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如何对 pandas dataframe 的每一行进行拟合、预测和 plot 线性回归？

[英]How to fit, predict, and plot linear regression for each row of pandas dataframe?

 原文  2022-08-01 08:18:45  2   2    python/ machine-learning/ scikit-learn/ plotly/ regression

问题描述

I want to perform linear regression analysis to determine the effect of time-dependent treatment on gene expression and survival for each gene.我想进行线性回归分析以确定时间依赖性治疗对每个基因的基因表达和存活的影响。

In df4 dataframe, the time column represents time, the last column survival_info represents survival info, and the remaining columns represent gene expression.在df4 dataframe中， time列代表时间，最后一列survival_info代表生存信息，其余列代表基因表达。

For each row of df5 dataframe, I want to individually fit, predict and plot linear regression using sklearn.对于df5 dataframe的每一行，我想使用sklearn单独拟合，预测和plot线性回归。

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split


df5 = df4.groupby(["time"], sort=False).mean()
df5 = df5.reset_index()
df5["time"] = df5["time"].str[:-1]
df5 = df5.set_index("time").T

# split the dataset into train and test sets
X = df5.iloc[:,1:]
y = df5.iloc[:,0]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)

# Linear regression analysis
reg = LinearRegression().fit(X_train, y_train)
y_pred = reg.predict(X_test)  

# Plot
for i in X_test.index:
  fig = px.scatter(X_test.loc[i], y_pred.flatten())
  fig.update_layout(title="Regression analysis of time-dependent treatment on " + i + " expression", xaxis_title="Time", yaxis_title="Gene Expression", legend_title="Survival info")
  fig.show()

Traceback:追溯：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-422-04ea16d131bd> in <module>()
      1 # Plot
      2 for i in X_test.index:
----> 3   fig = px.scatter(X_test.loc[i], y_pred.flatten())
      4   fig.update_layout(title="Regression analysis of time-dependent treatment on " + i + " expression", xaxis_title="Time", yaxis_title="Gene Expression", legend_title="Survival info")
      5   fig.show()

3 frames
/usr/local/lib/python3.7/dist-packages/plotly/express/_chart_types.py in scatter(data_frame, x, y, color, symbol, size, hover_name, hover_data, custom_data, text, facet_row, facet_col, facet_col_wrap, facet_row_spacing, facet_col_spacing, error_x, error_x_minus, error_y, error_y_minus, animation_frame, animation_group, category_orders, labels, orientation, color_discrete_sequence, color_discrete_map, color_continuous_scale, range_color, color_continuous_midpoint, symbol_sequence, symbol_map, opacity, size_max, marginal_x, marginal_y, trendline, trendline_options, trendline_color_override, trendline_scope, log_x, log_y, range_x, range_y, render_mode, title, template, width, height)
     64     mark in 2D space.
     65     """
---> 66     return make_figure(args=locals(), constructor=go.Scatter)
     67 
     68 

/usr/local/lib/python3.7/dist-packages/plotly/express/_core.py in make_figure(args, constructor, trace_patch, layout_patch)
   1943     apply_default_cascade(args)
   1944 
-> 1945     args = build_dataframe(args, constructor)
   1946     if constructor in [go.Treemap, go.Sunburst, go.Icicle] and args["path"] is not None:
   1947         args = process_dataframe_hierarchy(args)

/usr/local/lib/python3.7/dist-packages/plotly/express/_core.py in build_dataframe(args, constructor)
   1404 
   1405     df_output, wide_id_vars = process_args_into_dataframe(
-> 1406         args, wide_mode, var_name, value_name
   1407     )
   1408 

/usr/local/lib/python3.7/dist-packages/plotly/express/_core.py in process_args_into_dataframe(args, wide_mode, var_name, value_name)
   1247                         "The length of argument `%s` is %d, whereas the "
   1248                         "length of  previously-processed arguments %s is %d"
-> 1249                         % (field, len(argument), str(list(df_output.columns)), length)
   1250                     )
   1251                 df_output[str(col_name)] = to_unindexed_series(argument)

ValueError: All arguments should have the same length. The length of argument `y` is 5, whereas the length of  previously-processed arguments ['x'] is 18

Data:数据：

df4.head().to_dict()

