biology-ecology

name biology-ecology description Experimental and ecological biology — experimental design with controls/replicates, biology-specific statistical tests, diversity indices, cell biology assays (IC50, ELISA, flow cytometry), imaging analysis, and survival analysis. Use when working with biological experimental data. allowed_agents ["data","experiment"] Biology and Ecology Overview This skill covers experimental biology and ecology workflows: from designing valid experiments with appropriate controls and replicates, to analyzing assay data, microscopy images, ecological surveys, and survival data. For genomics and sequencing data, also see the bioinformatics-analysis skill. When to Use This Skill Analyzing in vitro or in vivo experimental data (cell lines, animal studies) Processing ecology survey data (species counts, diversity indices) Fitting dose-response curves (IC50, EC50) Analyzing flow cytometry, ELISA, or imaging data Survival analysis (Kaplan-Meier, Cox regression) Choosing the right statistical test for biological data 1. Experimental Design for Biology Controls Control type Purpose Example Negative control Establish background / baseline Vehicle (DMSO, PBS), untreated cells Positive control Confirm assay works Known active compound, reference drug Vehicle control Separate drug effect from solvent effect DMSO at matched concentration Isotype control (flow) Estimate non-specific antibody binding Matched isotype antibody Rule : Always include both negative AND positive controls in every experiment run. Replicates Biological replicates : independent samples (different cell passages, different animals, different patients) → what you use for statistics Technical replicates : same sample measured multiple times → estimate measurement variability only, do NOT treat as independent samples Minimum n : Aim for n ≥ 3 biological replicates per condition. For animal studies, use power analysis (see experiment-design skill) targeting power ≥ 0.80. Randomization and Blinding import random # Randomize treatment assignment wells = list ( range ( 96 )) random.seed( 42 ) random.shuffle(wells) treatment_wells = wells[: 24 ] # randomly assigned control_wells = wells[ 24 : 48 ] # Document blinding: encode sample IDs before measurement sample_map = { f"Sample_ {i} " : f"Unknown_ {i:03d} " for i in range ( 1 , 25 )} # Decode only after all measurements are complete 2. Biology-Specific Statistical Tests import scipy.stats as stats import pingouin as pg import numpy as np import pandas as pd # Two groups, continuous, normal distribution → Welch's t-test (unequal variance assumed) control = np.array([ 2.1 , 2.4 , 2.2 , 2.5 , 2.3 ]) treatment = np.array([ 3.5 , 3.8 , 3.2 , 3.9 , 3.6 ]) t_stat, p_val = stats.ttest_ind(control, treatment, equal_var= False ) print ( f"Welch's t-test: t= {t_stat: .2 f} , p= {p_val: .4 f} " ) # Effect size (Cohen's d) result = pg.ttest(treatment, control) print (result[[ "T" , "p-val" , "cohen-d" , "power" ]]) # Non-parametric alternative: Mann-Whitney U u_stat, p_mw = stats.mannwhitneyu(control, treatment, alternative= "two-sided" ) print ( f"Mann-Whitney U: p= {p_mw: .4 f} " ) # Multiple groups → one-way ANOVA + Tukey HSD post-hoc groups = { "Control" : [ 2.1 , 2.4 , 2.2 ], "Drug_A" : [ 3.5 , 3.8 , 3.2 ], "Drug_B" : [ 4.1 , 4.3 , 4.0 ]} df_long = pd.DataFrame([{ "group" : g, "value" : v} for g, vals in groups.items() for v in vals]) aov = pg.anova(dv= "value" , between= "group" , data=df_long) posthoc = pg.pairwise_tukey(dv= "value" , between= "group" , data=df_long) print (aov) print (posthoc[[ "A" , "B" , "diff" , "p-tukey" ]]) # Non-parametric: Kruskal-Wallis + Dunn's test kw_stat, kw_p = stats.kruskal(*[v for v in groups.values()]) print ( f"Kruskal-Wallis: H= {kw_stat: .2 f} , p= {kw_p: .4 f} " ) dunn = pg.pairwise_tests(dv= "value" , between= "group" , data=df_long, parametric= False ) # Paired comparison: paired t-test before = np.array([ 5.2 , 4.8 , 5.5 , 4.9 ]) after = np.array([ 4.1 , 3.9 , 4.3 , 3.8 ]) t_paired, p_paired = stats.ttest_rel(before, after) print ( f"Paired t-test: t= {t_paired: .2 f} , p= {p_paired: .4 f} " ) Test selection quick guide : Data type 2 independent groups 2 paired ≥3 independent ≥3 paired Normal Welch's t-test Paired t-test ANOVA + Tukey Repeated ANOVA Non-normal / small n Mann-Whitney U Wilcoxon signed-rank Kruskal-Wallis + Dunn Friedman 3. Dose-Response Analysis (IC50 / EC50) import numpy as np from scipy.optimize import curve_fit import matplotlib.pyplot as plt def four_pl ( x, bottom, top, ec50, hill ): """Four-parameter logistic (4PL) dose-response curve.""" return