703,\n",
+       "        0.722337...\n",
+       "        0.22405078, 0.6581784 ],\n",
+       "       [0.49650916, 0.19511601, 0.46217316, 0.4585156 , 0.26292098,\n",
+       "        0.21728244, 0.5464857 ],\n",
+       "       [0.683092  , 0.17024131, 0.5083324 , 0.20047309, 0.18183374,\n",
+       "        0.2175082 , 0.59437656],\n",
+       "       [0.68553203, 0.17374894, 0.50928295, 0.33964133, 0.16082321,\n",
+       "        0.21649924, 0.59890723]], dtype=float32)),\n",
+       "                 metacluster_kwargs={'n_clusters': 10},\n",
+       "                 metacluster_model=ConsensusCluster(K=10, n_clusters=10))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ - "MuData object with n_obs × n_vars = 19225 × 18\n", - " 2 modalities\n", - " cell_data:\t19225 x 18\n", - " obs:\t'clustering', 'distance_to_bmu', 'metaclustering'\n", - " var:\t'pretty_colnames', 'markers', 'channels', 'cols_used'\n", - " uns:\t'n_nodes', 'n_metaclusters'\n", - " cluster_data:\t100 x 18\n", - " obs:\t'percentages', 'metaclustering'\n", - " uns:\t'xdim', 'ydim', 'outliers', 'graph', 'metacluster_MFIs'\n", - " obsm:\t'cv_values', 'sd_values', 'mad_values', 'codes', 'grid', 'layout'" + "FlowSOMEstimator(cluster_kwargs={'alpha': (0.05, 0.01), 'mst': 1, 'rlen': 10,\n", + " 'xdim': 10, 'ydim': 10},\n", + " cluster_model=SOMEstimator(codes=array([[0.17293864, 0.38055497, 0.15488249, 0.27882084, 0.18838793,\n", + " 0.77889943, 0.25986448],\n", + " [0.16635749, 0.2384101 , 0.15171953, 0.29685625, 0.16434987,\n", + " 0.7477991 , 0.18665981],\n", + " [0.15941879, 0.19481242, 0.15016532, 0.172723 , 0.20582703,\n", + " 0.722337...\n", + " 0.22405078, 0.6581784 ],\n", + " [0.49650916, 0.19511601, 0.46217316, 0.4585156 , 0.26292098,\n", + " 0.21728244, 0.5464857 ],\n", + " [0.683092 , 0.17024131, 0.5083324 , 0.20047309, 0.18183374,\n", + " 0.2175082 , 0.59437656],\n", + " [0.68553203, 0.17374894, 0.50928295, 0.33964133, 0.16082321,\n", + " 0.21649924, 0.59890723]], dtype=float32)),\n", + " metacluster_kwargs={'n_clusters': 10},\n", + " metacluster_model=ConsensusCluster(K=10, n_clusters=10))" ] }, - "execution_count": 44, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fsom = fs.FlowSOM(ff_t, cols_to_use, xdim=10, ydim=10, n_clus=10)\n", - "fsom.mudata" + "fsom.model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "To add the a FlowSOM clustering and metaclustering to an anndata object, we use\n", - "flowsom_clustering, similar to other clustering methods in scverse. The FlowSOM\n", - "clustering and metaclustering can be found in obs and the parameters used in the\n", - "FlowSOM clustering in uns.FlowSOM" + "The output is stored in a MuData object, containing two AnnData object: `cell_data` (n_cells x n_features) and `cluster_data` (n_SOM_nodes x n_features)." ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
MuData object with n_obs × n_vars = 0 × 0\n",
+       "  2 modalities\n",
+       "    cell_data:\t19225 x 18\n",
+       "      obs:\t'clustering', 'distance_to_bmu', 'metaclustering'\n",
+       "      var:\t'n', 'channel', 'marker', '$PnB', '$PnE', '$PnG', '$PnR', '$PnV', 'pretty_colnames', 'markers', 'channels', 'cols_used'\n",
+       "      uns:\t'meta', 'n_nodes', 'n_metaclusters'\n",
+       "      layers:\t'original'\n",
+       "    cluster_data:\t100 x 18\n",
+       "      obs:\t'percentages', 'metaclustering'\n",
