123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277 |
- ---
- title: "Spacek et al., 2021, Figure 1-Supplement 3"
- output: pdf_document
- ---
- ```{r setup, include=FALSE}
- knitr::opts_chunk$set(echo = TRUE)
- library(arm)
- library(lmerTest)
- library(tidyverse)
- source('get_data.R')
- ```
- ```{r read_data, include=FALSE}
- tib = get_data("../csv/fig1S33S1.csv")
- ```
- # Figure 1-Supplement 3a
- ## Firing rate FMIs separated by cell types
- ```{r tidy_for_1_S3, include = FALSE}
- # Remove observations with NAs
- tb <- tib %>% filter(sbc != "Na")
- # Recode suppressed-by-contrast boolean into numeric, binary predictor
- tb$sbc = ifelse(tb$sbc == TRUE, 1, 0)
- ```
- ```{r fit_model_1_S3a}
- # Random intercept for series
- lmer.1_S3a = lmer(mvi_meanrate ~ sbc + (1 | sid),
- data = tb %>% drop_na(mvi_meanrate))
- display(lmer.1_S3a)
- anova(lmer.1_S3a)
- ```
- ```{r get_predicted_average_change_1_S3a, include=F}
- m_sbc0 = fixef(lmer.1_S3a)[1]
- diffMeans = fixef(lmer.1_S3a)[2]
- m_sbc1 = fixef(lmer.1_S3a)[1] + diffMeans
- ```
- ```{r store_coefficients_1_S3a, include=FALSE}
- pred_means_df = data.frame("non_sbc" = m_sbc0, "sbc" = m_sbc1, row.names = "")
- write_csv(pred_means_df, "_stats/figure_1_S3a_pred_means.csv")
- ```
- FMI sbc: `r format(m_sbc1, digits=3, nsmall=3)` \newline
- FMI non-sbc: `r format(m_sbc0, digits=3, nsmall=3)` \newline
- n = `r nrow(tb %>% drop_na(mvi_meanrate) %>% count(uid))` neurons from `r nrow(tb %>% drop_na(mvi_meanrate) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 3b
- ## Relation between firing rate FMI and recording depth
- ```{r fit_model_1_S3b}
- # Random intercept for series
- lmer.1_S3b = lmer(mvi_meanrate ~ depth + (1 | sid),
- data = tib %>% drop_na(mvi_meanrate, depth))
- display(lmer.1_S3b)
- anova(lmer.1_S3b)
- ```
- ```{r store_coefficients, include=FALSE}
- coef_df = data.frame("intercept" = fixef(lmer.1_S3b)[1], "slope" = fixef(lmer.1_S3b)[2], row.names = "")
- write_csv(coef_df, "_stats/figure_1_S3b_coefs.csv")
- ```
- Slope of `r format(fixef(lmer.1_S3b)[2], digits=2, nsmall=2)` $\pm$ `r format(2 * se.fixef(lmer.1_S3b)[2], digits=2, nsmall=2)` (95\%-confidence interval) \newline
- n = `r nrow(tib %>% drop_na(mvi_meanrate, depth) %>% count(uid))` neurons from `r nrow(tib %>% drop_na(mvi_meanrate, depth) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 3c
- ## Relation between firing rate FMI and direction selectivity
- ``` {r, fit_model_1_S3c}
- # Random intercept for series
- lmer.1_S3c = lmer(mvi_meanrate ~ dsi + (1 | sid),
- data = tib %>% drop_na(mvi_meanrate, dsi))
- display(lmer.1_S3c)
- anova(lmer.1_S3c)
- ```
- ```{r store_coefficients_1_S3c, include=FALSE}
- coef_df = data.frame("intercept" = fixef(lmer.1_S3c)[1], "slope" = fixef(lmer.1_S3c)[2], row.names = "")
- write_csv(coef_df, "_stats/figure_1_S3c_coefs.csv")
- ```
- Slope of `r format(fixef(lmer.1_S3c)[2], digits=2, nsmall=2)` $\pm$ `r format(2 * se.fixef(lmer.1_S3c)[2], digits=2, nsmall=2)` (95\%-confidence interval) \newline
- n = `r nrow(tib %>% drop_na(mvi_meanrate, dsi) %>% count(uid))` neurons from `r nrow(tib %>% drop_na(mvi_meanrate, dsi) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 3d
