Commit 162808fb authored by Chris Evenhuis's avatar Chris Evenhuis
Browse files

Added results file

parent 33065cf9
analysis-plots/B-tubulin-r2-circ.png

15 KB | W: | H:

analysis-plots/B-tubulin-r2-circ.png

14.6 KB | W: | H:

analysis-plots/B-tubulin-r2-circ.png
analysis-plots/B-tubulin-r2-circ.png
analysis-plots/B-tubulin-r2-circ.png
analysis-plots/B-tubulin-r2-circ.png
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......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {},
"outputs": [
{
......@@ -27,7 +27,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
......@@ -43,7 +43,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
......@@ -57,7 +57,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
......@@ -85,7 +85,7 @@
" #sorg = get_name(org, orgs )\n",
" #streat = get_name(treat,treats)\n",
" sorg,streat = convert_org_treat(date,org,treat)\n",
" path=\"MT_Oct22/201810{1:}_at_{5:}h/{2:}_{3:}_{4:}_{5:}h_nostain_clust.csv\".format(date,int(date+time/24-1),rep,sorg,streat,time)\n",
" path=\"/Volumes/scratch/megan/MT_Oct22/201810{1:}_at_{5:}h/{2:}_{3:}_{4:}_{5:}h_nostain_clust.csv\".format(date,int(date+time/24-1),rep,sorg,streat,time)\n",
" \n",
" if(date==29):\n",
" #orgs = ['H99WT','H99S', 'Yox1KO']\n",
......@@ -95,7 +95,7 @@
" # treats = ['control','0.5xMIC' ]\n",
" #streat = get_name(treat,treats)\n",
" sorg,streat = convert_org_treat(date,org,treat)\n",
" path=\"MT_Oct29/201810{1:}_at_{5:}h/{2:}_{3:}_{4:}_{5:}h_CFW_clust.csv\".format(date,int(date),rep,sorg,streat,time)\n",
" path=\"/Volumes/scratch/megan/MT_Oct29/201810{1:}_at_{5:}h/{2:}_{3:}_{4:}_{5:}h_CFW_clust.csv\".format(date,int(date),rep,sorg,streat,time)\n",
" return path"
]
},
......@@ -108,7 +108,7 @@
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 9,
"metadata": {},
"outputs": [
{
......@@ -143,22 +143,22 @@
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MT_Oct22/20181022_at_24h/1_B-tubulin_control_24h_nostain_clust.csv\n",
"MT_Oct22/20181023_at_48h/1_B-tubulin_control_48h_nostain_clust.csv\n",
"MT_Oct22/20181022_at_24h/1_B-tubulin_0.5xMIC_24h_nostain_clust.csv\n",
"MT_Oct22/20181023_at_48h/1_B-tubulin_0.5xMIC_48h_nostain_clust.csv\n"
"/Volumes/scratch/megan/MT_Oct22/20181022_at_24h/2_B-tubulin_control_24h_nostain_clust.csv\n",
"/Volumes/scratch/megan/MT_Oct22/20181023_at_48h/2_B-tubulin_control_48h_nostain_clust.csv\n",
"/Volumes/scratch/megan/MT_Oct22/20181022_at_24h/2_B-tubulin_0.5xMIC_24h_nostain_clust.csv\n",
"/Volumes/scratch/megan/MT_Oct22/20181023_at_48h/2_B-tubulin_0.5xMIC_48h_nostain_clust.csv\n"
]
},
{
"data": {
"image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 432x432 with 4 Axes>"
]
......@@ -553,7 +553,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 22,
"metadata": {
"hidden": true
},
......@@ -562,10 +562,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
"MT_Oct22/20181022_at_24h/1_B-tubulin_control_24h_nostain_clust.csv\n",
"MT_Oct22/20181023_at_48h/1_B-tubulin_control_48h_nostain_clust.csv\n",
"MT_Oct22/20181022_at_24h/1_B-tubulin_0.5xMIC_24h_nostain_clust.csv\n",
"MT_Oct22/20181023_at_48h/1_B-tubulin_0.5xMIC_48h_nostain_clust.csv\n"