bottom + (top - bottom) / ( 1 + (ec50 / x) ** hill) # Concentration in nM, response as % inhibition conc = np.array([ 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 ]) # nM response = np.array([ 2 , 5 , 15 , 50 , 85 , 95 , 98 ]) # % inhibition # Fit the curve try : popt, pcov = curve_fit( four_pl, conc, response, p0=[ 0 , 100 , 1.0 , 1.0 ], # initial guesses bounds=([ 0 , 50 , 0 , 0.1 ], [ 20 , 110 , 1e6 , 10 ]), # realistic bounds maxfev= 10000 , ) bottom, top, ic50, hill = popt perr = np.sqrt(np.diag(pcov)) print ( f"IC50 = {ic50: .3 f} nM (95% CI: {ic50- 2 *perr[ 2 ]: .3 f} – {ic50+ 2 *perr[ 2 ]: .3 f} )" ) print ( f"Hill slope = {hill: .2 f} " ) print ( f"Bottom = {bottom: .1 f} %, Top = {top: .1 f} %" ) except RuntimeError: print ( "Curve fitting failed — check data range covers full sigmoidal curve" ) # Plot x_fit = np.logspace(np.log10(conc. min ()), np.log10(conc. max ()), 200 ) plt.semilogx(conc, response, "o" , label= "Data" ) plt.semilogx(x_fit, four_pl(x_fit, *popt), "-" , label= f"4PL fit (IC50= {ic50: .2 f} nM)" ) plt.axvline(ic50, ls= "--" , color= "gray" , alpha= 0.5 ) plt.xlabel( "Concentration (nM)" ) plt.ylabel( "% Inhibition" ) plt.legend() 4. ELISA Quantification # Standard curve fitting and interpolation std_conc = np.array([ 0 , 0.5 , 1 , 2 , 5 , 10 , 20 ]) # ng/mL std_od = np.array([ 0.05 , 0.12 , 0.21 , 0.38 , 0.82 , 1.45 , 2.1 ]) # OD450 # Fit 4PL to standard curve popt_std, _ = curve_fit(four_pl, std_conc[ 1 :], std_od[ 1 :], p0=[ 0 , 2.5 , 5 , 1.5 ], maxfev= 10000 ) # Interpolate unknown samples unknown_od = np.array([ 0.45 , 0.78 , 1.12 ]) def interpolate_from_curve ( od_values, popt ): """Inverse 4PL: solve for concentration given OD.""" from scipy.optimize import brentq concentrations = [] for od in od_values: try : c = brentq( lambda x: four_pl(x, *popt) - od, 0.01 , 1000 ) concentrations.append(c) except ValueError: concentrations.append(np.nan) return np.array(concentrations) conc_unknown = interpolate_from_curve(unknown_od, popt_std) print ( f"Concentrations: {conc_unknown} ng/mL" ) 5. Ecology: Diversity Indices import numpy as np from scipy.stats import entropy as scipy_entropy # Community composition (species counts) community_A = np.array([ 50 , 30 , 10 , 5 , 3 , 2 ]) community_B = np.array([ 15 , 14 , 13 , 12 , 11 , 10 ]) def shannon_index ( counts ): props = counts / counts. sum () return scipy_entropy(props, base=np.e) def simpson_index ( counts ): n = counts. sum () return 1 - np. sum (counts * (counts - 1 )) / (n * (n - 1 )) def chao1 ( counts ): """Chao1 species richness estimator.""" f1 = (counts == 1 ). sum () # singletons f2 = (counts == 2 ). sum () # doubletons return len (counts) + (f1** 2 ) / ( 2 * max (f2, 1 )) print ( f"Community A — Shannon H': {shannon_index(community_A): .3 f} , Simpson D: {simpson_index(community_A): .3 f} " ) print ( f"Community B — Shannon H': {shannon_index(community_B): .3 f} , Simpson D: {simpson_index(community_B): .3 f} " ) # Beta diversity: Bray-Curtis dissimilarity def bray_curtis ( a, b ): return np. sum (np. abs (a - b)) / np. sum (a + b) bc = bray_curtis(community_A, community_B) print ( f"Bray-Curtis dissimilarity: {bc: .3 f} (0=identical, 1=completely different)" ) 6. Survival Analysis from lifelines import KaplanMeierFitter, CoxPHFitter from lifelines.statistics import logrank_test import pandas as pd # Kaplan-Meier survival curves df = pd.DataFrame({ "time" : [ 5 , 12 , 20 , 28 , 35 , 8 , 15 , 22 , 30 , 40 ], "event" : [ 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 ], # 1=event, 0=censored "group" : [ "ctrl" ]* 5 + [ "treat" ]* 5 , }) kmf = KaplanMeierFitter() fig, ax = plt.subplots(figsize=( 7 , 5 )) for group in [ "ctrl" , "treat" ]: mask = df[ "group" ] == group kmf.fit(df[mask][ "time" ], df[mask][ "event" ], label=group) kmf.plot_survival_function(ax=ax, ci_show= True ) # Log-rank test between groups ctrl = df[df.group == "ctrl" ] treat = df[df.group == "treat" ] lr = logrank_test(ctrl[ "time" ], treat[ "time" ], ctrl[ "event" ], treat[ "event" ]) print ( f"Log-rank p-value: {lr.p_value: .4 f} " ) # Cox proportional hazards df[ "treated" ] = (df[ "group" ] == "treat" ).astype( int ) cph = CoxPHFitter() cph.fit(df[[ "time" , "event" , "treated" ]], duration_col= "time" , event_col= "event" ) cph.print_summary() # HR < 1 means treatment reduces hazard (protective) # HR > 1 means treatment increases hazard Assumptions to check : Proportional hazards assumption : use Schoenfeld residuals ( cph.check_assumptions(df) ) Censoring : must be non-informative (censored patients don't systematically differ from non-censored)