From 1da1fb95e7c8d9852ec530f855ca02b3db050369 Mon Sep 17 00:00:00 2001 From: Antonio Camargo Date: Sun, 2 Jun 2019 14:00:49 -0300 Subject: [PATCH 1/6] Make TSI an individualized scoring metric --- tspex/core/specificity_class.py | 10 ++--- tspex/core/specificity_functions.py | 60 ++++++++++++++--------------- tutorial.ipynb | 4 +- 3 files changed, 37 insertions(+), 37 deletions(-) diff --git a/tspex/core/specificity_class.py b/tspex/core/specificity_class.py index c9376e1..98696d9 100644 --- a/tspex/core/specificity_class.py +++ b/tspex/core/specificity_class.py @@ -46,9 +46,9 @@ class TissueSpecificity: corresponding to genes and columns to tissues/conditions. method : str A string representing which tissue-expression metric should be - calculated. One of: 'counts', 'tsi', 'tau', 'gini', 'simpson', - 'shannon_specificity', 'roku_specificity', 'zscore', 'spm', 'spm_dpm', - 'js_specificity', 'js_specificity_dpm'. + calculated. One of: 'counts', 'tau', 'gini', 'simpson', + 'shannon_specificity', 'roku_specificity', 'tsi', 'zscore', 'spm', + 'spm_dpm', 'js_specificity', 'js_specificity_dpm'. log : bool, default False Log-transform the expression matrix before computing tissue-specificity by taking the base-2 logarithm of one plus the expression values. By @@ -76,12 +76,12 @@ class TissueSpecificity: def __init__(self, expression_data, method, log=False, **kwargs): self._function_dictionary = { 'counts': counts, - 'tsi': tsi, 'tau': tau, 'gini': gini, 'simpson': simpson, 'shannon_specificity': shannon_specificity, 'roku_specificity': roku_specificity, + 'tsi': tsi, 'zscore': zscore, 'spm': spm, 'spm_dpm': spm_dpm, @@ -105,7 +105,7 @@ def __init__(self, expression_data, method, log=False, **kwargs): def _compute_tissue_specificity(self): func = self._function_dictionary[self._method] - if self._method in ['zscore', 'spm', 'js_specificity']: + if self._method in ['tau', 'zscore', 'spm', 'js_specificity']: tissue_specificity = self.expression_data.apply(func, axis=1, result_type='broadcast', transform=self._transform) else: diff --git a/tspex/core/specificity_functions.py b/tspex/core/specificity_functions.py index b44b54c..74de1df 100644 --- a/tspex/core/specificity_functions.py +++ b/tspex/core/specificity_functions.py @@ -82,36 +82,6 @@ def counts(vector, **kwargs): return cts_transformed -def tsi(vector, **kwargs): - """ - Quantify tissue-specificity as the ratio between the vector's maximum - expression value and the sum of the expression values in all tissues. - - Parameters - ---------- - vector : numpy.array - Gene expression vector. Each value corresponds to the gene expression - in a given tissue. - - Returns - ------- - float - Single summary of the tissue-specificity. Ranges from 0 (ubiquitous - expression) to 1 (specific expression). - - References - ---------- - .. [1] Julien, Philippe, et al. "Mechanisms and evolutionary patterns of - mammalian and avian dosage compensation." PLoS biology 10.5 (2012) - """ - - if not np.any(vector): - return 0.0 - else: - tissue_specificity_index = max(vector) / np.sum(vector) - return tissue_specificity_index - - def tau(vector, **kwargs): """ Quantify tissue-specificity as the Tau index [1]. @@ -307,6 +277,36 @@ def roku_specificity(vector, **kwargs): return rs +def tsi(vector, **kwargs): + """ + Quantify tissue-specificity as the ratio between the expression vector + and the sum of the expression values in all tissues. + + Parameters + ---------- + vector : numpy.array + Gene expression vector. Each value corresponds to the gene expression + in a given tissue. + + Returns + ------- + numpy.array + Summary of the tissue-specificity level in each tissue. It ranges from + 0 (ubiquitous expression) to 1 (specific expression). + + References + ---------- + .. [1] Julien, Philippe, et al. "Mechanisms and evolutionary patterns of + mammalian and avian dosage compensation." PLoS biology 10.5 (2012) + """ + + if not np.any(vector): + return 0.0 + else: + tissue_specificity_index = vector / np.sum(vector) + return tissue_specificity_index + + def zscore(vector, **kwargs): """ Quantify tissue-specificity as z-scores. diff --git a/tutorial.ipynb b/tutorial.ipynb index 68667db..7dfaa45 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -267,8 +267,8 @@ "metadata": {}, "source": [ "`tspex` provides twelve different tissue-specificity metrics that can be divided into two groups:\n", - " - **General scoring metrics:** Describe in a single value how tissue-specific or ubiquitous is a gene across all tissues (`counts`, `tsi`, `tau`, `gini`, `simpson`, `shannon_specificity`, `roku_specificity`, `spm_dpm` and `js_specificity_dpm`).\n", - " - **Individualized scoring metrics:** Quantify how specific is the expression of each gene to each tissue (`zscore`, `spm` and `js_specificity`)." + " - **General scoring metrics:** Describe in a single value how tissue-specific or ubiquitous is a gene across all tissues (`counts`, `tau`, `gini`, `simpson`, `shannon_specificity`, `roku_specificity`, `spm_dpm` and `js_specificity_dpm`).\n", + " - **Individualized scoring metrics:** Quantify how specific is the expression of each gene to each tissue (`tsi`, `zscore`, `spm` and `js_specificity`)." ] }, { From fccb1b28d4f226efec39d6a343b7cbe012025269 Mon Sep 17 00:00:00 2001 From: Antonio Camargo Date: Sun, 2 Jun 2019 14:06:44 -0300 Subject: [PATCH 2/6] Update visualization methods to support the new TSI --- tspex/core/specificity_class.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/tspex/core/specificity_class.py b/tspex/core/specificity_class.py index 98696d9..68cafd0 100644 --- a/tspex/core/specificity_class.py +++ b/tspex/core/specificity_class.py @@ -118,8 +118,8 @@ def _compute_tissue_specificity(self): def plot_histogram(self, bins=30, size=(6, 4), dpi=75): """ Plot a histogram of the tissue-specificity values. If the chosen metric - is one of 'zscore', 'spm' or 'js_specificity', the maximum row value is used - as a representative of the gene tissue-specificity. + is one of 'tsi', 'zscore', 'spm' or 'js_specificity', the maximum row + value is used as a representative of the gene tissue-specificity. Parameters ---------- @@ -132,7 +132,7 @@ def plot_histogram(self, bins=30, size=(6, 4), dpi=75): """ with plt.style.context('seaborn-whitegrid'): - if self._method in ['zscore', 'spm', 'js_specificity']: + if self._method in ['tsi', 'zscore', 'spm', 'js_specificity']: data = self.tissue_specificity.max(axis=1).values else: data = self.tissue_specificity.values @@ -147,8 +147,9 @@ def plot_heatmap(self, threshold, sort_genes=False, use_zscore=False, gene_names """ Plot a heatmap of the expression of genes with tissue-specificity over a given a threshold. The threshold should be in the [0,1] range. If the - chosen metric is one of 'zscore', 'spm' or 'js_specificity', the maximum - row value is used as a representative of the gene tissue-specificity. + chosen metric is one of 'tsi', 'zscore', 'spm' or 'js_specificity', the + maximum row value is used as a representative of the gene + tissue-specificity. Parameters ---------- @@ -170,7 +171,7 @@ def plot_heatmap(self, threshold, sort_genes=False, use_zscore=False, gene_names The resolution in dots per inch. """ - if self._method in ['zscore', 'spm', 'js_specificity']: + if self._method in ['tsi', 'zscore', 'spm', 'js_specificity']: ts_data = self.tissue_specificity.max(axis=1) else: ts_data = self.tissue_specificity From 0a949158eb11ac9e6170c6370ea4ddf20795cd69 Mon Sep 17 00:00:00 2001 From: Antonio Camargo Date: Sun, 2 Jun 2019 14:16:43 -0300 Subject: [PATCH 3/6] Tau was wrongly assigned as an individualized metric --- tspex/core/specificity_class.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tspex/core/specificity_class.py b/tspex/core/specificity_class.py index 68cafd0..885770c 100644 --- a/tspex/core/specificity_class.py +++ b/tspex/core/specificity_class.py @@ -105,7 +105,7 @@ def __init__(self, expression_data, method, log=False, **kwargs): def _compute_tissue_specificity(self): func = self._function_dictionary[self._method] - if self._method in ['tau', 'zscore', 'spm', 'js_specificity']: + if self._method in ['tsi', 'zscore', 'spm', 'js_specificity']: tissue_specificity = self.expression_data.apply(func, axis=1, result_type='broadcast', transform=self._transform) else: From 6c3d667a133eae0d2dec6fdaee6f46714c5bd300 Mon Sep 17 00:00:00 2001 From: Antonio Camargo Date: Sun, 2 Jun 2019 14:28:11 -0300 Subject: [PATCH 4/6] Remove genes with expression 0 from the analysis --- tutorial.ipynb | 106 ++++++++++++++++++++++++++++--------------------- 1 file changed, 61 insertions(+), 45 deletions(-) diff --git a/tutorial.ipynb b/tutorial.ipynb index 7dfaa45..ee0850f 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -57,18 +57,34 @@ "expression_data = expression_data.loc[:, ['Bladder', 'Liver', 'Lung', 'Pancreas', 'Stomach']]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll remove genes that are not expressed in any of the five tissues." + ] + }, { "cell_type": "code", "execution_count": 3, "metadata": {}, + "outputs": [], + "source": [ + "expression_data = expression_data.loc[(expression_data > 0).any(axis=1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(56202, 5)" + "(31872, 5)" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -79,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -173,7 +189,7 @@ "ENSG00000268020.2 0.00000 0.02959 0.00000 0.000000 0.000000" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -198,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -215,9 +231,9 @@ " corresponding to genes and columns to tissues/conditions.\n", " method : str\n", " A string representing which tissue-expression metric should be\n", - " calculated. One of: 'counts', 'tsi', 'tau', 'gini', 'simpson',\n", - " 'shannon_specificity', 'roku_specificity', 'zscore', 'spm', 'spm_dpm',\n", - " 'js_specificity', 'js_specificity_dpm'.