"/Volumes/scratch/megan/MT_Oct22/20181022_at_24h/2_B-tubulin_control_24h_nostain_clust.csv\n",
"/Volumes/scratch/megan/MT_Oct22/20181023_at_48h/2_B-tubulin_control_48h_nostain_clust.csv\n",
"/Volumes/scratch/megan/MT_Oct22/20181022_at_24h/2_B-tubulin_0.5xMIC_24h_nostain_clust.csv\n",
"/Volumes/scratch/megan/MT_Oct22/20181023_at_48h/2_B-tubulin_0.5xMIC_48h_nostain_clust.csv\n"
]
}
],
......@@ -622,14 +622,14 @@
},
{
"cell_type": "code",
"execution_count": 799,
"execution_count": 23,
"metadata": {
"hidden": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -1585,7 +1585,7 @@
"heading_collapsed": true
},
"source": [
"## Circ of cells"
"## Roundness of cells"
]
},
{
......@@ -1599,7 +1599,7 @@
},
{
"cell_type": "code",
"execution_count": 108,
"execution_count": 103,
"metadata": {
"hidden": true
},
......@@ -1618,9 +1618,39 @@
" 1.0: 1.0}"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"For roundness"
]
},
{
"cell_type": "code",
"execution_count": 109,
"execution_count": 11,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"circ2axis={0.0: 0.0,\n",
" 0.1: 0.1,\n",
" 0.2: 0.2,\n",
" 0.3: 0.3,\n",
" 0.4: 0.4,\n",
" 0.5: 0.5,\n",
" 0.6: 0.6,\n",
" 0.7: 0.7,\n",
" 0.8: 0.8,\n",
" 0.9: 0.9,\n",
" 1.0: 1.0}"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"hidden": true
},
......@@ -1629,15 +1659,15 @@
"name": "stdout",
"output_type": "stream",
"text": [
"MT_Oct22/20181022_at_24h/1_B-tubulin_control_24h_nostain_clust.csv\n",
"MT_Oct22/20181023_at_48h/1_B-tubulin_control_48h_nostain_clust.csv\n",
"MT_Oct22/20181022_at_24h/1_B-tubulin_0.5xMIC_24h_nostain_clust.csv\n",
"MT_Oct22/20181023_at_48h/1_B-tubulin_0.5xMIC_48h_nostain_clust.csv\n"
"/Volumes/scratch/megan/MT_Oct22/20181022_at_24h/2_B-tubulin_control_24h_nostain_clust.csv\n",
"/Volumes/scratch/megan/MT_Oct22/20181023_at_48h/2_B-tubulin_control_48h_nostain_clust.csv\n",
"/Volumes/scratch/megan/MT_Oct22/20181022_at_24h/2_B-tubulin_0.5xMIC_24h_nostain_clust.csv\n",
"/Volumes/scratch/megan/MT_Oct22/20181023_at_48h/2_B-tubulin_0.5xMIC_48h_nostain_clust.csv\n"
]
},
{
"data": {
"image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 432x432 with 4 Axes>"
]
......@@ -1687,7 +1717,7 @@
" if(time==48):\n",
" h=y1-y0\n",
" w=x1-x0\n",
" for c in [0.7,0.8,0.9,1.0]:\n",
" for c in [0.6,0.7,0.8,0.9,1.0]:\n",
" e = Ellipse(xy=(c,-h*0.17),height=0.09*h,width=0.09*w*circ2axis[c],clip_on=False,color='grey')\n",
" ax.add_patch(e)\n",
"ax.annotate(\"Roundness\",(0.52,0.02),xycoords=\"figure fraction\",ha=\"center\",fontsize=14)\n",
......@@ -1698,86 +1728,57 @@
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"dfc[\"Roundess\"] = dfc['Roundess'].where(dfc['Roundess']<1.0, 0.99999)"
]
},
{
"cell_type": "code",
"execution_count": 159,
"execution_count": 37,
"metadata": {
"hidden": true
},
"outputs": [
{
"data": {
"text/plain": [
"(6.962181948227787, 0.9678963684010562, 0, 1)"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
"name": "stdout",
"output_type": "stream",
"text": [
"/Volumes/scratch/megan/MT_Oct22/20181022_at_24h/2_B-tubulin_control_24h_nostain_clust.csv\n",