{'DNAJA1': {'0h_T1_TimeC1_PIDC1_Survivor': 0.30466896848826,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.378391959798995,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.2466734902763562,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.20020120724346072,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.21559633027522937},
 'DNAJA1P5': {'0h_T1_TimeC1_PIDC1_Survivor': -0.1971955281717348,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': -0.19145728643216084,
  '0h_T1_TimeC2_PIDC2_Survivor': -0.2231320368474923,
  '0h_T1_TimeC4_PIDC3_Survivor': -0.22032193158953728,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': -0.21457696228338433},
 'DNAJA2': {'0h_T1_TimeC1_PIDC1_Survivor': 0.25586022467105374,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.22261306532663308,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.25588536335721596,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.21730382293762568,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.17380224260958205},
 'DNAJA3': {'0h_T1_TimeC1_PIDC1_Survivor': 0.12484533092566467,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.18040201005025125,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.12384851586489252,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.13380281690140847,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.16666666666666663},
 'DNAJA4': {'0h_T1_TimeC1_PIDC1_Survivor': 0.25447309843039345,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.45477386934673364,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.20573183213920163,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.25955734406438624,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.2706422018348623},
 'DNAJB1': {'0h_T1_TimeC1_PIDC1_Survivor': 0.36540720966934437,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.5793969849246231,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.3316274309109519,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.30281690140845063,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.41743119266055034},
 'DNAJB11': {'0h_T1_TimeC1_PIDC1_Survivor': 0.33305451443172285,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.31005025125628144,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.21596724667349024,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.23943661971830987,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.23802242609582047},
 'DNAJB12': {'0h_T1_TimeC1_PIDC1_Survivor': 0.1304748089195234,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.08492462311557787,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.12077789150460601,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.09054325955734402,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.10448521916411818},
 'DNAJB13': {'0h_T1_TimeC1_PIDC1_Survivor': -0.06551009305018526,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': -0.04773869346733674,
  '0h_T1_TimeC2_PIDC2_Survivor': -0.08904810644831117,
  '0h_T1_TimeC4_PIDC3_Survivor': -0.10462776659959759,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': -0.06472986748216113},
 'DNAJB14': {'0h_T1_TimeC1_PIDC1_Survivor': 0.31506844602697626,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.27487437185929653,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.2947799385875129,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.289738430583501,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.27268093781855235},
 'DNAJB2': {'0h_T1_TimeC1_PIDC1_Survivor': 0.20049372379622737,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.18643216080402006,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.12998976458546577,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.11569416498993959,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.15239551478083588},
 'DNAJB3': {'0h_T1_TimeC1_PIDC1_Survivor': -0.06546246185535144,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': -0.09396984924623121,
  '0h_T1_TimeC2_PIDC2_Survivor': -0.10133060388945755,
  '0h_T1_TimeC4_PIDC3_Survivor': -0.12273641851106647,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': -0.09531090723751279},
 'DNAJB4': {'0h_T1_TimeC1_PIDC1_Survivor': 0.06816950142840666,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.26381909547738686,
  '0h_T1_TimeC2_PIDC2_Survivor': -0.0071647901740019854,
  '0h_T1_TimeC4_PIDC3_Survivor': -0.014084507042253579,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.02599388379204892},
 'DNAJB5': {'0h_T1_TimeC1_PIDC1_Survivor': 0.08117546379552881,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.12311557788944721,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.08802456499488233,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.08752515090543261,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.12181447502548413},
 'DNAJB6 /// TMEM135': {'0h_T1_TimeC1_PIDC1_Survivor': 0.27126097766732055,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.31909547738693467,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.3070624360286592,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.3078470824949698,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.20948012232415897},
 'DNAJB7': {'0h_T1_TimeC1_PIDC1_Survivor': -0.13556605441176003,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': -0.13015075376884422,
  '0h_T1_TimeC2_PIDC2_Survivor': -0.17297850562947795,
  '0h_T1_TimeC4_PIDC3_Survivor': -0.19215291750503022,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': -0.14424057084607542},
 'DNAJB8': {'0h_T1_TimeC1_PIDC1_Survivor': -0.05724766845301285,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': -0.042713567839195964,
  '0h_T1_TimeC2_PIDC2_Survivor': -0.06243602865916064,
  '0h_T1_TimeC4_PIDC3_Survivor': -0.07042253521126762,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': -0.063710499490316},
 'DNAJB9': {'0h_T1_TimeC1_PIDC1_Survivor': 0.1658875438074684,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.17135678391959802,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.07062436028659165,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.03521126760563377,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.07186544342507646},