\n", + " calculated. One of: 'counts', 'tau', 'gini', 'simpson',\n", + " 'shannon_specificity', 'roku_specificity', 'tsi', 'zscore', 'spm',\n", + " 'spm_dpm', 'js_specificity', 'js_specificity_dpm'.\n", " log : bool, default False\n", " Log-transform the expression matrix before computing tissue-specificity\n", " by taking the base-2 logarithm of one plus the expression values. By\n", @@ -294,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -310,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -404,7 +420,7 @@ "ENSG00000268020.2 0.000000 0.029161 0.000000 0.000000 0.000000" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -422,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -437,7 +453,7 @@ "dtype: float64" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -455,19 +471,19 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.50 0.1916\n", - "0.75 0.5593\n", - "0.95 1.0000\n", + "0.50 0.502950\n", + "0.75 0.761925\n", + "0.95 1.000000\n", "dtype: float64" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -492,12 +508,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -519,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -562,7 +578,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -578,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -672,7 +688,7 @@ "ENSG00000268020.2 0.0000 1.0000 0.0000 0.0000 0.0000" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -690,12 +706,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -720,7 +736,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -729,7 +745,7 @@ "(1282, 5)" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -741,7 +757,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -835,7 +851,7 @@ "ENSG00000162493.12 0.6019 0.0214 0.6080 0.2072 0.4740" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -847,7 +863,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -890,7 +906,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -899,7 +915,7 @@ "1.0" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -911,12 +927,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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jRql9AcCSJUvQsGFDDB06FE5OThg1ahR+/fVXAE9vL5o+fTq6dOmCfv36wdzc/JXvMSXSJ7VywgF9ExUVhZKSklK3ENDrVdtuoPfw8MCsWbPQr1+/6i6FqM7jiFIHTp8+jaysLJSUlODYsWP47rvvpHsViV5m37590Gg06NOnT3WXQkTgxTw6cfv2bUybNg1//vknLC0tMW3aNLi5uVV3WTXC6dOn8V//9V9lrvviiy9q/fyk/fv3x4MHD7B06VKe5yPSEzz0SkREJIOHXomIiGTUqkOvz88IU9kbvomIqG55/sDq87e3PVOrghJApaboIiIiKm/CkVoXlMDTD8sR5b+lpqbCwcGhusuocdhvlcN+qxz2W+W8jn4TQsgOsmpVUD4LR4VCwaB8DvujcthvlcN+qxz2W+W8zn4rbz+8mIeIiEgGg5KIiEgGg5KIiEgGg5KIiEiGzoPS3d0dAwcOhL+/P/z9/aUHGEdHR8PT0xPe3t7Yv3+/tP2VK1cQGBgILy8vhISEID8/X1pXXhsiIiJdqZKrXr/66qtSDwk+evQoLly4gISEBOTl5SEoKAiurq4wMzPD/PnzMWPGDLi6uiI6OhobNmxAWFiYbBsiIiJdqZZDr4mJiQgMDISRkREsLS3RuXNnJCcnIzc3FyqVCq6urgCePsD22cNhy2tDRESkS1UyogwJCUFJSQn69u2LyZMnIzs7G1ZWVtJ6KysrqFQqqFSqUiNPa2trqFQqACi3TVlSU1N5P9JzCgsLkZqaWt1l1Djst8phv1UO+61yXke/vezZIDoPytjYWCiVSuTn5+P999/Hhg0bAJS+sfP5IuUCrrw2f+Xg4MCgfA5n/Kgc9lvlsN8qh/1WOa9rZp6zZ8+Wu17nQflshGhmZoagoCDExcVBqVQiMzNT2iY7OxudOnWCUqksNUrMysqS2pfXRle8vb0rtP2BAwd0VAkREVUnnZ6jfPTokXTVanFxMRITE2FnZwcvLy/Ex8dDo9EgOzsbZ86cgYuLC5o2bQpLS0vp3GNcXBw8PT0BoNw2REREuqTTEWVeXh5CQ0NRUlKCkpISODk5YcKECXjjjTeQnJwMHx8fKBQKzJw5U7p6dcGCBZg1axYWLlwIW1tbrFixAgDg4uJSbhsiIiJd0WlQNm/eHHv27ClzXUREBCIiIl5Ybm9vj927d1eoDRERka5wZh4iIiIZDEoiIiIZDEoiIiIZDEoiIiIZDEoiIiIZDEoiIiIZDEoiIiIZVTIpel3AKe+IiGonjiiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkMCiJiIhkVElQLliwAO3atZNeR0dHw9PTE97e3ti/f7+0/MqVKwgMDISXlxdCQkKQn5//0jZERES6pPOgPH36NB4/fiy9Pnr0KC5cuICEhARs2bIFS5YskQJx/vz5mDFjBhITE2Fra4sNGza8tA0REZEu6TQo1Wo1VqxYgZkzZ0rLEhMTERgYCCMjI1haWqJz585ITk5Gbm4uVCoVXF1dAQBDhw5FUlKSbBsiIiJd02lQrlu3DkOHDkXjxo2lZdnZ2bCyspJeW1lZQaVSQaVSQalUSsutra2hUqlk2xAREemaka52fPnyZVy4cAFTp059YZ1CoZB+FkKUuVzbNmVJTU2V3Zc21Gr1K7V/mdTUVJ3u/3mFhYVV+n61BfutcthvlcN+q5zX0W8vyxSdBeXZs2dx7do19OvXDwCg0Wjg7u4ONzc3ZGZmSttlZ2ejU6dOUCqVpUaJWVlZ0ghTqVSW2aY8Dg4OrxyUJiYmr9T+ZRwcHHS6/+elpqZW6fvVFuy3ymG/VQ77rXJeR78JIXD27Nly1+vs0Os777yDo0eP4tChQzh06BAMDQ1x6NAheHl5IT4+HhqNBtnZ2Thz5gxcXFzQtGlTWFpaSuce4+Li4OnpCQDltiEiItI1nY0oy+Pi4oLk5GT4+PhAoVBg5syZMDMzA/D0NpJZs2Zh4cKFsLW1xYoVK17ahoiISJeqLCgvXbok/RwREYGIiIgXtrG3t8fu3bvLbF9eGyIiIl3izDxEREQyGJREREQyGJREREQyGJREREQyGJREREQyGJREREQyGJREREQyGJREREQyGJREREQyGJREREQyGJREREQyGJREREQyGJREREQyGJREREQyGJREREQyGJREREQyGJREREQyGJREREQytArKrKwsqNVqAMC5c+cQGxuL/Px8nRZGRESkD7QKyokTJwIAVCoVZsyYgcuXL2PmzJk6LYyIiEgfaBWUCoUCJiYmOHz4MEaOHIlFixYhIyND17URERFVO62CUqPRIDs7G4mJiXBzc5OWERER1XZaBWVYWBgmTJiA1q1bw97eHr///jtatGih69qIiIiqnZE2G3l4eMDDw0N6/fe//x3r1q3TWVF1gbe3d4W2P3DggI4qISIiOVqNKK9evYqgoCC4u7sDAC5fvozVq1frtDAiIiJ9oFVQzp8/H3PnzkXDhg0BAPb29khKStJpYURERPpAq6AsLCxEx44dSy0zMtLqqC0REVGNplVQmpmZIScnBwqFAgBw7NgxaXRJRERUm2k1LJw9ezYmT56MmzdvIjAwEAUFBbyYh4iI6gStgtLOzg7btm3DjRs3IISAra0tD70SEVGdoHXa5eXl4eHDh9BoNDh37hwAoEuXLjorjIiISB9oFZSffPIJdu7cCVtbWxgaGgJ4Oq0dg5KIiGo7rYIyISEBSUlJqFevnq7rISIi0itaXfVqY2MjjSSJiIjqEq1GlEqlEiNGjECfPn1gYmIiLQ8JCdFZYURERPpAq6C0sbGBjY0NAKC4uFinBREREekTrYIyNDRU13UQERHpJU6KTkREJIOTohMREcnQ6aTowcHB8PPzg6+vL8LCwpCfnw8A2LRpE7y8vODh4YGvv/5a2j4rKwvvvPMOvL29MXr0aGRnZ0vrymtDRESkSzqdFH3NmjXYu3cvvv32W1hZWWHTpk1IT0/H9u3bsXv3bsTHx+Prr7/GrVu3AAArVqyAv78/Dhw4gP79++Pjjz8GANk2REREuqTTSdHNzc0BACUlJXj8+DHMzc1x8OBBDBgwAA0aNAAAeHt74+DBgwgODsaPP/6Ijz76CAAQGBiIVatWAUC5bd59992Kf2IiItJb3t7eFdr+2YBKl3Q+KXpISAjOnz+Pt956CzNnzsTKlSthb28vrbeyssLvv/+O+/fvo169ejA1NQUA1K9fH8bGxnj48CFUKlWZbYiIiHRNq7Q7e/ZsqdcpKSkwMzNDy5YtpWArT0xMDDQaDT7++GPExsYCgHQIFwCEENLPzy+XW/f88rKkpqa+sK+KUqvVr9T+dUtNTa1028LCwldqX1ex3yqH/VY57LenKvrd+zr67WWZolVQLl26FJcuXULbtm0hhMBvv/0Ge3t75OXlYdGiRejVq5dse0NDQwwZMgRhYWHw8/NDZmamtC47OxtKpRKNGjXCo0ePUFhYCFNTUzx+/BjFxcUwNzeHUqkss015HBwcXjkon5+BSB84ODhUum1qauorta+r2G+Vw36rHPbbUxX97jU1NX3lfhNCvDAgfJ5WF/NYWVkhLi4Ou3btwu7du7Fz5060bdsWmzdvxsqVK8tsk5+fj5ycHOl1YmIi2rRpA09PTyQkJODRo0fIz8/HgQMH4OnpCYVCgd69e2PPnj0AgN27d0v3bZbXhoiISNe0GlGmp6eXOkdob2+PtLQ0tGzZstwh68OHDxEaGioNo21tbREVFQULCwuMGDEC/v7+EELgH//4B5o3bw4ACA8Px4wZM/DVV1/BwsJCOknbqlWrctsQERHpktaToq9atQq+vr5QKBTYs2cPLCwsUFxcXO4hTisrK+zcubPMdePGjcO4cePKbPPsPKa2bYiIiHRJq0Ovy5cvR35+PmbMmIHp06ejoKAAy5cvhxACn3/+ua5rJCIiqjZajSj/4z/+A3Pnzi1znbW19WstiIiISJ9oNaIkIiKqqxiUREREMmSDMjExEQBK3cNIRERUl8gGZUxMDADgvffeq5JiiIiI9I3sxTzGxsaYPn06srKyEBUV9cL6xYsX66wwIiIifSAblDExMTh27BjOnTsHJyenqqqJiIhIb8gGZaNGjTBgwAC8+eabLzy4mYiIqC7Q6j5KGxsbTJ8+HSdOnIBCoUC3bt0we/ZsNGnSRNf1ERERVSutbg+ZM2cOOnTogP379yMhIQEODg6YPXu2rmsjIiKqdloFZVZWFoKDg2Fubg5zc3O8++67UKlUuq6NiIio2mkVlEZGRrh8+bL0Oi0tDUZGWh21JSIiqtG0SrtZs2YhJCQETZs2hRACeXl5WLFiha5rIyIiqnZaBaWzszMSExNx48YNCCFga2tb4adQExER1URaHz81MTGBnZ2dLmshIiLSO5wUnYiISAaDkoiISMZLg7KkpARjx