- ## Relation between firing rate FMI and receptive field location
- ``` {r, fit_model_1_S3d}
- # Random intercept for series nested within mice
- lmer.1_S3d = lmer(mvi_meanrate ~ rfdist + (1 | mid/sid),
- data = tib %>% drop_na(mvi_meanrate, rfdist))
- display(lmer.1_S3d)
- anova(lmer.1_S3d)
- ```
- ```{r store_coefficients_1_S3d, include=FALSE}
- # store results in file
- coef_df = data.frame("intercept" = fixef(lmer.1_S3d)[1], "slope" = fixef(lmer.1_S3d)[2], row.names = "")
- write_csv(coef_df, "_stats/figure_1_S3d_coefs.csv")
- ```
- Slope of `r format(fixef(lmer.1_S3d)[2], digits=2, nsmall=2)` $\pm$ `r format(2 * se.fixef(lmer.1_S3d)[2], digits=2, nsmall=2)` (95\%-confidence interval) \newline
- n = `r nrow(tib %>% drop_na(mvi_meanrate, rfdist) %>% count(uid))` neurons from `r nrow(tib %>% drop_na(mvi_meanrate, rfdist) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 3e
- ## Relation between firing rate FMI and firing rate
- ``` {r, fit_model_1_S3e}
- # Random intercept for series nested within mice
- lmer.1_S3e = lmer(mvi_meanrate ~ mvi_meanrate_raw + (1 | mid/sid),
- data = tib %>% drop_na(mvi_meanrate, mvi_meanrate_raw))
- display(lmer.1_S3e)
- anova(lmer.1_S3e)
- ```
- ```{r store_coefficients_1_S3e, include=FALSE}
- coef_df = data.frame("intercept" = fixef(lmer.1_S3e)[1], "slope" = fixef(lmer.1_S3e)[2], row.names = "")
- write_csv(coef_df, "_stats/figure_1_S3e_coefs.csv")
- ```
- Slope of `r format(fixef(lmer.1_S3e)[2], digits=2, nsmall=2)` $\pm$ `r format(2 * se.fixef(lmer.1_S3e)[2], digits=2, nsmall=2)` (95\%-confidence interval) \newline
- n = `r nrow(tib %>% drop_na(mvi_meanrate, mvi_meanrate_raw) %>% count(uid))` neurons from `r nrow(tib %>% drop_na(mvi_meanrate, mvi_meanrate_raw) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 3f
- ## Burst ratio FMIs separated by cell types
- ```{r fit_model_1_S3f}
- # Random intercept for series
- lmer.1_S3f = lmer(mvi_meanburstratio ~ sbc + (1 | sid),
- data = tb %>% drop_na(mvi_meanburstratio))
- display(lmer.1_S3f)
- anova(lmer.1_S3f)
- ```
- ```{r get_predicted_average_effect_1_S3f, include=F}
- m_sbc0 = fixef(lmer.1_S3f)[1]
- diffMeans = fixef(lmer.1_S3f)[2]
- m_sbc1 = fixef(lmer.1_S3f)[1] + diffMeans
- ```
- ```{r store_coefficients_1_S3f, include=FALSE}
- pred_means_df = data.frame("non_sbc" = m_sbc0, "sbc" = m_sbc1, row.names = "")
- write_csv(pred_means_df, "_stats/figure_1_S3f_pred_means.csv")
- ```
- FMI sbc: `r format(m_sbc1, digits=3, nsmall=3)` \newline
- FMI non-sbc `r format(m_sbc0, digits=3, nsmall=3)` \newline
- n = `r nrow(tb %>% drop_na(mvi_meanburstratio) %>% count(uid))` neurons from `r nrow(tb %>% drop_na(mvi_meanburstratio) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 3g
- ## Relation between burst ratio FMI and recording depth
- ```{r fit_model_1_S3g}
- # Random intercept for series nested within mice
- lmer.1_S3g = lmer(mvi_meanburstratio ~ depth + (1 | mid/sid),
- data = tib %>% drop_na(mvi_meanburstratio, depth))
- display(lmer.1_S3g)
- anova(lmer.1_S3g)
- ```
- ```{r store_coefficients_1_S3g, include=FALSE}