 'DNAJC1': {'0h_T1_TimeC1_PIDC1_Survivor': 0.2780145518592573,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.23567839195979898,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.24257932446264086,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.23943661971830987,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.24006116207951073},
 'DNAJC10': {'0h_T1_TimeC1_PIDC1_Survivor': 0.25124370342066066,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.28190954773869353,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.23029682702149437,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.25754527162977864,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.27166156982670736},
 'DNAJC11': {'0h_T1_TimeC1_PIDC1_Survivor': 0.0779460687857962,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.14221105527638192,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.06448311156601842,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.11569416498993959,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.1564729867482161},
 'DNAJC12': {'0h_T1_TimeC1_PIDC1_Survivor': -0.09891279151614327,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': -0.11507537688442217,
  '0h_T1_TimeC2_PIDC2_Survivor': -0.1361310133060389,
  '0h_T1_TimeC4_PIDC3_Survivor': -0.14185110663983905,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': -0.13812436289500513},
 'DNAJC13': {'0h_T1_TimeC1_PIDC1_Survivor': 0.3859045005128292,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.35829145728643225,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.42988741044012296,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.4295774647887325,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.35524974515800195},
 'DNAJC14': {'0h_T1_TimeC1_PIDC1_Survivor': 0.25440959017061504,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.23768844221105523,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.2128966223132037,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.1971830985915493,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.2543323139653414},
 'DNAJC15': {'0h_T1_TimeC1_PIDC1_Survivor': 0.3008182510036951,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.2597989949748744,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.24257932446264086,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.22837022132796786,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.2635066258919469},
 'DNAJC16': {'0h_T1_TimeC1_PIDC1_Survivor': 0.23949414612615494,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.25778894472361813,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.2589559877175025,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.24849094567404426,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.2737003058103976},
 'DNAJC17': {'0h_T1_TimeC1_PIDC1_Survivor': 0.05949056849418747,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.08793969849246232,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.060388945752302955,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.07947686116700202,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.0922528032619775},
 'DNAJC18': {'0h_T1_TimeC1_PIDC1_Survivor': 0.06714595997497777,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.0809045226130653,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.03787103377686798,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.026156941649899377,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.09836901121304789},
 'DNAJC19': {'0h_T1_TimeC1_PIDC1_Survivor': -0.056208249934639354,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': -0.028643216080402028,
  '0h_T1_TimeC2_PIDC2_Survivor': -0.08597748208802455,
  '0h_T1_TimeC4_PIDC3_Survivor': -0.07444668008048293,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': -0.05861365953109074},
 'DNAJC2': {'0h_T1_TimeC1_PIDC1_Survivor': 0.13301249313316946,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.1522613065326633,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.09825997952917093,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.1106639839034205,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.14424057084607542},
 'DNAJC21': {'0h_T1_TimeC1_PIDC1_Survivor': 0.19769142183350463,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.20954773869346735,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.1463664278403276,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.1891348088531187,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.19520897043832813},
 'DNAJC22': {'0h_T1_TimeC1_PIDC1_Survivor': -0.07788256052601769,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': -0.05879396984924623,
  '0h_T1_TimeC2_PIDC2_Survivor': -0.11463664278403277,
  '0h_T1_TimeC4_PIDC3_Survivor': -0.12173038229376258,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': -0.0922528032619776},
 'DNAJC24': {'0h_T1_TimeC1_PIDC1_Survivor': -0.04031160327660278,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': -0.028643216080402028,
  '0h_T1_TimeC2_PIDC2_Survivor': -0.029682702149437058,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.0010060362173038016,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': -0.027013251783894042},
 'DNAJC25 /// DNAJC25-GNG10 /// GNG10': {'0h_T1_TimeC1_PIDC1_Survivor': 0.2051155374016019,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.19145728643216084,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.1924257932446265,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.16700201207243454,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.17787971457696228},
 'DNAJC27': {'0h_T1_TimeC1_PIDC1_Survivor': 0.06399595028996814,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.04974874371859298,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.0051177072671443015,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.009054325955734393,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.08307849133537201},
 'DNAJC28': {'0h_T1_TimeC1_PIDC1_Survivor': -0.10060158199075109,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': -0.06482412060301512,
  '0h_T1_TimeC2_PIDC2_Survivor': -0.12691914022517914,
  '0h_T1_TimeC4_PIDC3_Survivor': -0.13179074446680086,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': -0.09021406727828753},
