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- ---
- title: "Spacek et al., 2021, Figure 1-Supplement 2"
- output: pdf_document
- ---
- ```{r setup, include=FALSE}
- knitr::opts_chunk$set(echo = TRUE)
- library(arm)
- library(lmerTest)
- library(tidyverse)
- source('get_data.R')
- ```
- ```{r read_data, include=FALSE}
- tib = get_data("../csv/fig1.csv")
- ```
- ```{r tidy_for_1_S2ab, include = FALSE}
- # Turn booleans for 'optogenetic manipulation' into a binary predictor
- tb <- tib %>% mutate(feedback = ifelse(opto == TRUE, 0, 1))
- # Signal-to-noise ratio and mean peak-width are not computed on a trial-by-trial basis. In the csv-file,
- # these measures are therefore identical across trials, so we simply pull out the first trial of each neuron
- tb = tb %>% select(mid, sid, eid, uid, mseu, feedback, snr, meanpkw) %>% distinct(mseu, feedback, .keep_all = TRUE)
- ```
- # Figure 1-Supplement 2a
- ## Average effect of feedback on signal-to-noise ratio (SNR)
- ```{r, fit_model_1_S2a}
- # We fit a random-intercept model with two random effects:
- # (1) Neurons (uid) can have different baseline firing rates
- # (2) Mean firing rates are allowed to differ across recording sessions (sid)
- # More complex models with random slopes for neurons (or with experiments nested in
- # sessions, nested in mice) give singular fits.
- lmer.1_S2a = lmer(snr ~ feedback + (1 | uid) + (1 | sid),
- data = tb %>% drop_na(snr))
- display(lmer.1_S2a)
- anova(lmer.1_S2a)
- ```
- ```{r get_predicted_average_effect1_S2a, include=F}
- mSuppr = fixef(lmer.1_S2a)[1]
- diffMeans = fixef(lmer.1_S2a)[2]
- mActive = fixef(lmer.1_S2a)[1] + diffMeans
- ```
- Feedback SNR: `r format(mActive, digits=2, nsmall=2)` \newline
- Suppression SNR: `r format(mSuppr, digits=2, nsmall=2)` \newline
- n = `r nrow(tb %>% drop_na(snr) %>% count(uid))` neurons from `r nrow(tb %>% drop_na(snr) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 2b
- ## Average effect of feedback on PSTH mean peak width
- ```{r, fit_model_1_S2b}
- # Random-intercept for single neurons,
- # random intercept for experiments, nested in series
- lmer.1_S2b = lmer(meanpkw ~ feedback + (1 | uid) + (1 | sid/eid),
- data = tb %>% drop_na(meanpkw))
- display(lmer.1_S2b)
- anova(lmer.1_S2b)
- ```
- ```{r get_predicted_average_effect_1_S2b, include=F}
- mSuppr = fixef(lmer.1_S2b)[1]
- diffMeans = fixef(lmer.1_S2b)[2]
- mActive = fixef(lmer.1_S2b)[1] + diffMeans
- ```
- Feedback mean peak width: `r format(mActive, digits=2, nsmall=2)` \newline
- Suppression mean peak width: `r format(mSuppr, digits=2, nsmall=2)` \newline
- n = `r nrow(tb %>% drop_na(meanpkw) %>% count(uid))` neurons from `r nrow(tb %>% drop_na(meanpkw) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 2c
- ## Relation between firing rate FMI and burst ratio FMI
- ```{r read_data_1_S2c-g, include=FALSE}
- tib = get_data("../csv/mviFMI.csv")
- # filter based on 'state'
- tb <- tib %>% filter(st8 == 'none')
- ```
- ```{r fit_model_1_S2c}
- # Random-intercept for single neurons,
- # random intercept for experiments, nested in series
- lmer.1_S2c = lmer(meanburstratio ~ meanrate + (1 | uid) + (1 | sid/eid),
- data = tb %>% drop_na(meanburstratio, meanrate))
- display(lmer.1_S2c)