"/Volumes/scratch/megan/MT_Oct22/20181023_at_48h/2_B-tubulin_control_48h_nostain_clust.csv\n",
"/Volumes/scratch/megan/MT_Oct22/20181022_at_24h/2_B-tubulin_0.5xMIC_24h_nostain_clust.csv\n",
"/Volumes/scratch/megan/MT_Oct22/20181023_at_48h/2_B-tubulin_0.5xMIC_48h_nostain_clust.csv\n"
]
}
],
"source": [
"stats.beta.fit(tmp,floc=0,fscale=1)"
"df_array=[]\n",
"for it,treat in enumerate(itreats):\n",
" for ih,time in enumerate(itimes):\n",
" path = get_data(date,iorg,rep,it,time)\n",
" print(path)\n",
" if( os.path.exists(path)):\n",
" df =pd.read_csv(path)\n",
" df['Treat']=treat\n",
" df['Time']=time\n",
" df['Rep']=rep\n",
"\n",
" # add the cluster count for each particle\n",
" for ic in df['Cluster'].unique():\n",
" mask = df['Cluster']==ic\n",
" df.loc[mask,'Cluster_count']=sum(mask)\n",
"\n",
" df_array.append(df)\n",
"dfc = pd.concat(df_array)"
]
},
{
"cell_type": "code",
"execution_count": 188,
"execution_count": 38,
"metadata": {
"hidden": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(12.762534566809661, 0.7871817168180372, 0, 0.9999900000000002)\n"
]
},
{
"data": {
"text/plain": [
"(0.6, 1)"
]
},
"execution_count": 188,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"df=dfc[(dfc['Treat']==0)].copy()\n",
"\n",
"\n",
"plt.hist(df['Roundess'],align='mid',bins=bins,density=True)\n",
"x = np.linspace(0,1,201)\n",
"print(stats.beta.fit(df['Roundess'],floc=0,scale=1))\n",
"plt.plot(x,stats.beta.pdf(x,6.86,1,scale=1))\n",
"plt.xlim(0.6,1)"
"dfc[\"Roundess\"] = dfc['Roundess'].where(dfc['Roundess']<1.0, 0.99999)"
]
},
{
"cell_type": "code",
"execution_count": 189,
"execution_count": 39,
"metadata": {
"hidden": true
},
......@@ -1816,6 +1817,8 @@
" <th>String length</th>\n",
" <th>Treat</th>\n",
" <th>Time</th>\n",
" <th>Rep</th>\n",
" <th>Cluster_count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
......@@ -1824,143 +1827,149 @@
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>30.751</td>\n",
" <td>6.556</td>\n",
" <td>1.504</td>\n",
" <td>170.844</td>\n",
" <td>0.911</td>\n",
" <td>2882.049</td>\n",
" <td>0</td>\n",
" <td>0.000</td>\n",
" <td>36.356</td>\n",
" <td>7.446</td>\n",
" <td>22.624</td>\n",
" <td>0.893</td>\n",
" <td>0.835</td>\n",
" <td>6569.307</td>\n",
" <td>3</td>\n",
" <td>10.177</td>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>30.708</td>\n",
" <td>6.943</td>\n",
" <td>1.510</td>\n",
" <td>169.205</td>\n",
" <td>0.811</td>\n",
" <td>2865.910</td>\n",
" <td>0</td>\n",
" <td>0.000</td>\n",
" <td>34.969</td>\n",
" <td>6.785</td>\n",
" <td>22.066</td>\n",
" <td>0.902</td>\n",
" <td>0.967</td>\n",
" <td>5750.617</td>\n",
" <td>7</td>\n",
" <td>13.689</td>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>29.518</td>\n",
" <td>6.783</td>\n",
" <td>1.485</td>\n",
" <td>168.317</td>\n",
" <td>0.817</td>\n",
" <td>2743.700</td>\n",
" <td>0</td>\n",
" <td>0.000</td>\n",
" <td>40.032</td>\n",
" <td>7.515</td>\n",
" <td>23.620</td>\n",
" <td>0.902</td>\n",
" <td>0.903</td>\n",
" <td>4937.377</td>\n",
" <td>7</td>\n",
" <td>12.451</td>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>24.736</td>\n",
" <td>5.685</td>\n",
" <td>1.350</td>\n",
" <td>170.554</td>\n",