46tilqIiIj0zkuD0sDAAKampnj8+HFV1ENERKRXtLqYp1mzZggKCkLfvn1Rv359aXlISIjOCiMiItIHWgWllZUVrKysAADFxcU6LYiIiEifaBWUoaGhuq6DiIhIL2l11evVq1cRFBQEd3d3AMDly5exevVqnRZGRESkD7QKyvnz52Pu3Llo2LAhAMDe3h5JSUk6LYyIiEgfaBWUhYWFLzy4mZOiExFRXaBVUJqZmSEnJwcKhQIAcOzYMWl0SUREVJtpNSycPXs2Jk+ejJs3byIwMBAFBQVYt26drmuj53h7e1do+wMHDuioEiKiukWroLSzs8O2bdtKPT2Eh16JiKgu0DrtkpOTcfLkSSgUCnTp0gW9e/fWZV1ERER6QatzlNHR0fj8889haWmJZs2a4fPPP8fy5ct1XRsREVG102pEeejQIXz77bcwNjYGAIwYMQL+/v54//33dVocERFRddNqRGlhYYGioiLpdVFRkTSlHRERUW0mO6KMiYkBAFhbW2Pw4MHw8PAAABw8eBBdunTRfXVERETVTDYon02A3qJFC7Ro0UJaPmjQIN1WRUREpCdkg5KToRMRUV2n1cU8eXl52LFjB+7cuQONRiMtj46O1llhRERE+kCroAwJCUHXrl3h4uICAwOtrv8hIiKqFbQKyuLiYt4KQkREdZJWQenl5YWEhAR4eHhI91JqIysrC5GRkcjJyYGhoSH69euHadOmAXh62DYpKQkGBgaYNm0afHx8AABXrlxBZGQkCgoKYGtrixUrVsDMzEy2Db3o+blh1Wo1TExMZLfn3LBERGXTKihbtmyJ2bNnIzw8HAqFAkIIKBQK/Prrr7LtDA0NER4ejg4dOkCtVuPdd9/FDz/8AFNTU1y4cAEJCQnIy8tDUFAQXF1dYWZmhvnz52PGjBlwdXVFdHQ0NmzYgLCwMBw9erTcNkRERLqi1QnHFStWYMeOHUhNTcWvv/4q/f/LNGvWDB06dAAAmJiYwM7ODpmZmUhMTERgYCCMjIxgaWmJzp07Izk5Gbm5uVCpVHB1dQUADB06VHpAdHltiIiIdEnrEaWdnd0rvdH9+/dx8OBBbNiwAUePHi01s4+VlRVUKhVUKhWUSqW03NraGiqVCgCQnZ1dZpuypKamSs/OrCy1Wv1K7fWJEOKlnyc1NbWKqqk5CgsL2S+VwH6rHPbbUxX97n0d/SaEkF2vdVBOmjQJ/fr1K3Wuy9fXV6si1Go1wsLCEBwcjNatWwNAqSB7vki5gCuvzV85ODi8clC+7JxeTaLNOUoHB4cqqqbmSE1NZb9UAvutcthvT1X0u9fU1PSV+00IgbNnz5a7XqugLCwsxN/+9jecPn261HJtglKj0SA8PBzt27fHuHHjAABKpRKZmZnSNtnZ2ejUqROUSmWpUWJWVpY0wiyvDRERkS5pFZRLliyp9BtERUWhQYMGiIiIkJZ5eXnh888/R2BgIO7evYszZ85g4cKFMDMzg6UULdL9AAAQHklEQVSlJZKTk+Hi4oK4uDh4enrKtiEiItIlrYIyPj6+zOUBAQGy7c6cOYOdO3eibdu20rZDhgzB2LFjkZycDB8fHygUCsycOVO6enXBggWYNWsWFi5cKN0eAgAuLi7ltiEiItIVrYLy+UOuarUaJ0+ehIODw0uDsnPnzrhy5UqZ6yIiIkqNMp+xt7fH7t27K9SGiIhIV7QKyg8++KDU6z/++APz5s3TSUFERET6pFITtzZs2BDXr19/3bUQERHpHa1GlHPnzpVutygpKcHly5eliQSIiIhqM62CsnPnztLPhoaGGDJkCJycnHRWFBERkb7QKigDAwN1XQcREZFekg3KMWPGlDvDjUKhwObNm3VSFBERkb6QDcqpU6e+sOzatWtYv359hR63RUREVFPJBuXz5yZv3LiBTz/9FBcvXsR777330nsoiYiIaoOXnqO8desW1q5di7Nnz2LChAlYtmwZjIy0OrVJRERU48km3qxZs3Dy5EmMHz8eS5YsYUASEVGdI5t8u3fvhoGBAT744AN8+OGH0nIhBBQKhVYPbyYiIqrJZIPy8uXLVVUHERGRXqrUFHZERER1BYOSiIhIBoOSiIhIBi9jJQCAt7d3hbY/cOCAjiohItIvHFESERHJYFASERHJYFASERHJYFASERHJYFASERHJYFASERHJYFASERHJYFASERHJYFASERHJYFASERHJYFASERHJYFASERHJYFASERHJYFASERHJYFASERHJ4PMoqVIq+vxKgM+wJKKaiSNKIiIiGQxKIiIiGQxKIiIiGQxKIiIiGQxKIiIiGQxKIiIiGQxKIiIiGTq9j3Lx4sVISkrC3bt3cenSJWl5dHQ0kpKSYGBggGnTpsHHxwcAcOXKFURGRqKgoAC2trZYsWIFzMzMZNsQke5U9H5Z3itLtZFOg7J///6YOHEievXqJS07evQoLly4gISEBOTl5SEoKAiurq4wMzPD/PnzMWPGDLi6uiI6OhobNmxAWFiYbBuqOfilS0Q1kU4PvTo7O8PCwqLUssTERAQGBsLIyAiWlpbo3LkzkpOTkZubC5VKBVdXVwDA0KFDkZSUJNuGiIhI16p8Crvs7GxYWVlJr62srKBSqaBSqaBUKqXl1tbWUKlUsm3Kk5qaCoVC8Up1qtXqV2qvT4QQNfLzpKamVuv7FxYWVnsN1a2i/25SU1PZb5XEfnuqov/mXke/CSFk11fLXK/Ph9jzBcqFW3ltyuLg4PDKQWliYvJK7fWJWq2ukZ/HwcGhWt8/NTW12muobhX9d+Pg4MB+qyT221MV/Tdnamr6yv0mhMDZs2fLXV/lV70qlUpkZmZKr7Ozs6FUKqFUKkuNErOysqQRZnltiIiIdK3Kg9LLywvx8fHQaDTIzs7GmTNn4OLigqZNm8LS0lI69xgXFwdPT0/ZNkRERLqm00Ov8+bNw5EjR6DRaNCrVy/06dMHixYtQnJyMnx8fKBQKDBz5kzp6tUFCxZg1qxZWLhwoXR7CAC4uLiU24aIiEiXdBqUixYtKnN5REQEIiIiXlhub2+P3bt3V6gNERGRLnFmHiIiIhnVctUrkTYqOkFBRXFCAyLSBkeUREREMjiipDrrZSPWv95/yhEoUd3EESUREZEMBiUREZEMBiUREZEMBiUREZEMBiUREZEMBiUREZEMBiUREZEMBiUREZEMTjhApKWKTqnHCQqIageOKImIiGQwKImIiGQwKImIiGQwKImIiGQwKImIiGQwKImIiGTw9hAiHeHtJES1A0eUREREMjiiJNITtWEE6u3tDbVaDRMTE62218fPQPRXHFESERHJYFASERHJYFASERHJYFASERHJ4MU8RDVUbbj4h6gm4IiSiIhIBkeURFRtOCqmmoAjSiIiIhkcURLVERUdvekjjkCpOnBESUREJINBSUREJINBSUREJIPnKImo1tL1eVmeA60bOKIkIiKSwRElEVEl8SrcuoFBSURUReSCtazneDJY9QODkoioFuF52devRgXl8ePHsWjRIhQVFcHZ2RmLFy+GkVGN+ghERFqri5NE6KMakzIajQZz5sxBTEwM2rRpgylTpmDPnj0YMmRIdZdGRFRn1Ibgq6gaE5QXL16EUqlEmzZtAABDhw7Ftm3bSgWlEKLU/78KA4Pac0GwgYFBrfo8VYX9Vjnst8phv1WOEOKVv/Ofzw6FQvHC+hoTlCqVCkqlUnptbW0NlUpV5rbnzp175ff74IMPXnkfRESkW4WFhTh79qxO36PGBCWAUklf3l8Qjo6OL2xLRERUnpeNSGtMUFpZWSErK0t6/dcRJlC7DpcSEVHVeNnAqsYkS/v27ZGdnY1r164BAHbu3AlPT89qroqIiGq7GhOUhoaGWLx4McLCwuDp6Yk33ngD/v7+1V2W3jh+/DgGDBgAT09PzJo1C8XFxaXWp6WlYcSIERg4cCB8fX2xbdu2aqpUv7ys3543YcIE/nH2/7Tpt9zcXISEhMDHxwc+Pj44fPhwNVSqX7Tpt/j4eAwaNAh+fn4IDg5Gbm5uNVSqXxYvXoxevXqhXbt25W5Tkf+WK0xQjVdcXCzc3d3Fb7/9JoQQIiwsTMTFxZXaJj09XVy/fl0IIUR+fr7w8vISaWlpVV6rPtGm357Zs2ePCA8PFx4eHlVZol7Stt+Cg4PFvn37hBBCFBUViXv37lVpnfpGm3578uSJ6NKli8jLyxNCCLFy5UqxdOnSKq9V35w6dUrk5uaKt99+u8z1FflvuTJqzIiSylfWrTNJSUmltmnVqhVsbW0BAA0aNECrVq2QmZlZ5bXqE236DQDu3buH2NhYhISEVHWJekmbfrt+/Tru3buHAQMGAACMjIzQqFGjKq9Vn2jTbyUlJQCAx48fQwiB/Px8NGvWrMpr1TfOzs6wsLAod722/y1XFoOyFqjIrTMAcPPmTVy8eFG6Qriu0rbfPvroI0yZMgWmpqZVWZ7e0qbfbty4gSZNmmDatGkICAhAeHg47t+/X9Wl6hVt+q1evXpYuHAh/Pz84ObmhkuXLmHUqFFVXWqNU9HvwIpiUNYS2tw6AwAPHjxAaGgo5s2bV+f/wgde3m8//vgjDA0N0aNHj6osS++9rN+Ki4tx8uRJTJgwAfHx8bCxsUF0dHRVlqiXXtZv+fn52LFjB/bt24ejR4+iZ8+eWLJkSVWWWGNp+x1YGQzKWkCbW2eAp4dz/vu//xsjRoyok9NQ/ZU2/Xb69GkcO3YM7u7ueOedd5CZmYmBAwdWdal6RZt+s7a2Rps2bfD2228DAHx9fXHp0qUqrVPfaNNvR48ehYWFhbTc19cXJ06cqNI6ayJtvwMri0FZC2hz60xRURFCQ0Ph6urKQzn/T5t+mzFjBn766SccOnQIsbGxsLa2xr59+6qjXL2hTb+1b98eRUVFyMjIAAAkJyfjrbfeqvJa9Yk2/WZtbY2UlBTk5+cDAH7++ec632/a0PXtgzVmwgEq3/O3zjx7soq/vz9++OEHHDp0CB9++CESEhLwyy+/4O7du9JJ7okTJ8LHx6eaq68+2vQbvUibfjMwMMD8+fPx3nvvoaSkBM2aNcNHH31U3aVXK236rWPHjhg6dCiGDRsGY2NjNGnShP8OAcybNw9HjhyBRqNBr1690KdPHwwbNgxr1qzBF198UW7fvi4K8boP5hIREdUiPPRKREQkg0FJREQkg0FJREQkg0FJREQkg0FJREQkg0FJREQkg0FJVA1OnDiBcePGVXcZWhk8eLD089atW9G/f3+MGzcOFy9exLx582Tbbt++Hd988w2Ap5/51KlTOq2VSBc44QCRjmg0GhgaGlZ3Ga9s165d0s/btm3Dp59+itatWwMAOnToINt25MiR0s8nT56EoaEhunTpoptCiXSEI0qi16hdu3ZYv349RowYge+//x4qlQr//Oc/4evriyFDhuD8+fMvtLl16xYCAgJw+vTpF0aan332GdauXVvu+6Wnp2P48OEICAjAwIEDcfDgQQDAmDFjsHTpUgwePBje3t6lpt1LSEjAsGHDEBAQgMmTJ+Phw4cAgDt37mD8+PHw9fWFn58fjh07Jn0mAJg1axZu376NsLAwrFix4oVat2zZIrUNCwsDAKxduxafffYZ0tPTsWPHDsTGxsLf3x8JCQnw9vbGnTt3pPbjx4/Hjz/+WMEeJ9I9jiiJXiONRoPGjRtjx44dAIDQ0FD07t0bY8eORUpKCqZOnYoDBw5I21++fBnvv/8+li1bhnbt2lV4AuzY2FiMHDkSAQEB0vMLn8nJycHOnTuRk5ODIUOGoEePHvjjjz/wr3/9C9u2bYOJiQn+53/+BzExMXj//fcRHh6OESNGICAgACUlJaX2BQBLlizBiRMn8NVXX0GpVJaqNTk5Gbt370ZsbCzMzc1feKSWra0tRowYAUNDQ0yaNAkAkJGRgZ07d2LKlCnIyspCeno63NzcKvT5iaoCg5LoNfPz85N+PnnyJJYtWwYA6NixI8zNzXHz5k0AT0eSYWFhiImJkR6qXVFOTk747LPPoFKp0KtXL2n0BwD+/v5QKBSwtLREp06dkJKSgjt37uDq1asYNmwYgKePw2rbti3y8/Nx48YNBAQEAAAMDAzQsGFDres4evQoBg8eDHNzcwDQ6hFugwcPRlBQEEJDQxEXF4fBgwfDwIAHuUj/MCiJXiNDQ8OXPuD52XPzmjRpAkNDQ5w5c0YKSkNDQ+kp9wCgVqtlz3MOGDAA//mf/4mff/4ZUVFR6N+/P8aPH1/u+woh0L9/f8yZM6fUur+OHiuqMlNGN27cGB07dsSRI0cQHx+PrVu3vlINRLrCP9+IdKhr166Ii4sDAKSkpODhw4do0aIFAKBBgwb44osv8M0332Dnzp0AgDfffBPp6el48uQJnjx5gp9++kl2/zdv3oSVlRWGDx+OcePGISUlRVq3Z88eCCGQnZ2NCxcuoEOHDujZsycOHjwoPf398ePHuH79OszMzNCqVSvEx8cDAEpKSvDnn39q/Tl79eqFXbt2Sec7/3roFQDMzMxQUFBQatnw4cOxaNEitG7dGlZWVlq/H1FV4oiSSIfmzp2LOXPmIC4uDiYmJli5ciWMjY2l9ebm5vjyyy8RHBwMAwMDBAYGYvDgwfDz80PLli3Rpk0b2f0nJCTg22+/hbGxMUxNTbFw4UJpXfPmzfHOO+/g3r17iIyMROPGjdG4cWNERkZi4sSJ0Gg0EEIgNDQUrVu3xvLly7FgwQJ89dVXMDAwQGRkJHr06KHV5+zZsycCAgIwYsQIGBkZ4e9//ztWr15dahsPDw9MnjwZx44dQ0hICLy8vNCtWzcYGBggKCioAr1KVLX4mC2iWmjMmDGYMmUKnJ2dq7sUWZmZmRg7diz2798PIyP+3U76iYdeiahabN68GaNGjUJ4eDhDkvQaR5RENcCnn36KpKSkUstatWqFVatWVVNFRHUHg5KIiEgGD70SERHJYFASERHJYFASERHJYFASERHJYFASERHJ+D8hJehrm2EpzwAAAABJRU5ErkJggg==\n", 