- anova(lmer.1_S2c)
- ```
- ```{r store_coefficients_1_S2c, include=FALSE}
- coef_df = data.frame("intercept" = fixef(lmer.1_S2c)[1], "slope" = fixef(lmer.1_S2c)[2], row.names = "")
- write_csv(coef_df, "_stats/figure_1_S2c_coefs.csv")
- ```
- Slope of `r format(fixef(lmer.1_S2c)[2], digits=2, nsmall=2)` $\pm$ `r format(2 * se.fixef(lmer.1_S2c)[2], digits=2, nsmall=2)` (95\%-confidence interval) \newline
- n = `r nrow(tb %>% drop_na(meanburstratio, meanrate) %>% count(uid))` neurons from `r nrow(tb %>% drop_na(meanburstratio, meanrate) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 2d
- ## Relation between firing rate FMI and sparseness FMI
- ```{r fit_model_1_S2d}
- # Random-intercept for single neurons,
- # random intercept for experiments, nested in series
- lmer.1_S2d = lmer(spars ~ meanrate + (1 | uid) + (1 | sid/eid),
- data = tb %>% drop_na(spars, meanrate))
- display(lmer.1_S2d)
- anova(lmer.1_S2d)
- ```
- ```{r store_coefficients_1_S2d, include=FALSE}
- coef_df = data.frame("intercept" = fixef(lmer.1_S2d)[1], "slope" = fixef(lmer.1_S2d)[2], row.names = "")
- write_csv(coef_df, "_stats/figure_1_S2d_coefs.csv")
- ```
- Slope of `r format(fixef(lmer.1_S2d)[2], digits=2, nsmall=2)` $\pm$ `r format(2 * se.fixef(lmer.1_S2d)[2], digits=2, nsmall=2)` (95\%-confidence interval) \newline
- n = `r nrow(tb %>% drop_na(spars, meanrate) %>% count(uid))` neurons from `r nrow(tb %>% drop_na(spars, meanrate) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 2e
- ## Relation between firing rate FMI and reliability FMI
- ```{r fit_model_1_S2e}
- # Random-intercept for single neurons,
- # random intercept for series, nested in mice
- lmer.1_S2e = lmer(rel ~ meanrate + (1 | uid) + (1 | mid/sid),
- data = tb %>% drop_na(rel, meanrate))
- display(lmer.1_S2e)
- anova(lmer.1_S2e)
- ```
- ```{r store_coefficients_1_S2e, include=FALSE}
- coef_df = data.frame("intercept" = fixef(lmer.1_S2e)[1], "slope" = fixef(lmer.1_S2e)[2], row.names = "")
- write_csv(coef_df, "_stats/figure_1_S2e_coefs.csv")
- ```
- Slope of `r format(fixef(lmer.1_S2e)[2], digits=2, nsmall=2)` $\pm$ `r format(2 * se.fixef(lmer.1_S2e)[2], digits=2, nsmall=2)` (95\%-confidence interval) \newline
- n = `r nrow(tb %>% drop_na(rel, meanrate) %>% count(uid))` neurons from `r nrow(tb %>% drop_na(rel, meanrate) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 2f
- ## Relation between firing rate FMI and SNR FMI
- ```{r fit_model_1_S2f}
- # Random intercept for neurons,
- # random intercept for series
- lmer.1_S2f = lmer(snr ~ meanrate + (1 | uid) + (1 | sid),
- data = tb %>% drop_na(snr, meanrate))
- display(lmer.1_S2f)
- anova(lmer.1_S2f)
- ```
- ```{r store_coefficients_1_S2f, include=FALSE}
- coef_df = data.frame("intercept" = fixef(lmer.1_S2f)[1], "slope" = fixef(lmer.1_S2f)[2], row.names = "")
- write_csv(coef_df, "_stats/figure_1_S2f_coefs.csv")
- ```
- Slope of `r format(fixef(lmer.1_S2f)[2], digits=2, nsmall=2)` $\pm$ `r format(2 * se.fixef(lmer.1_S2f)[2], digits=2, nsmall=2)` (95\%-confidence interval) \newline
- n = `r nrow(tb %>% drop_na(snr, meanrate) %>% count(uid))` neurons from `r nrow(tb %>% drop_na(snr, meanrate) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 2g
- ## Relation between firing rate FMI and mean peak witdth FMI