 'DNAJC3': {'0h_T1_TimeC1_PIDC1_Survivor': 0.2834111662339285,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.2648241206030152,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.3643807574206756,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.2977867203219316,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.19317023445463807},
 'DNAJC30': {'0h_T1_TimeC1_PIDC1_Survivor': 0.11612405915159318,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.12713567839195977,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.11873080859774823,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.09356136820925551,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.13608562691131498},
 'DNAJC4 /// NUDT22': {'0h_T1_TimeC1_PIDC1_Survivor': 0.24217472392430245,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.21959798994974872,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.2906857727737973,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.22032193158953728,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.2359836901121304},
 'DNAJC5': {'0h_T1_TimeC1_PIDC1_Survivor': 0.3032077492778582,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.242713567839196,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.3285568065506654,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.295774647887324,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.25535168195718644},
 'DNAJC5B': {'0h_T1_TimeC1_PIDC1_Survivor': -0.16639402217920127,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': -0.1482412060301508,
  '0h_T1_TimeC2_PIDC2_Survivor': -0.1668372569089048,
  '0h_T1_TimeC4_PIDC3_Survivor': -0.17907444668008052,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': -0.15545361875637106},
 'DNAJC5G': {'0h_T1_TimeC1_PIDC1_Survivor': -0.11752177010221651,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': -0.09095477386934676,
  '0h_T1_TimeC2_PIDC2_Survivor': -0.11873080859774823,
  '0h_T1_TimeC4_PIDC3_Survivor': -0.15895372233400404,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': -0.11671763506625896},
 'DNAJC6': {'0h_T1_TimeC1_PIDC1_Survivor': 0.044744479809136525,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.03165829145728639,
  '0h_T1_TimeC2_PIDC2_Survivor': -0.008188331627430918,
  '0h_T1_TimeC4_PIDC3_Survivor': -0.014084507042253579,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.008664627930682973},
 'DNAJC7': {'0h_T1_TimeC1_PIDC1_Survivor': 0.24253301635655233,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.2175879396984925,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.2128966223132037,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.19919517102615691,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.1819571865443425},
 'DNAJC8': {'0h_T1_TimeC1_PIDC1_Survivor': 0.14284727639535588,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.14623115577889448,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.11668372569089054,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.1277665995975855,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.11875637104994903},
 'DNAJC9': {'0h_T1_TimeC1_PIDC1_Survivor': 0.10419456178771519,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.15527638190954773,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.09518935516888441,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.09356136820925551,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.11162079510703361},
 'LOC646358': {'0h_T1_TimeC1_PIDC1_Survivor': 0.24757133829897363,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.21959798994974872,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.25588536335721596,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.22434607645875249,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.21865443425076447},
 'ST13': {'0h_T1_TimeC1_PIDC1_Survivor': 0.4051681434101184,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.45577889447236175,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.34288638689866935,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.3581488933601609,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.4001019367991844},
 'ST13P4': {'0h_T1_TimeC1_PIDC1_Survivor': 0.22716930984515626,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.2507537688442211,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.17604912998976466,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.1961770623742455,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.21457696228338433},
 'ST13P5': {'0h_T1_TimeC1_PIDC1_Survivor': 0.26162201153945086,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.28190954773869353,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.19856704196519956,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.21327967806841047,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.2420998980632008},
 'VEGFA': {'0h_T1_TimeC1_PIDC1_Survivor': 0.3142407217078641,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.2668341708542714,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.11668372569089054,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.09154929577464782,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.2319062181447503},
 'VEGFB': {'0h_T1_TimeC1_PIDC1_Survivor': 0.124370606683821,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.19547738693467331,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.14943705220061412,
  '0h_T1_TimeC4_PIDC3_Survivor': 0.1006036217303823,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.19011213047910297},
 'VEGFC': {'0h_T1_TimeC1_PIDC1_Survivor': 0.008064755138612082,
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 0.028643216080402028,
  '0h_T1_TimeC2_PIDC2_Survivor': 0.012282497441146378,
  '0h_T1_TimeC4_PIDC3_Survivor': -0.011066398390342085,
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 0.015800203873598304},
 'survival_info': {'0h_T1_TimeC1_PIDC1_Survivor': 'Survivor',
  '0h_T1_TimeC1_PIDC4_Non-Survivor': 'Non-Survivor',
  '0h_T1_TimeC2_PIDC2_Survivor': 'Survivor',
  '0h_T1_TimeC4_PIDC3_Survivor': 'Survivor',
  '4h_T2_TimeC1_PIDC4_Non-Survivor': 'Non-Survivor'},
 'time': {'0h_T1_TimeC1_PIDC1_Survivor': '0h',
  '0h_T1_TimeC1_PIDC4_Non-Survivor': '0h',
  '0h_T1_TimeC2_PIDC2_Survivor': '0h',
  '0h_T1_TimeC4_PIDC3_Survivor': '0h',
  '4h_T2_TimeC1_PIDC4_Non-Survivor': '4h'}}