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" ] @@ -931,7 +947,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -940,7 +956,7 @@ "2.3219" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -952,12 +968,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -993,7 +1009,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1002,7 +1018,7 @@ "2400" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1014,7 +1030,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1043,7 +1059,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1052,7 +1068,7 @@ "1557" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1064,7 +1080,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [ { From 003e765bb4f1a13b94f918e9d3a5a5ec928cc29d Mon Sep 17 00:00:00 2001 From: Antonio Camargo Date: Sun, 2 Jun 2019 14:29:21 -0300 Subject: [PATCH 5/6] Version 0.5.0 --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 732e8f9..57bfd96 100644 --- a/setup.py +++ b/setup.py @@ -25,7 +25,7 @@ setup( name='tspex', - version='0.4.0', + version='0.5.0', packages=find_packages(), license='GNU General Public License v3.0', description='A Python package for calculating tissue-specificity metrics for gene expression.', From 5ff645d3af3a5a6dae0c280675cad9f67618f200 Mon Sep 17 00:00:00 2001 From: Antonio Camargo Date: Sun, 2 Jun 2019 14:41:57 -0300 Subject: [PATCH 6/6] Change order of the metrics in the CLI --- README.md | 6 +++--- tspex/cli.py | 5 ++--- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index e25c45c..3ba1a06 100644 --- a/README.md +++ b/README.md @@ -50,9 +50,9 @@ positional arguments: input_file Expression matrix file in the TSV or CSV formats. output_file Output TSV file containing tissue-specificity values. method Tissue-specificity metric. Allowed values are: - "counts", "tsi", "tau", "gini", "simpson", - "shannon_specificity", "roku_specificity", "zscore", - "spm", "spm_dpm", "js_specificity", + "counts", "tau", "gini", "simpson", + "shannon_specificity", "roku_specificity", "tsi", + "zscore", "spm", "spm_dpm", "js_specificity", "js_specificity_dpm". optional arguments: diff --git a/tspex/cli.py b/tspex/cli.py index 05c47c2..c7a03fb 100644 --- a/tspex/cli.py +++ b/tspex/cli.py @@ -39,9 +39,8 @@ def tspex_cli(input_file, output_file, method, log, disable_transformation, thre def main(): - method_choices = ['counts', 'tsi', 'tau', 'gini', 'simpson', 'shannon_specificity', - 'roku_specificity', 'zscore', 'spm', 'spm_dpm', 'js_specificity', - 'js_specificity_dpm'] + method_choices = ['counts', 'tau', 'gini', 'simpson', 'shannon_specificity', 'roku_specificity', + 'tsi', 'zscore', 'spm', 'spm_dpm', 'js_specificity', 'js_specificity_dpm'] parser = argparse.ArgumentParser( description='Compute gene tissue-specificity from an expression matrix and save the output.', formatter_class=argparse.ArgumentDefaultsHelpFormatter)