- ```{r fit_model_1_S2g}
- # Random intercept for neurons,
- # random intercept for experiments, nested in series
- lmer.1_S2g = lmer(meanpkw ~ meanrate + (1 | uid) + (1 | sid/eid),
- data = tb %>% drop_na(meanpkw, meanrate))
- display(lmer.1_S2g)
- anova(lmer.1_S2g)
- ```
- ```{r store_coefficients_1_S2g, include=FALSE}
- coef_df = data.frame("intercept" = fixef(lmer.1_S2g)[1], "slope" = fixef(lmer.1_S2g)[2], row.names = "")
- write_csv(coef_df, "_stats/figure_1_S2g_coefs.csv")
- ```
- Slope of `r format(fixef(lmer.1_S2g)[2], digits=2, nsmall=2)` $\pm$ `r format(2 * se.fixef(lmer.1_S2g)[2], digits=2, nsmall=2)` (95\%-confidence interval) \newline
- n = `r nrow(tb %>% drop_na(meanpkw, meanrate) %>% count(uid))` neurons from `r nrow(tb %>% drop_na(meanpkw, meanrate) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 2h
- ## Effect of feedback on eye position variability
- ```{r read_data_1_S2h, include=FALSE}
- tib = get_data("../csv/ipos_opto.csv")
- # Turn feedback into a binary variable
- tb = tib %>% mutate(feedback = ifelse(opto == "FALSE", 1, 0))
- # The standard deviation of eye position is not computed on a trial-by-trial basis. In the csv-file,
- # std is therefore identical across trials, so we simply pull out the first trial of each neuron
- tb = tb %>% select(mid, sid, eid, mse, feedback, std_xpos_cross) %>% distinct(mse, feedback, .keep_all = TRUE)
- ```
- ```{r fit_model_1_S2h}
- # Random intercept for experiments, nested in series, nested in mice
- lmer.8 = lmer(std_xpos_cross ~ feedback + (1 | mid/sid/eid),
- data = tb %>% drop_na(std_xpos_cross))
- display(lmer.8)
- anova(lmer.8)
- ```
- ```{r get_predicted_average_effect_1_S2h, include=F}
- mSuppr = fixef(lmer.8)[1]
- diffMeans = fixef(lmer.8)[2]
- mFeedback = fixef(lmer.8)[1] + diffMeans
- ```
- Mean eye position standard deviation with feedback: `r format(mFeedback, digits=2, nsmall=2)`$^{\circ}$ \newline
- Mean eye position standard deviation with suppression: `r format(mSuppr, digits=2, nsmall=2)`$^{\circ}$ \newline
- n = `r nrow(tb %>% drop_na(std_xpos_cross) %>% count(eid))` experiments from `r nrow(tb %>% drop_na(std_xpos_cross) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 2i
- ## Relation between feedback effects on eye position and feedback effects on reliability
- ```{r read_data_1_S2i, include=FALSE}
- tib = get_data("../csv/iposmi.csv")
- ```
- ```{r fit_model_1S2i}
- # Random intercept for neurons,
- # random intercept for experiments nested in series
- lmer.1_S2i = lmer(relfmi ~ iposfmi + (1 | uid) + (1 | sid/eid),
- data = tib %>% drop_na(relfmi, iposfmi))
- display(lmer.1_S2i)
- anova(lmer.1_S2i)
- ```
- ```{r store_coefficients, include=FALSE}
- coef_df = data.frame("intercept" = fixef(lmer.1_S2i)[1], "slope" = fixef(lmer.1_S2i)[2], row.names = "")
- write_csv(coef_df, "_stats/figure_1_S2i_coefs.csv")
- ```
- Slope of `r format(fixef(lmer.1_S2i)[2], digits=2, nsmall=2)` $\pm$ `r format(2 * se.fixef(lmer.1_S2i)[2], digits=2, nsmall=2)` (95\%-confidence interval) \newline
- n = `r nrow(tib %>% drop_na(relfmi, iposfmi) %>% count(uid))` neurons from `r nrow(tib %>% drop_na(relfmi, iposfmi) %>% count(mid))` mice
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