I want to achieve something similar to this:我想实现类似的东西：

在此处输入图像描述

2 个解决方案

解决方案1
 0  2022-08-01 08:47:20

As per your code:根据您的代码：

  X = pd.DataFrame(df5.loc[i]).iloc[1:,]
  y = pd.DataFrame(df5.loc[i]).iloc[0,]

So X is n dimensional where as y is a scalar.所以X是n维，其中y是标量。

However in linear regression, given a set of features (called independent variables) per sample and a corresponding prediction (called dependent variable), you fit a model such that the models prediction of the dependent variable and the actual value is minimised for all the samples.然而，在线性回归中，给定每个样本的一组特征（称为自变量）和相应的预测（称为因变量），您拟合 model 以便模型对所有样本的因变量和实际值的预测最小化.

So you will need a set of samples, each sample represented by a set of features and a corresponding prediction for that sample.因此，您将需要一组样本，每个样本由一组特征和该样本的相应预测表示。

Or just simply X.shape[0] == y.shape[0]或者只是简单X.shape[0] == y.shape[0]

and X.shape == (n,m) and y.shape == (n,) where n: No:of Samples , m: No:of features per sample和X.shape == (n,m)和y.shape == (n,)其中n: No:of Samples , m: No:of features per sample

解决方案2
 0  2022-08-02 13:35:27

Your for loop is iterating over the wrong values.您for循环正在迭代错误的值。 The expression表达方式

for i in X_test.index:
    ...

iterates over the rows of X_test .遍历X_test的行。 However, it's the columns of X_test that should have the same length as y_pred .但是， X_test的列应该与y_pred具有相同的长度。

This may get you closer to what you want:这可能会让你更接近你想要的：

for col_name in X_test.columns:
  fig = px.scatter(X_test[col_name], y_pred.flatten())
  fig.update_layout(title=("Regression analysis of time-dependent treatment on " + i + 
                           " expression"), 
                    xaxis_title="Time",
                    yaxis_title="Gene Expression",
                    legend_title="Survival info")
  fig.show()

Depending on what you are using to plot your data, this may just create a bunch of overlapping plots, though, rather than a set of subplots.根据您对 plot 数据使用的内容，这可能只是创建一堆重叠图，而不是一组子图。

Judging from this question and your previous one , it looks like you don't have a very good grasp of Python or the various packages that you are using.从这个问题和你之前的问题来看，你似乎对 Python 或者你正在使用的各种包没有很好的掌握。 I strongly recommend that find some good textbooks and/or tutorials to help you get a sense of how they work.我强烈建议您找到一些好的教科书和/或教程来帮助您了解它们的工作原理。 Also, remember that while Stack Overflow may help you if you're stuck, it isn't a code-writing service.另外，请记住，虽然 Stack Overflow可能会在您遇到困难时为您提供帮助，但它不是代码编写服务。

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