- coef_df = data.frame("intercept" = fixef(lmer.1_S3g)[1], "slope" = fixef(lmer.1_S3g)[2], row.names = "")
- write_csv(coef_df, "_stats/figure_1_S3g_coefs.csv")
- ```
- Slope of `r format(fixef(lmer.1_S3g)[2], digits=2, nsmall=2)` $\pm$ `r format(2 * se.fixef(lmer.1_S3g)[2], digits=2, nsmall=2)` (95\%-confidence interval) \newline
- n = `r nrow(tib %>% drop_na(mvi_meanburstratio, depth) %>% count(uid))` neurons from `r nrow(tib %>% drop_na(mvi_meanburstratio, depth) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 3h
- ## Relation between burst ratio FMI and direction selectivity
- ``` {r, fit_model_1_S3h}
- # Random intercept for series nested within mice
- lmer.1_S3h = lmer(mvi_meanburstratio ~ dsi + (1 | mid/sid),
- data = tib %>% drop_na(mvi_meanburstratio, dsi))
- display(lmer.1_S3h)
- anova(lmer.1_S3h)
- ```
- ```{r store_coefficients_1_S3h, include=FALSE}
- coef_df = data.frame("intercept" = fixef(lmer.1_S3h)[1], "slope" = fixef(lmer.1_S3h)[2], row.names = "")
- write_csv(coef_df, "_stats/figure_1_S3h_coefs.csv")
- ```
- Slope of `r format(fixef(lmer.1_S3h)[2], digits=2, nsmall=2)` $\pm$ `r format(2 * se.fixef(lmer.1_S3h)[2], digits=2, nsmall=2)` (95\%-confidence interval) \newline
- n = `r nrow(tib %>% drop_na(mvi_meanburstratio, dsi) %>% count(uid))` neurons from `r nrow(tib %>% drop_na(mvi_meanburstratio, dsi) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 3i
- ## Relation between burst ratio FMI and receptive field location
- ``` {r, fit_model_1_S3i}
- # Random intercept for series nested within mice
- lmer.1_S3i = lmer(mvi_meanburstratio ~ rfdist + (1 | mid/sid),
- data = tib %>% drop_na(mvi_meanburstratio, rfdist))
- display(lmer.1_S3i)
- anova(lmer.1_S3i)
- ```
- ```{r store_coefficients_1_S3i, include=FALSE}
- coef_df = data.frame("intercept" = fixef(lmer.1_S3i)[1], "slope" = fixef(lmer.1_S3i)[2], row.names = "")
- write_csv(coef_df, "_stats/figure_1_S3i_coefs.csv")
- ```
- Slope of `r format(fixef(lmer.1_S3i)[2], digits=2, nsmall=2)` $\pm$ `r format(2 * se.fixef(lmer.1_S3i)[2], digits=2, nsmall=2)` (95\%-confidence interval) \newline
- n = `r nrow(tib %>% drop_na(mvi_meanburstratio, rfdist) %>% count(uid))` neurons from `r nrow(tib %>% drop_na(mvi_meanburstratio, rfdist) %>% count(mid))` mice
- \newpage
- # Figure 1-Supplement 3j
- ## Relation between burst ratio FMI and burst ratio
- ``` {r, fit_model_1_S3j}
- # Random intercept for series
- lmer.1_S3j = lmer(mvi_meanburstratio ~ mvi_meanburstratio_raw + (1 | sid),
- data = tib %>% drop_na(mvi_meanburstratio, mvi_meanburstratio_raw))
- display(lmer.1_S3j)
- anova(lmer.1_S3j)
- ```
- ```{r store_coefficients_1_S3j, include=FALSE}
- coef_df = data.frame("intercept" = fixef(lmer.1_S3j)[1], "slope" = fixef(lmer.1_S3j)[2], row.names = "")
- write_csv(coef_df, "_stats/figure_1_S3j_coefs.csv")
- ```
- Slope of `r format(fixef(lmer.1_S3j)[2], digits=2, nsmall=2)` $\pm$ `r format(2 * se.fixef(lmer.1_S3j)[2], digits=2, nsmall=2)` (95\%-confidence interval) \newline
- n = `r nrow(tib %>% drop_na(mvi_meanburstratio, mvi_meanburstratio_raw) %>% count(uid))` neurons from `r nrow(tib %>% drop_na(mvi_meanburstratio, mvi_meanburstratio_raw) %>% count(mid))` mice
|