+       "      uns:\t'xdim', 'ydim', 'outliers', 'metacluster_MFIs', 'graph'\n",
+       "      obsm:\t'cv_values', 'sd_values', 'mad_values', 'codes', 'grid', 'layout'
" + ], "text/plain": [ - "AnnData object with n_obs × n_vars = 19225 × 18\n", - " obs: 'FlowSOM_clusters', 'FlowSOM_metaclusters'\n", - " var: 'n', 'channel', 'marker', '$PnB', '$PnE', '$PnG', '$PnR', '$PnV'\n", - " uns: 'meta', 'FlowSOM'\n", - " layers: 'original'" + "MuData object with n_obs × n_vars = 0 × 0\n", + " 2 modalities\n", + " cell_data:\t19225 x 18\n", + " obs:\t'clustering', 'distance_to_bmu', 'metaclustering'\n", + " var:\t'n', 'channel', 'marker', '$PnB', '$PnE', '$PnG', '$PnR', '$PnV', 'pretty_colnames', 'markers', 'channels', 'cols_used'\n", + " uns:\t'meta', 'n_nodes', 'n_metaclusters'\n", + " layers:\t'original'\n", + " cluster_data:\t100 x 18\n", + " obs:\t'percentages', 'metaclustering'\n", + " uns:\t'xdim', 'ydim', 'outliers', 'metacluster_MFIs', 'graph'\n", + " obsm:\t'cv_values', 'sd_values', 'mad_values', 'codes', 'grid', 'layout'" ] }, - "execution_count": 45, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ff_clustered = fs.flowsom_clustering(ff_t, cols_to_use, xdim=10, ydim=10, n_clus=10)\n", - "ff_clustered" + "fsom.mudata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can access the cell data and the cluster data with the `get_cell_data()` and \n", - "`get_cluster_data()` functions. \n", + "We can access the cell data and the cluster data with the `get_cell_data()` and\n", + "`get_cluster_data()` functions.\n", "\n", - "The *cell data* is an anndata object that contains \n", - "the original cell data. As observations, we find the clustering, metaclustering \n", + "The _cell data_ is an anndata object that contains\n", + "the original cell data. As observations, we find the clustering, metaclustering\n", "and distance to best matching unit per cell. In var, we find the pretty colnames,\n", - "i.e. a combination of markers and channels, the markers, the channels and a \n", + "i.e. a combination of markers and channels, the markers, the channels and a\n", "boolean mask of the columns used for clustering. n_nodes and n_metaclusters in\n", - "uns contain the number of clusters and metaclusters respectively." + "uns contain the number of clusters and metaclusters respectively.\n" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -584,11 +1114,12 @@ "text/plain": [ "AnnData object with n_obs × n_vars = 19225 × 18\n", " obs: 'clustering', 'distance_to_bmu', 'metaclustering'\n", - " var: 'pretty_colnames', 'markers', 'channels', 'cols_used'\n", - " uns: 'n_nodes', 'n_metaclusters'" + " var: 'n', 'channel', 'marker', '$PnB', '$PnE', '$PnG', '$PnR', '$PnV', 'pretty_colnames', 'markers', 'channels', 'cols_used'\n", + " uns: 'meta', 'n_nodes', 'n_metaclusters'\n", + " layers: 'original'" ] }, - "execution_count": 46, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -601,15 +1132,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The *cluster data* contains the original median values per cluster per marker. \n", - "In obsm, we find the cv values, sd values, mad values, coordinates of the nodes, \n", - "coordinates of the the grid and the coordinates of the MST layout. \n", - "The xdim, ydim, outliers, igraph object and metacluster MFIs can be found in uns." + "The _cluster data_ contains the original median values per cluster per marker.