" <td>0.975</td>\n",
" <td>3565.513</td>\n",
" <td>3</td>\n",
" <td>6.694</td>\n",
" <td>47.151</td>\n",
" <td>7.947</td>\n",
" <td>25.585</td>\n",
" <td>0.905</td>\n",
" <td>0.951</td>\n",
" <td>5219.837</td>\n",
" <td>4</td>\n",
" <td>9.462</td>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>27.733</td>\n",
" <td>6.131</td>\n",
" <td>1.433</td>\n",
" <td>169.663</td>\n",
" <td>0.939</td>\n",
" <td>2747.476</td>\n",
" <td>0</td>\n",
" <td>0.000</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>22.298</td>\n",
" <td>6.051</td>\n",
" <td>19.037</td>\n",
" <td>0.773</td>\n",
" <td>0.775</td>\n",
" <td>4143.744</td>\n",
" <td>2</td>\n",
" <td>4.779</td>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Frame Cluster sub Area Major Perimeter Circ. Roundess Mean Int \\\n",
"0 1 1 1 30.751 6.556 1.504 170.844 0.911 2882.049 \n",
"1 1 2 1 30.708 6.943 1.510 169.205 0.811 2865.910 \n",
"2 1 3 1 29.518 6.783 1.485 168.317 0.817 2743.700 \n",
"3 1 4 1 24.736 5.685 1.350 170.554 0.975 3565.513 \n",
"4 1 5 1 27.733 6.131 1.433 169.663 0.939 2747.476 \n",
" Frame Cluster sub Area Major Perimeter Circ. Roundess Mean Int \\\n",
"0 1 1 1 36.356 7.446 22.624 0.893 0.835 6569.307 \n",
"1 1 2 1 34.969 6.785 22.066 0.902 0.967 5750.617 \n",
"2 1 3 1 40.032 7.515 23.620 0.902 0.903 4937.377 \n",
"3 1 4 1 47.151 7.947 25.585 0.905 0.951 5219.837 \n",
"4 1 4 2 22.298 6.051 19.037 0.773 0.775 4143.744 \n",
"\n",
" nStrings String length Treat Time \n",
"0 0 0.000 0 24 \n",
"1 0 0.000 0 24 \n",
"2 0 0.000 0 24 \n",
"3 3 6.694 0 24 \n",
"4 0 0.000 0 24 "
" nStrings String length Treat Time Rep Cluster_count \n",
"0 3 10.177 0 24 2 1.0 \n",
"1 7 13.689 0 24 2 1.0 \n",
"2 7 12.451 0 24 2 1.0 \n",
"3 4 9.462 0 24 2 2.0 \n",
"4 2 4.779 0 24 2 2.0 "
]
},
"execution_count": 189,
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
"dfc.head()"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {
"hidden": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 191,
"execution_count": 42,
"metadata": {
"hidden": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/evenhuis/anaconda3/lib/python3.5/site-packages/scipy/optimize/minpack.py:162: RuntimeWarning: The iteration is not making good progress, as measured by the \n",
" improvement from the last ten iterations.\n",
" warnings.warn(msg, RuntimeWarning)\n"
]
},
{
"data": {
"text/plain": [
"(array([ 0.75099051, 0. , 0.93873814, 1.31423339, 2.0652239 ,\n",
" 2.81621441, 5.06918594, 7.13440984, 5.82017645, 11.82810053]),\n",
" array([0.735 , 0.761499, 0.787998, 0.814497, 0.840996, 0.867495,\n",
" 0.893994, 0.920493, 0.946992, 0.973491, 0.99999 ]),\n",
" <a list of 10 Patch objects>)"
"(0.6, 1)"
]
},
"execution_count": 191,
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -1969,6 +1978,65 @@
"output_type": "display_data"
}
],
"source": [
"df=dfc[(dfc['Treat']==1)].copy()\n",
"bins=np.linspace(0.5,1,21)\n",
"\n",
"plt.hist(df['Roundess'],align='mid',bins=bins,density=True)\n",
"x = np.linspace(0,1,401)\n",
"params = stats.beta.fit(df['Roundess'],floc=0,scale=1)\n",
"plt.plot(x,stats.beta.pdf(x,*params))\n",
"plt.xlim(0.6,1)"
]
},
{