\n", + "In obsm, we find the cv values, sd values, mad values, coordinates of the nodes,\n", + "coordinates of the the grid and the coordinates of the MST layout.\n", + "The xdim, ydim, outliers, igraph object and metacluster MFIs can be found in uns.\n" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -617,11 +1148,11 @@ "text/plain": [ "AnnData object with n_obs × n_vars = 100 × 18\n", " obs: 'percentages', 'metaclustering'\n", - " uns: 'xdim', 'ydim', 'outliers', 'graph', 'metacluster_MFIs'\n", + " uns: 'xdim', 'ydim', 'outliers', 'metacluster_MFIs', 'graph'\n", " obsm: 'cv_values', 'sd_values', 'mad_values', 'codes', 'grid', 'layout'" ] }, - "execution_count": 47, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -630,73 +1161,488 @@ "fsom.get_cluster_data()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## FlowSOM visualizations\n", - "A FlowSOM object can be visualized with the `plot_stars()` function" - ] - }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nchannelmarker$PnB$PnE$PnG$PnR$PnVpretty_colnamesmarkerschannelscols_used
Time1Time320,00.01262144Time <Time>TimeTimeFalse
FSC-A2FSC-A320,01.0262144280FSC-A <FSC-A>FSC-AFSC-AFalse
FSC-H3FSC-H320,01.0262144280FSC-H <FSC-H>FSC-HFSC-HFalse
FSC-W4FSC-W320,01.0262144280FSC-W <FSC-W>FSC-WFSC-WFalse
SSC-A5SSC-A320,01.0262144280SSC-A <SSC-A>SSC-ASSC-AFalse
SSC-H6SSC-H320,01.0262144280SSC-H <SSC-H>SSC-HSSC-HFalse
SSC-W7SSC-W320,01.0262144280SSC-W <SSC-W>SSC-WSSC-WFalse
FITC-A8FITC-AGFP320,01.0262144412GFP <FITC-A>GFPFITC-AFalse
Pacific Blue-A9Pacific Blue-ACD8320,01.0262144417CD8 <Pacific Blue-A>CD8Pacific Blue-ATrue
AmCyan-A10AmCyan-Al/d320,01.0262144496l/d <AmCyan-A>l/dAmCyan-AFalse
Qdot 605-A11Qdot 605-A320,01.0262144588Qdot 605-A <Qdot 605-A>Qdot 605-AQdot 605-AFalse
APC-A12APC-ATCRyd320,01.0262144597TCRyd <APC-A>TCRydAPC-ATrue
Alexa Fluor 700-A13Alexa Fluor 700-ACD45320,01.0262144492CD45 <Alexa Fluor 700-A>CD45Alexa Fluor 700-AFalse
APC-Cy7-A14APC-Cy7-ATCRb320,01.0262144511TCRb <APC-Cy7-A>TCRbAPC-Cy7-ATrue
PE-A15PE-ANK1/1320,01.0262144505NK1/1 <PE-A>NK1/1PE-ATrue
PE-Texas Red-A16PE-Texas Red-ACD4320,01.0262144560CD4 <PE-Texas Red-A>CD4PE-Texas Red-ATrue
PE-Cy5-A17PE-Cy5-ACD19320,01.0262144593CD19 <PE-Cy5-A>CD19PE-Cy5-ATrue
PE-Cy7-A18PE-Cy7-ACD3320,01.0262144588CD3 <PE-Cy7-A>CD3PE-Cy7-ATrue
\n", + "
" + ], "text/plain": [ - "
" + " n channel marker $PnB $PnE $PnG $PnR $PnV \\\n", + "Time 1 Time 32 0,0 0.01 262144 \n", + "FSC-A 2 FSC-A 32 0,0 1.0 262144 280 \n", + "FSC-H 3 FSC-H 32 0,0 1.0 262144 280 \n", + "FSC-W 4 FSC-W 32 0,0 1.0 262144 280 \n", + "SSC-A 5 SSC-A 32 0,0 1.0 262144 280 \n", + "SSC-H 6 SSC-H 32 0,0 1.0 262144 280 \n", + "SSC-W 7 SSC-W 32 0,0 1.0 262144 280 \n", + "FITC-A 8 FITC-A GFP 32 0,0 1.0 262144 412 \n", + "Pacific Blue-A 9 Pacific Blue-A CD8 32 0,0 1.0 262144 417 \n", + "AmCyan-A 10 AmCyan-A l/d 32 0,0 1.0 262144 496 \n", + "Qdot 605-A 11 Qdot 605-A 32 0,0 1.0 262144 588 \n", + "APC-A 12 APC-A TCRyd 32 0,0 1.0 262144 597 \n", + "Alexa Fluor 700-A 13 Alexa Fluor 700-A CD45 32 0,0 1.0 262144 492 \n", + "APC-Cy7-A 14 APC-Cy7-A TCRb 32 0,0 1.0 262144 511 \n", + "PE-A 15 PE-A NK1/1 32 0,0 1.0 262144 505 \n", + "PE-Texas Red-A 16 PE-Texas Red-A CD4 32 0,0 1.0 262144 560 \n", + "PE-Cy5-A 17 PE-Cy5-A CD19 32 0,0 1.0 262144 593 \n", + "PE-Cy7-A 18 PE-Cy7-A CD3 32 0,0 1.0 262144 588 \n", + "\n", + " pretty_colnames markers channels \\\n", + "Time Time